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fake-truncate js-profile-about" style="margin: 0px;">PhD(Mathematics)<br /><b>Address: </b>Department of Mathematics, Yildiz Technical University, Faculty of Arts and Science, Esenler, Istanbul, 34210, Turkey<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://hu-pk.academia.edu/MGulistan"><img class="profile-avatar u-positionAbsolute" alt="Muhammad Gulistan" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" 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class="profile--tab_heading_container">Papers by Nasreen Kausar</h3></div><div class="js-work-strip profile--work_container" data-work-id="109368812"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109368812/A_Novel_Method_for_Determining_Tourism_Carrying_Capacity_in_a_Decision_Making_Context_Using_q_Rung_Orthopair_Fuzzy_Hypersoft_Environment"><img alt="Research paper thumbnail of A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment" class="work-thumbnail" src="https://attachments.academia-assets.com/107515302/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109368812/A_Novel_Method_for_Determining_Tourism_Carrying_Capacity_in_a_Decision_Making_Context_Using_q_Rung_Orthopair_Fuzzy_Hypersoft_Environment">A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment</a></div><div class="wp-workCard_item"><span>Computer Modeling in Engineering and Sciences</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tourism is a popular activity that allows individuals to escape their daily routines and explore ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (q−ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. With the advanced mathematical tools in this field, the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to support multiattribute decision-making algorithms. By implementing this technique, effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism. A case study of selected tourism carrying capacity will demonstrate the proposed methodology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="90948602bbb14cf46b73e05df0e77beb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515302,"asset_id":109368812,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515302/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368812"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368812"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368812; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368812]").text(description); $(".js-view-count[data-work-id=109368812]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368812; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368812']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "90948602bbb14cf46b73e05df0e77beb" } } $('.js-work-strip[data-work-id=109368812]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368812,"title":"A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment","translated_title":"","metadata":{"doi":"10.32604/cmes.2023.030896","issue":"2","volume":"138","abstract":"Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (q−ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. With the advanced mathematical tools in this field, the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to support multiattribute decision-making algorithms. By implementing this technique, effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism. A case study of selected tourism carrying capacity will demonstrate the proposed methodology.","ai_title_tag":"Assessing Tourism Carrying Capacity Using Fuzzy Hypersoft Sets","page_numbers":"1951-1979","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Computer Modeling in Engineering and Sciences"},"translated_abstract":"Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (q−ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. With the advanced mathematical tools in this field, the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to support multiattribute decision-making algorithms. By implementing this technique, effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism. A case study of selected tourism carrying capacity will demonstrate the proposed methodology.","internal_url":"https://www.academia.edu/109368812/A_Novel_Method_for_Determining_Tourism_Carrying_Capacity_in_a_Decision_Making_Context_Using_q_Rung_Orthopair_Fuzzy_Hypersoft_Environment","translated_internal_url":"","created_at":"2023-11-18T22:44:40.071-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107515302,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515302/thumbnails/1.jpg","file_name":"1.pdf","download_url":"https://www.academia.edu/attachments/107515302/download_file","bulk_download_file_name":"A_Novel_Method_for_Determining_Tourism_C.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515302/1-libre.pdf?1700377364=\u0026response-content-disposition=attachment%3B+filename%3DA_Novel_Method_for_Determining_Tourism_C.pdf\u0026Expires=1738795241\u0026Signature=UoYtcxlnbU1MLKKTmmgELrb4I5tv1r3sHsKMLciQ5I5zXOCY0nkUmmlaniNAe93BkVuXJpXIr2YaXVKaL1CXaoDILsuCkCN4hwvi3ZDBlVtDOtmu9lyRizzcdiK3JIUc1dJ6-iXADIB8WNom1Ir2n~A3oRXbatS-JVqffbuNFVz8FcC78WHQcYGdSMnwmBihabUJL3Yog3fmLgtKD~nLAIbp~HVgps2NhSl-UUQSIwEtnfbufNEXdzF4RNO8NqLqt2ZBI2Pyb~lI2J-Ez9CqlaI66UGqsdhqsKezEqypMuwm5eWD3THiKL1UKIB-f6EYERj0FW9UFa~rRJZl5NB09A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Novel_Method_for_Determining_Tourism_Carrying_Capacity_in_a_Decision_Making_Context_Using_q_Rung_Orthopair_Fuzzy_Hypersoft_Environment","translated_slug":"","page_count":29,"language":"en","content_type":"Work","summary":"Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (q−ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. With the advanced mathematical tools in this field, the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to support multiattribute decision-making algorithms. By implementing this technique, effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism. 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Singh</a></span></div><div class="wp-workCard_item"><span>Computational Journal of Mathematical and Statistical Sciences</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Three parameters Birnbaum-Saunders, BS (3P), distribution is an important statistical model which...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Three parameters Birnbaum-Saunders, BS (3P), distribution is an important statistical model which is useable in the fields of both pure and applied sciences. Since its inception, Birnbaum-Saunders (BS) distribution has received a considerable attention in view of its wide applications in many areas of research in applied sciences, such as engineering science, earth science, environmental science, medical science, material fatigue and reliability studies. The objective of this paper is a statistical analysis of the three parameter Birnbaum-Saunders, BS (3P), distribution and to draw some inferences on it. Several new distributional properties of this distribution have been discussed. Based on these distributional properties, we have established several new characterization results of the three-parameter Birnbaum-Saunders distribution. Finally, applications to some real-life data sets are analyzed to show the usefulness of this distribution. The results of this article will be useful for the researchers, scientists, and statisticians in fields of theoretical and applied sciences.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f98fe42a63b712e90021fc0681e91d16" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515220,"asset_id":109368689,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515220/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368689"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368689"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368689; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368689]").text(description); $(".js-view-count[data-work-id=109368689]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368689; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368689']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f98fe42a63b712e90021fc0681e91d16" } } $('.js-work-strip[data-work-id=109368689]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368689,"title":"Some Inferences on Three Parameters Birnbaum-Saunders Distribution: Statistical Properties, Characterizations and Applications","translated_title":"","metadata":{"doi":"10.21608/CJMSS.2023.224583.1011","issue":"2","volume":"2","abstract":"Three parameters Birnbaum-Saunders, BS (3P), distribution is an important statistical model which is useable in the fields of both pure and applied sciences. 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The objective of this paper is a statistical analysis of the three parameter Birnbaum-Saunders, BS (3P), distribution and to draw some inferences on it. Several new distributional properties of this distribution have been discussed. Based on these distributional properties, we have established several new characterization results of the three-parameter Birnbaum-Saunders distribution. Finally, applications to some real-life data sets are analyzed to show the usefulness of this distribution. 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Since its inception, Birnbaum-Saunders (BS) distribution has received a considerable attention in view of its wide applications in many areas of research in applied sciences, such as engineering science, earth science, environmental science, medical science, material fatigue and reliability studies. The objective of this paper is a statistical analysis of the three parameter Birnbaum-Saunders, BS (3P), distribution and to draw some inferences on it. Several new distributional properties of this distribution have been discussed. Based on these distributional properties, we have established several new characterization results of the three-parameter Birnbaum-Saunders distribution. Finally, applications to some real-life data sets are analyzed to show the usefulness of this distribution. 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This problem has been approached in a number of ways, including use of the sequential Newton's method and the traditional Weierstrass simultaneous iterative scheme. To approximate all of the roots of a given nonlinear equation, sequential iterative algorithms must use a deflation strategy because rounding errors can produce inaccurate results. This study aims to develop an efficient numerical simultaneous scheme for approximating all nonlinear equations' roots of convergence order 12. The numerical outcomes of the considered engineering problems show that, in terms of accuracy, validations, error, computational CPU time, and residual error, recently developed simultaneous methods perform better than existing methods in the literature.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5e52ccb568db4959a965ef1537379288" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515193,"asset_id":109368633,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515193/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368633"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368633"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368633; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368633]").text(description); $(".js-view-count[data-work-id=109368633]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368633; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368633']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5e52ccb568db4959a965ef1537379288" } } $('.js-work-strip[data-work-id=109368633]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368633,"title":"Numerical scheme for estimating all roots of non-linear equations with applications","translated_title":"","metadata":{"doi":"10.3934/math.20231200","issue":"10","volume":"8","abstract":"The roots of non-linear equations are a major challenge in many scientific and professional fields. This problem has been approached in a number of ways, including use of the sequential Newton's method and the traditional Weierstrass simultaneous iterative scheme. To approximate all of the roots of a given nonlinear equation, sequential iterative algorithms must use a deflation strategy because rounding errors can produce inaccurate results. This study aims to develop an efficient numerical simultaneous scheme for approximating all nonlinear equations' roots of convergence order 12. The numerical outcomes of the considered engineering problems show that, in terms of accuracy, validations, error, computational CPU time, and residual error, recently developed simultaneous methods perform better than existing methods in the literature.","page_numbers":"23603-23620","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"AIMS Mathematics"},"translated_abstract":"The roots of non-linear equations are a major challenge in many scientific and professional fields. This problem has been approached in a number of ways, including use of the sequential Newton's method and the traditional Weierstrass simultaneous iterative scheme. To approximate all of the roots of a given nonlinear equation, sequential iterative algorithms must use a deflation strategy because rounding errors can produce inaccurate results. This study aims to develop an efficient numerical simultaneous scheme for approximating all nonlinear equations' roots of convergence order 12. The numerical outcomes of the considered engineering problems show that, in terms of accuracy, validations, error, computational CPU time, and residual error, recently developed simultaneous methods perform better than existing methods in the literature.","internal_url":"https://www.academia.edu/109368633/Numerical_scheme_for_estimating_all_roots_of_non_linear_equations_with_applications","translated_internal_url":"","created_at":"2023-11-18T22:37:23.783-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":40553715,"work_id":109368633,"tagging_user_id":29298824,"tagged_user_id":252057293,"co_author_invite_id":null,"email":"g***o@yahoo.co.uk","display_order":1,"name":"Georgia Oros","title":"Numerical scheme for estimating all roots of non-linear equations with applications"},{"id":40553716,"work_id":109368633,"tagging_user_id":29298824,"tagged_user_id":null,"co_author_invite_id":47387,"email":"s***i@hku.edu.tr","display_order":2,"name":"Serkan Araci","title":"Numerical scheme for estimating all roots of non-linear equations with applications"}],"downloadable_attachments":[{"id":107515193,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515193/thumbnails/1.jpg","file_name":"3.pdf","download_url":"https://www.academia.edu/attachments/107515193/download_file","bulk_download_file_name":"Numerical_scheme_for_estimating_all_root.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515193/3-libre.pdf?1700377384=\u0026response-content-disposition=attachment%3B+filename%3DNumerical_scheme_for_estimating_all_root.pdf\u0026Expires=1738795241\u0026Signature=dSXzP2ABMGVeGIchOF~oPXmRIiZeFNpT4HKnTNPPHTbvgHpTC81cMzVlCo3cKzvHP8qPkzx5JQbI4BOgO0KYhMuQL6CF4EKquT4KjSkGdHFvYf~0RT1sfiS88-QOlnjX6iSnHH47ffDG2x~640kD~aQjU7iKTVMNrBBS16zwefSYACRvLnRrrFQ8M1uwlGC-Dyj3svMqAstjE75SihjBevxaAetL1jcUnkVfqUZTwHG979~7o9DrdGyiWiBWQ4V2kGtMokZqdWDtmd1qOz36o0~gI6mI7gY94yFlIfu6pzsKxpswZkhR3mc74NOnt~4dG4lBr0zUxykohV9hzTNFtw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Numerical_scheme_for_estimating_all_roots_of_non_linear_equations_with_applications","translated_slug":"","page_count":18,"language":"en","content_type":"Work","summary":"The roots of non-linear equations are a major challenge in many scientific and professional fields. This problem has been approached in a number of ways, including use of the sequential Newton's method and the traditional Weierstrass simultaneous iterative scheme. To approximate all of the roots of a given nonlinear equation, sequential iterative algorithms must use a deflation strategy because rounding errors can produce inaccurate results. This study aims to develop an efficient numerical simultaneous scheme for approximating all nonlinear equations' roots of convergence order 12. The numerical outcomes of the considered engineering problems show that, in terms of accuracy, validations, error, computational CPU time, and residual error, recently developed simultaneous methods perform better than existing methods in the literature.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":107515193,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515193/thumbnails/1.jpg","file_name":"3.pdf","download_url":"https://www.academia.edu/attachments/107515193/download_file","bulk_download_file_name":"Numerical_scheme_for_estimating_all_root.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515193/3-libre.pdf?1700377384=\u0026response-content-disposition=attachment%3B+filename%3DNumerical_scheme_for_estimating_all_root.pdf\u0026Expires=1738795241\u0026Signature=dSXzP2ABMGVeGIchOF~oPXmRIiZeFNpT4HKnTNPPHTbvgHpTC81cMzVlCo3cKzvHP8qPkzx5JQbI4BOgO0KYhMuQL6CF4EKquT4KjSkGdHFvYf~0RT1sfiS88-QOlnjX6iSnHH47ffDG2x~640kD~aQjU7iKTVMNrBBS16zwefSYACRvLnRrrFQ8M1uwlGC-Dyj3svMqAstjE75SihjBevxaAetL1jcUnkVfqUZTwHG979~7o9DrdGyiWiBWQ4V2kGtMokZqdWDtmd1qOz36o0~gI6mI7gY94yFlIfu6pzsKxpswZkhR3mc74NOnt~4dG4lBr0zUxykohV9hzTNFtw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":12022,"name":"Numerical Analysis","url":"https://www.academia.edu/Documents/in/Numerical_Analysis"}],"urls":[{"id":35527626,"url":"https://www.aimspress.com/article/doi/10.3934/math.20231200"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109368535"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109368535/SOLVING_AN_INTEGRAL_EQUATION_VIA_INTUITIONISTIC_FUZZY_BIPOLAR_METRIC_SPACES"><img alt="Research paper thumbnail of SOLVING AN INTEGRAL EQUATION VIA INTUITIONISTIC FUZZY BIPOLAR METRIC SPACES" class="work-thumbnail" src="https://attachments.academia-assets.com/107515113/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109368535/SOLVING_AN_INTEGRAL_EQUATION_VIA_INTUITIONISTIC_FUZZY_BIPOLAR_METRIC_SPACES">SOLVING AN INTEGRAL EQUATION VIA INTUITIONISTIC FUZZY BIPOLAR METRIC SPACES</a></div><div class="wp-workCard_item"><span>Decision Making Applications in Management and Engineering</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we introduce the notion of intuitionistic fuzzy bipolar metric space and prove fix...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, we introduce the notion of intuitionistic fuzzy bipolar metric space and prove fixed point theorems. Our results are extension or generalisation of results proved in the literature. The derived results are substantiated with suitable example and an application.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="788fe4aace7c247a0f92c61aefd4c571" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515113,"asset_id":109368535,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515113/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368535"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368535"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368535; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109368358"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109368358/A_Stable_Fuzzy_Based_Computational_Model_and_Control_for_Inductions_Motors"><img alt="Research paper thumbnail of A Stable Fuzzy-Based Computational Model and Control for Inductions Motors" class="work-thumbnail" src="https://attachments.academia-assets.com/107515044/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109368358/A_Stable_Fuzzy_Based_Computational_Model_and_Control_for_Inductions_Motors">A Stable Fuzzy-Based Computational Model and Control for Inductions Motors</a></div><div class="wp-workCard_item"><span>Computer Modeling in Engineering and Sciences</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is in...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99842c665c6390495756f550a770771a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515044,"asset_id":109368358,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515044/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368358"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368358"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368358; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368358]").text(description); $(".js-view-count[data-work-id=109368358]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368358; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368358']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "99842c665c6390495756f550a770771a" } } $('.js-work-strip[data-work-id=109368358]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368358,"title":"A Stable Fuzzy-Based Computational Model and Control for Inductions Motors","translated_title":"","metadata":{"doi":"10.32604/cmes.2023.028175","issue":"1","volume":"138","abstract":"In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.","page_numbers":" 793-812","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Computer Modeling in Engineering and Sciences"},"translated_abstract":"In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.","internal_url":"https://www.academia.edu/109368358/A_Stable_Fuzzy_Based_Computational_Model_and_Control_for_Inductions_Motors","translated_internal_url":"","created_at":"2023-11-18T22:28:11.619-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107515044,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515044/thumbnails/1.jpg","file_name":"5.pdf","download_url":"https://www.academia.edu/attachments/107515044/download_file","bulk_download_file_name":"A_Stable_Fuzzy_Based_Computational_Model.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515044/5-libre.pdf?1700377426=\u0026response-content-disposition=attachment%3B+filename%3DA_Stable_Fuzzy_Based_Computational_Model.pdf\u0026Expires=1738795241\u0026Signature=NURfPnokv01A1xSGdgeEO~CBCjZ3-XWh18U9wUNMjaNRQKErzewccikTZTene0GA5Qep~3V5tbccLjnej0FrT-~hnXvdQ4SC0aru58-szzpDxD8DM1OzOHCGp0gmQe~vidWbYZtDsLyVYRpotU5ZNAEaKqm9mTcpi7avewFhjX94z~yFgDuXH6amSx0fZKtpcLirqDA9sOzJnrYgaRG8j7sndCwk7jfhGfEv-lPtIEzaIP4ImA-AbbbRA7R1kUuIUta79YvElQkC5zPOVFy-Krshi6VdT4597f5B4QD-XeyHbn8btxHbUHtCjumnCLXj8rw~KOxTWHawyo1tf5rnKQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Stable_Fuzzy_Based_Computational_Model_and_Control_for_Inductions_Motors","translated_slug":"","page_count":20,"language":"en","content_type":"Work","summary":"In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":107515044,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515044/thumbnails/1.jpg","file_name":"5.pdf","download_url":"https://www.academia.edu/attachments/107515044/download_file","bulk_download_file_name":"A_Stable_Fuzzy_Based_Computational_Model.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515044/5-libre.pdf?1700377426=\u0026response-content-disposition=attachment%3B+filename%3DA_Stable_Fuzzy_Based_Computational_Model.pdf\u0026Expires=1738795241\u0026Signature=NURfPnokv01A1xSGdgeEO~CBCjZ3-XWh18U9wUNMjaNRQKErzewccikTZTene0GA5Qep~3V5tbccLjnej0FrT-~hnXvdQ4SC0aru58-szzpDxD8DM1OzOHCGp0gmQe~vidWbYZtDsLyVYRpotU5ZNAEaKqm9mTcpi7avewFhjX94z~yFgDuXH6amSx0fZKtpcLirqDA9sOzJnrYgaRG8j7sndCwk7jfhGfEv-lPtIEzaIP4ImA-AbbbRA7R1kUuIUta79YvElQkC5zPOVFy-Krshi6VdT4597f5B4QD-XeyHbn8btxHbUHtCjumnCLXj8rw~KOxTWHawyo1tf5rnKQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1621284,"name":"Mathematical Engineering","url":"https://www.academia.edu/Documents/in/Mathematical_Engineering"}],"urls":[{"id":35527468,"url":"https://www.techscience.com/CMES/v138n1/54264"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109368140"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109368140/Robotic_sensor_based_on_score_and_accuracy_values_in_q_rung_complex_diophatine_neutrosophic_normal_set_with_an_aggregation_operation"><img alt="Research paper thumbnail of Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation" class="work-thumbnail" src="https://attachments.academia-assets.com/107514868/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109368140/Robotic_sensor_based_on_score_and_accuracy_values_in_q_rung_complex_diophatine_neutrosophic_normal_set_with_an_aggregation_operation">Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation</a></div><div class="wp-workCard_item"><span>Alexandria Engineering Journal </span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex dioph...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (qrung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="916072114d332638ac930184b230ae1a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107514868,"asset_id":109368140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107514868/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368140; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368140]").text(description); $(".js-view-count[data-work-id=109368140]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368140; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368140']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "916072114d332638ac930184b230ae1a" } } $('.js-work-strip[data-work-id=109368140]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368140,"title":"Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation","translated_title":"","metadata":{"doi":"10.1016/j.aej.2023.06.064","volume":"77","abstract":"The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (qrung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology.","ai_title_tag":"MADM Solutions with q-Rung CDNNS and Aggregation Operations","page_numbers":"149-164","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Alexandria Engineering Journal "},"translated_abstract":"The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (qrung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology.","internal_url":"https://www.academia.edu/109368140/Robotic_sensor_based_on_score_and_accuracy_values_in_q_rung_complex_diophatine_neutrosophic_normal_set_with_an_aggregation_operation","translated_internal_url":"","created_at":"2023-11-18T22:22:01.672-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":40553697,"work_id":109368140,"tagging_user_id":29298824,"tagged_user_id":null,"co_author_invite_id":7559360,"email":"h***g@thapar.edu","display_order":1,"name":"Harish Garg","title":"Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation"},{"id":40553698,"work_id":109368140,"tagging_user_id":29298824,"tagged_user_id":null,"co_author_invite_id":7957081,"email":"s***y@nor-off.no","display_order":2,"name":"Seifedine Kadry","title":"Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation"}],"downloadable_attachments":[{"id":107514868,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514868/thumbnails/1.jpg","file_name":"6.pdf","download_url":"https://www.academia.edu/attachments/107514868/download_file","bulk_download_file_name":"Robotic_sensor_based_on_score_and_accura.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514868/6-libre.pdf?1700377470=\u0026response-content-disposition=attachment%3B+filename%3DRobotic_sensor_based_on_score_and_accura.pdf\u0026Expires=1738795241\u0026Signature=ERfZJlBLcCVZ7L9rbPLjB~fnpw5IK1NMFfBXzhil2u6oJ4P7gAUDrJWX5PbAPgHLf~g815FaLERfzBKgi~zdeESEVx5aUMEYFL7Sem-eGPeuhnAdQkkFFbVEw4tVNlqmwSu66wKXxwIaUOPs9n0oR-XsbXoY3woGKVoEj-W02c3heuoMee-iOWCX7UBxZkfo8AotLrg9g0pQ70-SnOGqRTmY0EMW31X5tJu0W8IunwG2~q2Nsg4JA9GBu0BO3tlfK4mTMocl5mw5JY-83EzDlfzqfXCXl65rnKz8b-bxjJlRg2MPXHu5df9eXac2OG0eO63ZtbaHhshJ9fRajRWDnw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robotic_sensor_based_on_score_and_accuracy_values_in_q_rung_complex_diophatine_neutrosophic_normal_set_with_an_aggregation_operation","translated_slug":"","page_count":16,"language":"en","content_type":"Work","summary":"The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (qrung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":107514868,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514868/thumbnails/1.jpg","file_name":"6.pdf","download_url":"https://www.academia.edu/attachments/107514868/download_file","bulk_download_file_name":"Robotic_sensor_based_on_score_and_accura.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514868/6-libre.pdf?1700377470=\u0026response-content-disposition=attachment%3B+filename%3DRobotic_sensor_based_on_score_and_accura.pdf\u0026Expires=1738795241\u0026Signature=ERfZJlBLcCVZ7L9rbPLjB~fnpw5IK1NMFfBXzhil2u6oJ4P7gAUDrJWX5PbAPgHLf~g815FaLERfzBKgi~zdeESEVx5aUMEYFL7Sem-eGPeuhnAdQkkFFbVEw4tVNlqmwSu66wKXxwIaUOPs9n0oR-XsbXoY3woGKVoEj-W02c3heuoMee-iOWCX7UBxZkfo8AotLrg9g0pQ70-SnOGqRTmY0EMW31X5tJu0W8IunwG2~q2Nsg4JA9GBu0BO3tlfK4mTMocl5mw5JY-83EzDlfzqfXCXl65rnKz8b-bxjJlRg2MPXHu5df9eXac2OG0eO63ZtbaHhshJ9fRajRWDnw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109367977"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109367977/A_novel_approach_based_on_neutrosophic_Bonferroni_mean_operator_of_trapezoidal_and_triangular_neutrosophic_interval_environments_in_multi_attribute_group_decision_making"><img alt="Research paper thumbnail of A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making" class="work-thumbnail" src="https://attachments.academia-assets.com/107514747/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109367977/A_novel_approach_based_on_neutrosophic_Bonferroni_mean_operator_of_trapezoidal_and_triangular_neutrosophic_interval_environments_in_multi_attribute_group_decision_making">A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine seve...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy. Abbreviations NMAGDM Neutrosophic Multi-Attribute Group Decision Making DMs Decision makers TITENBM Trapezoidal and triangular neutrosophic Bonferroni mean TITRNWBM Trapezoidal and triangular neutrosophic weighted Bonferroni mean FMAGDM Fuzzy multi-attributes group decision-making OPEN 1</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5995d47a92abcf471de1020978c246e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107514747,"asset_id":109367977,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107514747/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109367977"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109367977"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109367977; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109367977]").text(description); $(".js-view-count[data-work-id=109367977]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109367977; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109367977']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5995d47a92abcf471de1020978c246e6" } } $('.js-work-strip[data-work-id=109367977]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109367977,"title":"A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making","translated_title":"","metadata":{"doi":"10.1038/s41598-023-37497-z","issue":"1","volume":"13","abstract":"Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy. Abbreviations NMAGDM Neutrosophic Multi-Attribute Group Decision Making DMs Decision makers TITENBM Trapezoidal and triangular neutrosophic Bonferroni mean TITRNWBM Trapezoidal and triangular neutrosophic weighted Bonferroni mean FMAGDM Fuzzy multi-attributes group decision-making OPEN 1","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Scientific Reports"},"translated_abstract":"Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. 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The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy. Abbreviations NMAGDM Neutrosophic Multi-Attribute Group Decision Making DMs Decision makers TITENBM Trapezoidal and triangular neutrosophic Bonferroni mean TITRNWBM Trapezoidal and triangular neutrosophic weighted Bonferroni mean FMAGDM Fuzzy multi-attributes group decision-making OPEN 1","internal_url":"https://www.academia.edu/109367977/A_novel_approach_based_on_neutrosophic_Bonferroni_mean_operator_of_trapezoidal_and_triangular_neutrosophic_interval_environments_in_multi_attribute_group_decision_making","translated_internal_url":"","created_at":"2023-11-18T22:15:10.240-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107514747,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514747/thumbnails/1.jpg","file_name":"7.pdf","download_url":"https://www.academia.edu/attachments/107514747/download_file","bulk_download_file_name":"A_novel_approach_based_on_neutrosophic_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514747/7-libre.pdf?1700377510=\u0026response-content-disposition=attachment%3B+filename%3DA_novel_approach_based_on_neutrosophic_B.pdf\u0026Expires=1738795241\u0026Signature=bklajB-h89T6~ukQuVIcvoBdZcAB5RbIJaXtsy4SA0-8RjZH7xIYo0y9mNLkwdr9iguYBkYGvJRkZHx7IFaRsSpTM-xKnfBFKb8taBdf5q4WeX9bA46EB-U3-U1wREXoUR3fYU5wqg5Jx8w3pD84GdP6r3gNkSZrhZC-y1dBX~E9J12gwI1Gm3TUq4p3HMqnB4DWNIbYQs7LbI5kOukWDyrf3zzR54KOIJ7J9cBBdP01nQvv73luKpzH32B6Q0awtYtRkyQXisC-q1XzKGsIWM2wXjvkqme8wb1eInIryPk-zdCO5gWcZqVaoJiouTBUD6Feoe0mysTDXUsm920g4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_novel_approach_based_on_neutrosophic_Bonferroni_mean_operator_of_trapezoidal_and_triangular_neutrosophic_interval_environments_in_multi_attribute_group_decision_making","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy. Abbreviations NMAGDM Neutrosophic Multi-Attribute Group Decision Making DMs Decision makers TITENBM Trapezoidal and triangular neutrosophic Bonferroni mean TITRNWBM Trapezoidal and triangular neutrosophic weighted Bonferroni mean FMAGDM Fuzzy multi-attributes group decision-making OPEN 1","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":107514747,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514747/thumbnails/1.jpg","file_name":"7.pdf","download_url":"https://www.academia.edu/attachments/107514747/download_file","bulk_download_file_name":"A_novel_approach_based_on_neutrosophic_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514747/7-libre.pdf?1700377510=\u0026response-content-disposition=attachment%3B+filename%3DA_novel_approach_based_on_neutrosophic_B.pdf\u0026Expires=1738795241\u0026Signature=bklajB-h89T6~ukQuVIcvoBdZcAB5RbIJaXtsy4SA0-8RjZH7xIYo0y9mNLkwdr9iguYBkYGvJRkZHx7IFaRsSpTM-xKnfBFKb8taBdf5q4WeX9bA46EB-U3-U1wREXoUR3fYU5wqg5Jx8w3pD84GdP6r3gNkSZrhZC-y1dBX~E9J12gwI1Gm3TUq4p3HMqnB4DWNIbYQs7LbI5kOukWDyrf3zzR54KOIJ7J9cBBdP01nQvv73luKpzH32B6Q0awtYtRkyQXisC-q1XzKGsIWM2wXjvkqme8wb1eInIryPk-zdCO5gWcZqVaoJiouTBUD6Feoe0mysTDXUsm920g4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"}],"urls":[{"id":35527213,"url":"https://www.nature.com/articles/s41598-023-37497-z"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109367885"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109367885/CHARACTERIZING_HYPERGROUPOIDS_THROUGH_M_RELATIONS_AND_M_CONSISTENCIES"><img alt="Research paper thumbnail of CHARACTERIZING HYPERGROUPOIDS THROUGH M -RELATIONS AND M -CONSISTENCIES" class="work-thumbnail" src="https://attachments.academia-assets.com/107514665/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109367885/CHARACTERIZING_HYPERGROUPOIDS_THROUGH_M_RELATIONS_AND_M_CONSISTENCIES">CHARACTERIZING HYPERGROUPOIDS THROUGH M -RELATIONS AND M -CONSISTENCIES</a></div><div class="wp-workCard_item"><span>JOURNAL OF THE INTERNATIONAL MATHEMATICAL VIRTUAL INSTITUTE</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this article, we define the m-right consistent/m-left consistent and m-consistent hypergroupoi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this article, we define the m-right consistent/m-left consistent and m-consistent hypergroupoids. We also define the m-intra-consistent hypergroupoid. Along these line, we define the Green's m-relations namely mright relation, m-left relations, and m-relation. The other three relations, namely m-reflexive, m-symmetric and m-transitive, are also defined. The idea of m-equivalence relation is also given. We present different characterization of hypergroupoids in the article through these concepts.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="15ea2471f3efa4252706e426455b26ba" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107514665,"asset_id":109367885,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107514665/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109367885"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109367885"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109367885; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109367585"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109367585/Medical_robotic_engineering_selection_based_on_square_root_neutrosophic_normal_interval_valued_sets_and_their_aggregated_operators"><img alt="Research paper thumbnail of Medical robotic engineering selection based on square root neutrosophic normal interval-valued sets and their aggregated operators" class="work-thumbnail" src="https://attachments.academia-assets.com/107514462/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109367585/Medical_robotic_engineering_selection_based_on_square_root_neutrosophic_normal_interval_valued_sets_and_their_aggregated_operators">Medical robotic engineering selection based on square root neutrosophic normal interval-valued sets and their aggregated operators</a></div><div class="wp-workCard_item"><span>AIMS Mathematics</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We introduce the concepts of multiple attribute decision-making (MADM) using square root neutroso...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We introduce the concepts of multiple attribute decision-making (MADM) using square root neutrosophic normal interval-valued sets (SRNSNIVS). The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer ∆. A medical robotics system is described as combining computer science and 17403 machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="915c027f30d201391c11e3c9be7693eb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107514462,"asset_id":109367585,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107514462/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109367585"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109367585"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109367585; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109367585]").text(description); $(".js-view-count[data-work-id=109367585]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109367585; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109367585']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "915c027f30d201391c11e3c9be7693eb" } } $('.js-work-strip[data-work-id=109367585]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109367585,"title":"Medical robotic engineering selection based on square root neutrosophic normal interval-valued sets and their aggregated operators","translated_title":"","metadata":{"doi":"10.3934/math.2023889","issue":"8","volume":"8","abstract":"We introduce the concepts of multiple attribute decision-making (MADM) using square root neutrosophic normal interval-valued sets (SRNSNIVS). The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer ∆. A medical robotics system is described as combining computer science and 17403 machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.","ai_title_tag":"MADM for Medical Robotics Using Neutrosophic Sets","page_numbers":"17402–17432.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"AIMS Mathematics"},"translated_abstract":"We introduce the concepts of multiple attribute decision-making (MADM) using square root neutrosophic normal interval-valued sets (SRNSNIVS). The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer ∆. A medical robotics system is described as combining computer science and 17403 machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. 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The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer ∆. A medical robotics system is described as combining computer science and 17403 machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":107514462,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514462/thumbnails/1.jpg","file_name":"9.pdf","download_url":"https://www.academia.edu/attachments/107514462/download_file","bulk_download_file_name":"Medical_robotic_engineering_selection_ba.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514462/9-libre.pdf?1700377588=\u0026response-content-disposition=attachment%3B+filename%3DMedical_robotic_engineering_selection_ba.pdf\u0026Expires=1738795241\u0026Signature=Ru5gBj7HU6EN4jT30OJAnu59st~FI9l-jB7LJbxSKwxe-6UxTKHTKxi4OMOYrsUOJaUD8~5m8hQYBzo5wZ71oxFOaZzjxjziisT7IxPSIw4oj7Y4nWGSB35a5c0p4J5b0BwdEDBFs4nIoQYNybevzcq6ypbY773rMf8SutTvwO24vJKkz4VyPBFyckGadHlF5LXTRiCmjEuu-HbR8q3CVpECWtr9aAC1cJhFVezdzzLJsEEDhZBy0JwqUQ2-TKpC-Jg40zevP0lxftKOwH5ztybJXuZGxEW0obVypt7DRZpRqc~GqYDEzPYn9l6E1f2SounGY3P1OcNPHCrYm0MA8w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"}],"urls":[{"id":35527022,"url":"https://www.aimspress.com/article/doi/10.3934/math.2023889"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105590790"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105590790/Characterizations_of_non_associative_ordered_semigroups_by_their_fuzzy_bi_ideals"><img alt="Research paper thumbnail of Characterizations of non-associative ordered semigroups by their fuzzy bi-ideals" class="work-thumbnail" src="https://attachments.academia-assets.com/105002462/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105590790/Characterizations_of_non_associative_ordered_semigroups_by_their_fuzzy_bi_ideals">Characterizations of non-associative ordered semigroups by their fuzzy bi-ideals</a></div><div class="wp-workCard_item"><span>Theoretical Computer Science</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The aim of this paper is to investigate the characterizations of different classes of nonassociat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The aim of this paper is to investigate the characterizations of different classes of nonassociative and non-commutative ordered semigroups in terms of fuzzy left (right, bi-, generalized bi-, (1, 2)-) ideals.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="631ce284c10f49e0b733e0897d3e87bf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105002462,"asset_id":105590790,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105002462/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105590790"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105590790"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105590790; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101951836"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101951836/Techniques_for_Finding_Analytical_Solution_of_Generalized_Fuzzy_Differential_Equations_with_Applications"><img alt="Research paper thumbnail of Techniques for Finding Analytical Solution of Generalized Fuzzy Differential Equations with Applications" class="work-thumbnail" src="https://attachments.academia-assets.com/102349051/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101951836/Techniques_for_Finding_Analytical_Solution_of_Generalized_Fuzzy_Differential_Equations_with_Applications">Techniques for Finding Analytical Solution of Generalized Fuzzy Differential Equations with Applications</a></div><div class="wp-workCard_item"><span>COMLEXITY </span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Engineering and applied mathematics disciplines that involve diferential equations include classi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Engineering and applied mathematics disciplines that involve diferential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufcient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are signifcantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at diferential equations in a generalized intuitionistic fuzzy environment. We used the modifed Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. Te generalized modifed Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. Te outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modifed Adomian decomposition method is efcient, robotic, and reliable, as well as simple to implement.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2c2f26d3c755792284edaad98df1f9fe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":102349051,"asset_id":101951836,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/102349051/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101951836"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101951836"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101951836; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101951836]").text(description); $(".js-view-count[data-work-id=101951836]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101951836; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101951836']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2c2f26d3c755792284edaad98df1f9fe" } } $('.js-work-strip[data-work-id=101951836]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101951836,"title":"Techniques for Finding Analytical Solution of Generalized Fuzzy Differential Equations with Applications","translated_title":"","metadata":{"doi":"10.1155/2023/3000653","volume":"2023","abstract":"Engineering and applied mathematics disciplines that involve diferential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufcient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are signifcantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at diferential equations in a generalized intuitionistic fuzzy environment. We used the modifed Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. Te generalized modifed Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. Te outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modifed Adomian decomposition method is efcient, robotic, and reliable, as well as simple to implement.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"COMLEXITY "},"translated_abstract":"Engineering and applied mathematics disciplines that involve diferential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufcient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are signifcantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at diferential equations in a generalized intuitionistic fuzzy environment. We used the modifed Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. Te generalized modifed Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. Te outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modifed Adomian decomposition method is efcient, robotic, and reliable, as well as simple to implement.","internal_url":"https://www.academia.edu/101951836/Techniques_for_Finding_Analytical_Solution_of_Generalized_Fuzzy_Differential_Equations_with_Applications","translated_internal_url":"","created_at":"2023-05-17T20:39:22.208-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":102349051,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102349051/thumbnails/1.jpg","file_name":"4.pdf","download_url":"https://www.academia.edu/attachments/102349051/download_file","bulk_download_file_name":"Techniques_for_Finding_Analytical_Soluti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102349051/4-libre.pdf?1684381990=\u0026response-content-disposition=attachment%3B+filename%3DTechniques_for_Finding_Analytical_Soluti.pdf\u0026Expires=1738795241\u0026Signature=ai-S4J2AoDjbi00y4RCFytYOWYLOf02GIwwcc5aI1h3iRIFJnDkYu5zHZ4Efo8w1dvBcb0ZqjGsLgXQJZ5ZCPQgQFu5KOust~W4aIwS9HfQpJbvsX-WDhOXjcsVS9hZhLD2tU57o9SZWs7phZ6MM8AhmtFNZPI5yKolU2Ir097cqMPJ917CHfTJUC2Q2M9Gs1iuXqP~0IE2NuiDFa42S9U~eGz578JjxpfYYYECFircB6lApK7K4mzq9OdV3gXou-WrN9zMod7WrIyaCTXW1P9XABr-KEdmNlexHeosqIN8NFYxvmZi2abFnWcOMxlLBCFnyfpBMUyXXYf8mpIZWXw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Techniques_for_Finding_Analytical_Solution_of_Generalized_Fuzzy_Differential_Equations_with_Applications","translated_slug":"","page_count":31,"language":"en","content_type":"Work","summary":"Engineering and applied mathematics disciplines that involve diferential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufcient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are signifcantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at diferential equations in a generalized intuitionistic fuzzy environment. We used the modifed Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. Te generalized modifed Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. Te outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modifed Adomian decomposition method is efcient, robotic, and reliable, as well as simple to implement.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":102349051,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102349051/thumbnails/1.jpg","file_name":"4.pdf","download_url":"https://www.academia.edu/attachments/102349051/download_file","bulk_download_file_name":"Techniques_for_Finding_Analytical_Soluti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102349051/4-libre.pdf?1684381990=\u0026response-content-disposition=attachment%3B+filename%3DTechniques_for_Finding_Analytical_Soluti.pdf\u0026Expires=1738795241\u0026Signature=ai-S4J2AoDjbi00y4RCFytYOWYLOf02GIwwcc5aI1h3iRIFJnDkYu5zHZ4Efo8w1dvBcb0ZqjGsLgXQJZ5ZCPQgQFu5KOust~W4aIwS9HfQpJbvsX-WDhOXjcsVS9hZhLD2tU57o9SZWs7phZ6MM8AhmtFNZPI5yKolU2Ir097cqMPJ917CHfTJUC2Q2M9Gs1iuXqP~0IE2NuiDFa42S9U~eGz578JjxpfYYYECFircB6lApK7K4mzq9OdV3gXou-WrN9zMod7WrIyaCTXW1P9XABr-KEdmNlexHeosqIN8NFYxvmZi2abFnWcOMxlLBCFnyfpBMUyXXYf8mpIZWXw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":12022,"name":"Numerical Analysis","url":"https://www.academia.edu/Documents/in/Numerical_Analysis"},{"id":30888,"name":"Differential Equations","url":"https://www.academia.edu/Documents/in/Differential_Equations"},{"id":31900,"name":"Fuzzy","url":"https://www.academia.edu/Documents/in/Fuzzy"}],"urls":[{"id":31534825,"url":"https://www.hindawi.com/journals/complexity/2023/3000653/"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101951706"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101951706/Robot_sensors_process_based_on_generalized_Fermatean_normal_different_aggregation_operators_framework"><img alt="Research paper thumbnail of Robot sensors process based on generalized Fermatean normal different aggregation operators framework" class="work-thumbnail" src="https://attachments.academia-assets.com/102348946/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101951706/Robot_sensors_process_based_on_generalized_Fermatean_normal_different_aggregation_operators_framework">Robot sensors process based on generalized Fermatean normal different aggregation operators framework</a></div><div class="wp-workCard_item"><span>Aims Mathematics</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Novel methods for multiple attribute decision-making problems are presented in this paper using T...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number δ. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08d23cd26bfec78f325b1f5f9a02a4d6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":102348946,"asset_id":101951706,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/102348946/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101951706"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101951706"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101951706; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101951706]").text(description); $(".js-view-count[data-work-id=101951706]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101951706; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101951706']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "08d23cd26bfec78f325b1f5f9a02a4d6" } } $('.js-work-strip[data-work-id=101951706]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101951706,"title":"Robot sensors process based on generalized Fermatean normal different aggregation operators framework","translated_title":"","metadata":{"doi":"10.3934/math.2023832","issue":"7","volume":"8","abstract":"Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number δ. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.","page_numbers":"16252-16277","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Aims Mathematics"},"translated_abstract":"Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number δ. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.","internal_url":"https://www.academia.edu/101951706/Robot_sensors_process_based_on_generalized_Fermatean_normal_different_aggregation_operators_framework","translated_internal_url":"","created_at":"2023-05-17T20:35:12.393-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":102348946,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348946/thumbnails/1.jpg","file_name":"3.pdf","download_url":"https://www.academia.edu/attachments/102348946/download_file","bulk_download_file_name":"Robot_sensors_process_based_on_generaliz.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348946/3-libre.pdf?1684382008=\u0026response-content-disposition=attachment%3B+filename%3DRobot_sensors_process_based_on_generaliz.pdf\u0026Expires=1738795241\u0026Signature=U6UQU-ug2fys9DWpBPidB8QSfPp~-g-R1LLx~A1JZR~OzDO8lZLl9qQfFwYtYDlcQGuxPlZgUZ4lDXIscWgXB36d0ZB0iXbOv0H~MHlROpMbh7VQ60wiND9wMFbteGaYWRtcsP4EwsTXjicrs1ex8bIO20qvsL7a7cBllHZjFLAZ4Yvo3xP7nLGr1NRehAtbdTS4TBvrbe3kYlC8Hzo7v6c-VjB8IsFYRLjTOYsGLFn~6ISGefMUGZg~GvUahRo0d23ENEYTXDkfej~hEkHfxHeX6Jlpr1pAYvW1aUhv7Rr2EFv812wFoQTo0XJLT53sutnjsgk5ZAYQ2BNhEmd04Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robot_sensors_process_based_on_generalized_Fermatean_normal_different_aggregation_operators_framework","translated_slug":"","page_count":26,"language":"en","content_type":"Work","summary":"Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number δ. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":102348946,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348946/thumbnails/1.jpg","file_name":"3.pdf","download_url":"https://www.academia.edu/attachments/102348946/download_file","bulk_download_file_name":"Robot_sensors_process_based_on_generaliz.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348946/3-libre.pdf?1684382008=\u0026response-content-disposition=attachment%3B+filename%3DRobot_sensors_process_based_on_generaliz.pdf\u0026Expires=1738795241\u0026Signature=U6UQU-ug2fys9DWpBPidB8QSfPp~-g-R1LLx~A1JZR~OzDO8lZLl9qQfFwYtYDlcQGuxPlZgUZ4lDXIscWgXB36d0ZB0iXbOv0H~MHlROpMbh7VQ60wiND9wMFbteGaYWRtcsP4EwsTXjicrs1ex8bIO20qvsL7a7cBllHZjFLAZ4Yvo3xP7nLGr1NRehAtbdTS4TBvrbe3kYlC8Hzo7v6c-VjB8IsFYRLjTOYsGLFn~6ISGefMUGZg~GvUahRo0d23ENEYTXDkfej~hEkHfxHeX6Jlpr1pAYvW1aUhv7Rr2EFv812wFoQTo0XJLT53sutnjsgk5ZAYQ2BNhEmd04Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":667751,"name":"Aggregation Operators","url":"https://www.academia.edu/Documents/in/Aggregation_Operators"}],"urls":[{"id":31534768,"url":"https://www.aimspress.com/article/doi/10.3934/math.2023832"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101951613"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101951613/Financial_Performance_Assessment_by_a_Type_2_Fuzzy_Logic_Approach"><img alt="Research paper thumbnail of Financial Performance Assessment by a Type-2 Fuzzy Logic Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/102348876/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101951613/Financial_Performance_Assessment_by_a_Type_2_Fuzzy_Logic_Approach">Financial Performance Assessment by a Type-2 Fuzzy Logic Approach</a></div><div class="wp-workCard_item"><span>Mathematical Problems in Engineering</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Any company must constantly innovate if they want to maintain its market share in the present cut...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big infuence on consumer behavior, yet it goes against the principles of sustainability. Te issue of sustainability has become crucial to their company's growth. In order to evaluate a business frm's sustainability performance statistically, a new and efective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. Te judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. Te main input variables include corporate environmental performance (CEP) and corporate fnancial performance (CFP). Te suggested approach can efectively examine a corporation's sustainable performance, according to experimental fndings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. Te recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="591225f0fee971903c49de9d2b59631c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":102348876,"asset_id":101951613,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/102348876/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101951613"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101951613"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101951613; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101951613]").text(description); $(".js-view-count[data-work-id=101951613]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101951613; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101951613']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "591225f0fee971903c49de9d2b59631c" } } $('.js-work-strip[data-work-id=101951613]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101951613,"title":"Financial Performance Assessment by a Type-2 Fuzzy Logic Approach","translated_title":"","metadata":{"doi":"10.1155/2023/5926162","volume":"2023","abstract":"Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big infuence on consumer behavior, yet it goes against the principles of sustainability. Te issue of sustainability has become crucial to their company's growth. In order to evaluate a business frm's sustainability performance statistically, a new and efective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. Te judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. Te main input variables include corporate environmental performance (CEP) and corporate fnancial performance (CFP). Te suggested approach can efectively examine a corporation's sustainable performance, according to experimental fndings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. Te recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.","ai_title_tag":"Type-2 Fuzzy Logic for Financial Assessment","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Mathematical Problems in Engineering"},"translated_abstract":"Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big infuence on consumer behavior, yet it goes against the principles of sustainability. Te issue of sustainability has become crucial to their company's growth. In order to evaluate a business frm's sustainability performance statistically, a new and efective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. Te judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. Te main input variables include corporate environmental performance (CEP) and corporate fnancial performance (CFP). Te suggested approach can efectively examine a corporation's sustainable performance, according to experimental fndings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. Te recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.","internal_url":"https://www.academia.edu/101951613/Financial_Performance_Assessment_by_a_Type_2_Fuzzy_Logic_Approach","translated_internal_url":"","created_at":"2023-05-17T20:33:06.284-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":102348876,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348876/thumbnails/1.jpg","file_name":"2.pdf","download_url":"https://www.academia.edu/attachments/102348876/download_file","bulk_download_file_name":"Financial_Performance_Assessment_by_a_Ty.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348876/2-libre.pdf?1684382012=\u0026response-content-disposition=attachment%3B+filename%3DFinancial_Performance_Assessment_by_a_Ty.pdf\u0026Expires=1738795241\u0026Signature=cKwcUxc5iUGiCrZTOkuRkPrW~IzC8uFdWuthF5c5YaWxz23G57KnmY~nWYar~0HltY2wv~zhthhnS-0MLXzGndFNirm6CdEsB3MUnPrvyclvlxaHHMoWSbqasOlbIdKdGnnyULua2aoLVNeJ-ZRvt7gbeL-Pyvbq12QH5e9v4mRPgbDlSg7yM9Y27UWVBarBLCxJC~OSg8oyVHcAFvPi~5M81doSc6pNIrZb6mMZzPrKNH5baK7GB5pjOwkhB2N5HTOzRemkDz26Wup3lCcgNJPnungoyP45sA6dfZqXkh3H1rlvhCBstaE1b2kZetPbG4rVXpEbz0EuWZp8yzw8pw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Financial_Performance_Assessment_by_a_Type_2_Fuzzy_Logic_Approach","translated_slug":"","page_count":8,"language":"en","content_type":"Work","summary":"Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big infuence on consumer behavior, yet it goes against the principles of sustainability. Te issue of sustainability has become crucial to their company's growth. In order to evaluate a business frm's sustainability performance statistically, a new and efective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. Te judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. Te main input variables include corporate environmental performance (CEP) and corporate fnancial performance (CFP). Te suggested approach can efectively examine a corporation's sustainable performance, according to experimental fndings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. Te recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":102348876,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348876/thumbnails/1.jpg","file_name":"2.pdf","download_url":"https://www.academia.edu/attachments/102348876/download_file","bulk_download_file_name":"Financial_Performance_Assessment_by_a_Ty.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348876/2-libre.pdf?1684382012=\u0026response-content-disposition=attachment%3B+filename%3DFinancial_Performance_Assessment_by_a_Ty.pdf\u0026Expires=1738795241\u0026Signature=cKwcUxc5iUGiCrZTOkuRkPrW~IzC8uFdWuthF5c5YaWxz23G57KnmY~nWYar~0HltY2wv~zhthhnS-0MLXzGndFNirm6CdEsB3MUnPrvyclvlxaHHMoWSbqasOlbIdKdGnnyULua2aoLVNeJ-ZRvt7gbeL-Pyvbq12QH5e9v4mRPgbDlSg7yM9Y27UWVBarBLCxJC~OSg8oyVHcAFvPi~5M81doSc6pNIrZb6mMZzPrKNH5baK7GB5pjOwkhB2N5HTOzRemkDz26Wup3lCcgNJPnungoyP45sA6dfZqXkh3H1rlvhCBstaE1b2kZetPbG4rVXpEbz0EuWZp8yzw8pw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":595898,"name":"Financial Performance Analysis","url":"https://www.academia.edu/Documents/in/Financial_Performance_Analysis"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101951552"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101951552/A_Fuzzy_Intelligent_Computing_Approach_for_Energy_Voltage_Control_of_Microgrids"><img alt="Research paper thumbnail of A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids" class="work-thumbnail" src="https://attachments.academia-assets.com/102348834/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101951552/A_Fuzzy_Intelligent_Computing_Approach_for_Energy_Voltage_Control_of_Microgrids">A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids</a></div><div class="wp-workCard_item"><span>Journal of Mathematics</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Te control and energy management problems of microgrids (MGs) are challenging due to the high lev...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Te control and energy management problems of microgrids (MGs) are challenging due to the high level of uncertainties and disturbances such as changes in demands, mechanical powers, and solar energies. So, intelligent computing is needed to be developed for these systems. Tis paper uses an optimal and robust fuzzy controller for automatic voltage and frequency regulation. Te fuzzy logic develops the resistance against uncertainties and disturbances such as irradiation, wind power changes, and load demand variation. Te introduced controller uses appropriate and efective criteria that include rising time, settling time, overshoot, and the degree of resistance of the control system to uncertainties and perturbation efects. Trough simulations and compassion with conventional regulators, the better accuracy of the suggested approach is demonstrated.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f65bf5acf2f04b82e76b9618af22da1c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":102348834,"asset_id":101951552,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/102348834/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101951552"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101951552"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101951552; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101951552]").text(description); $(".js-view-count[data-work-id=101951552]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101951552; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101951552']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f65bf5acf2f04b82e76b9618af22da1c" } } $('.js-work-strip[data-work-id=101951552]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101951552,"title":"A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids","translated_title":"","metadata":{"doi":"10.1155/2023/5289114","volume":"2023","abstract":"Te control and energy management problems of microgrids (MGs) are challenging due to the high level of uncertainties and disturbances such as changes in demands, mechanical powers, and solar energies. So, intelligent computing is needed to be developed for these systems. Tis paper uses an optimal and robust fuzzy controller for automatic voltage and frequency regulation. Te fuzzy logic develops the resistance against uncertainties and disturbances such as irradiation, wind power changes, and load demand variation. Te introduced controller uses appropriate and efective criteria that include rising time, settling time, overshoot, and the degree of resistance of the control system to uncertainties and perturbation efects. Trough simulations and compassion with conventional regulators, the better accuracy of the suggested approach is demonstrated.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Journal of Mathematics"},"translated_abstract":"Te control and energy management problems of microgrids (MGs) are challenging due to the high level of uncertainties and disturbances such as changes in demands, mechanical powers, and solar energies. So, intelligent computing is needed to be developed for these systems. Tis paper uses an optimal and robust fuzzy controller for automatic voltage and frequency regulation. Te fuzzy logic develops the resistance against uncertainties and disturbances such as irradiation, wind power changes, and load demand variation. Te introduced controller uses appropriate and efective criteria that include rising time, settling time, overshoot, and the degree of resistance of the control system to uncertainties and perturbation efects. Trough simulations and compassion with conventional regulators, the better accuracy of the suggested approach is demonstrated.","internal_url":"https://www.academia.edu/101951552/A_Fuzzy_Intelligent_Computing_Approach_for_Energy_Voltage_Control_of_Microgrids","translated_internal_url":"","created_at":"2023-05-17T20:30:41.877-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":39875411,"work_id":101951552,"tagging_user_id":29298824,"tagged_user_id":44106890,"co_author_invite_id":null,"email":"m***9@gmail.com","display_order":1,"name":"Mohammed Salman","title":"A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids"}],"downloadable_attachments":[{"id":102348834,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348834/thumbnails/1.jpg","file_name":"1.pdf","download_url":"https://www.academia.edu/attachments/102348834/download_file","bulk_download_file_name":"A_Fuzzy_Intelligent_Computing_Approach_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348834/1-libre.pdf?1684382026=\u0026response-content-disposition=attachment%3B+filename%3DA_Fuzzy_Intelligent_Computing_Approach_f.pdf\u0026Expires=1738795242\u0026Signature=W1WcVdMotu2~ACaLnKQBo61TSO2ODJLZrchbRezzlBVUMT9DS0nNdaEeV64OEBSCxDzqTMpk20LK6f8jQOBIGLSA9LiDJqrv7GWnjJ9ajM2Rh18lm8FMoMvkN27J9saXYie~-V7gzonEoJ1hhK7cqq50NJdI5jd3~8ULPXnU4TwvCpiixIkV4ygBYwWqoUWP8yl5rVQjTtpWhdGJYa4Y3dXRgtSutqM5ebuYo3G2-~VbuWtCdJI5ntgn9~Wd-KjGHnR8ahQ0YHHuN6wxlxSDq11MRlCWvA7wt6s8x44MiPIIhCBGJv-tYDsjDg~zeC0bKc18ChR9Uuevc2dCwL0K2A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Fuzzy_Intelligent_Computing_Approach_for_Energy_Voltage_Control_of_Microgrids","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"Te control and energy management problems of microgrids (MGs) are challenging due to the high level of uncertainties and disturbances such as changes in demands, mechanical powers, and solar energies. So, intelligent computing is needed to be developed for these systems. Tis paper uses an optimal and robust fuzzy controller for automatic voltage and frequency regulation. Te fuzzy logic develops the resistance against uncertainties and disturbances such as irradiation, wind power changes, and load demand variation. Te introduced controller uses appropriate and efective criteria that include rising time, settling time, overshoot, and the degree of resistance of the control system to uncertainties and perturbation efects. Trough simulations and compassion with conventional regulators, the better accuracy of the suggested approach is demonstrated.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":102348834,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348834/thumbnails/1.jpg","file_name":"1.pdf","download_url":"https://www.academia.edu/attachments/102348834/download_file","bulk_download_file_name":"A_Fuzzy_Intelligent_Computing_Approach_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348834/1-libre.pdf?1684382026=\u0026response-content-disposition=attachment%3B+filename%3DA_Fuzzy_Intelligent_Computing_Approach_f.pdf\u0026Expires=1738795242\u0026Signature=W1WcVdMotu2~ACaLnKQBo61TSO2ODJLZrchbRezzlBVUMT9DS0nNdaEeV64OEBSCxDzqTMpk20LK6f8jQOBIGLSA9LiDJqrv7GWnjJ9ajM2Rh18lm8FMoMvkN27J9saXYie~-V7gzonEoJ1hhK7cqq50NJdI5jd3~8ULPXnU4TwvCpiixIkV4ygBYwWqoUWP8yl5rVQjTtpWhdGJYa4Y3dXRgtSutqM5ebuYo3G2-~VbuWtCdJI5ntgn9~Wd-KjGHnR8ahQ0YHHuN6wxlxSDq11MRlCWvA7wt6s8x44MiPIIhCBGJv-tYDsjDg~zeC0bKc18ChR9Uuevc2dCwL0K2A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":5412,"name":"Energy","url":"https://www.academia.edu/Documents/in/Energy"},{"id":31812,"name":"Fuzzy Control","url":"https://www.academia.edu/Documents/in/Fuzzy_Control"},{"id":470532,"name":"Microgrids","url":"https://www.academia.edu/Documents/in/Microgrids"}],"urls":[{"id":31534651,"url":"https://www.hindawi.com/journals/jmath/2023/5289114/"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="99950188"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/99950188/OPTIMIZING_TRANSPORTATION_COST_FOR_BIOMASS_SUPPLY_CHAIN"><img alt="Research paper thumbnail of OPTIMIZING TRANSPORTATION COST FOR BIOMASS SUPPLY CHAIN" class="work-thumbnail" src="https://attachments.academia-assets.com/100903321/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/99950188/OPTIMIZING_TRANSPORTATION_COST_FOR_BIOMASS_SUPPLY_CHAIN">OPTIMIZING TRANSPORTATION COST FOR BIOMASS SUPPLY CHAIN</a></div><div class="wp-workCard_item"><span>Thermal Science </span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Biomass conversion is largely impacted by the cost of transporting biomass materials. As a result...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Biomass conversion is largely impacted by the cost of transporting biomass materials. As a result, businesses need optimization solutions to optimize their transport operations, allocate resources effectively, and reduce their environmental impact. As part of the process of biomass conversion, this paper discusses the transport and biomass optimization problem in detail. The paper presents optimization of transportation cost of two biomass products, natural gas, and bio fuel during the process of biomass conversion final products depending on the transport routes and other factors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5d9a03840e0f3ceeb7b1fe4c30f961d4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100903321,"asset_id":99950188,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100903321/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="99950188"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="99950188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 99950188; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="99950041"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/99950041/Conormal_Product_for_Intutionistic_Anti_Fuzzy_Graphs"><img alt="Research paper thumbnail of Conormal Product for Intutionistic Anti-Fuzzy Graphs" class="work-thumbnail" src="https://attachments.academia-assets.com/100903206/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/99950041/Conormal_Product_for_Intutionistic_Anti_Fuzzy_Graphs">Conormal Product for Intutionistic Anti-Fuzzy Graphs</a></div><div class="wp-workCard_item"><span>International Journal of Fuzzy Logic and Intelligent Systems</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study introduces and analyzes the conormal product of intuitionistic anti-fuzzy graphs (IAFG...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study introduces and analyzes the conormal product of intuitionistic anti-fuzzy graphs (IAFGs) and analyzes certain fundamental theorems and applications. Further, new notions on complete and regular IAFGs were introduced, and the conormal product operation was applied to these IAFGs. We showed that the conormal product of two IAFGs could be used and analyzed important results showing that the conormal product of complete, regular, and strong IAFGs is an IAFG.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d8153cb6f1060ba6f2072043315e6ffc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100903206,"asset_id":99950041,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100903206/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="99950041"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="99950041"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 99950041; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=99950041]").text(description); $(".js-view-count[data-work-id=99950041]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 99950041; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='99950041']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98878289"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98878289/Construction_of_Nilpotent_and_Solvable_Lie_Algebra_in_Picture_Fuzzy_Environment"><img alt="Research paper thumbnail of Construction of Nilpotent and Solvable Lie Algebra in Picture Fuzzy Environment" class="work-thumbnail" src="https://attachments.academia-assets.com/100111787/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98878289/Construction_of_Nilpotent_and_Solvable_Lie_Algebra_in_Picture_Fuzzy_Environment">Construction of Nilpotent and Solvable Lie Algebra in Picture Fuzzy Environment</a></div><div class="wp-workCard_item"><span>International Journal of Computational Intelligence Systems</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The picture fuzzy set was introduced by Coung. It is a generalization of the intuitionistic fuzzy...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The picture fuzzy set was introduced by Coung. It is a generalization of the intuitionistic fuzzy set, giving the notion of neutral membership degrees along with the positive and negative ones. Lie groups and Lie algebras have become indispensable for a lot of fields in mathematical and intellectual physics. In 1872, Lie began his work in the field of continuous transformation groups, later named after him as Lie groups. These have become a fundamental body of interest in themselves. In this paper, the authors presented the notion of the picture fuzzy Lie algebra, picture fuzzy Lie sub-algebra, ideal, and homomorphism. Derived and lower central series of picture fuzzy Lie ideals are constructed to define and analyse solvable and nilpotent picture fuzzy Lie ideals.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c72f3533594dff1eefced1a831bf8e2d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100111787,"asset_id":98878289,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100111787/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98878289"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98878289"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98878289; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98878289]").text(description); $(".js-view-count[data-work-id=98878289]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98878289; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98878289']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c72f3533594dff1eefced1a831bf8e2d" } } $('.js-work-strip[data-work-id=98878289]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":98878289,"title":"Construction of Nilpotent and Solvable Lie Algebra in Picture Fuzzy Environment","internal_url":"https://www.academia.edu/98878289/Construction_of_Nilpotent_and_Solvable_Lie_Algebra_in_Picture_Fuzzy_Environment","owner_id":29298824,"coauthors_can_edit":true,"owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":100111787,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100111787/thumbnails/1.jpg","file_name":"DOC_20230321_WA0023..pdf","download_url":"https://www.academia.edu/attachments/100111787/download_file","bulk_download_file_name":"Construction_of_Nilpotent_and_Solvable_L.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100111787/DOC_20230321_WA0023.-libre.pdf?1679382519=\u0026response-content-disposition=attachment%3B+filename%3DConstruction_of_Nilpotent_and_Solvable_L.pdf\u0026Expires=1740167869\u0026Signature=Rfm~~C~R7m1tMN8W~rkg9dK1p5nU09XgLgqbQCwwXSl-YMeOkl9YSxOTy6qzm4e2bubj0yTlnuVSjeiy8hFc3F2-is1XieSLNnp4fhgABtT0Owyt6JThxCYrkABcCjocIRBMZQCbypydlkm2Ga9nYmYcB-g246vhlO1mPTDIfz1s2wbRyQWUh3y~-~g6Ceiw-pfLeFFLjAuKIV6RMa27eMxWpZm74lLl8ioIDOrSLtinap67pkx4NksB387O~9uTo~UcIzXbawtVeHl8jCTEzYhDoKpVUaVmFSncC3Ou7d8f1f2VkRr1euNNsF-~xWKnS1Qxo9iK086wdQ~KJU-ixA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98875094"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98875094/A_Non_singleton_Type_3_Fuzzy_Modeling_Optimized_by_Square_Root_Cubature_Kalman_Filter"><img alt="Research paper thumbnail of A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter" class="work-thumbnail" src="https://attachments.academia-assets.com/100109215/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98875094/A_Non_singleton_Type_3_Fuzzy_Modeling_Optimized_by_Square_Root_Cubature_Kalman_Filter">A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter</a></div><div class="wp-workCard_item"><span>Intelligent Automation & Soft Computing</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In many problems, to analyze the process/metabolism behavior, a model of the system is identified...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. The main advantages of the suggested approach include better resistance against uncertainties, deep learning, and good convergence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9215d2cfa9f498f9dd3f77c53d86124f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100109215,"asset_id":98875094,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100109215/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98875094"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98875094"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98875094; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98875094]").text(description); $(".js-view-count[data-work-id=98875094]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98875094; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98875094']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9215d2cfa9f498f9dd3f77c53d86124f" } } $('.js-work-strip[data-work-id=98875094]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":98875094,"title":"A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter","translated_title":"","metadata":{"doi":"10.32604/iasc.2023.036623","abstract":"In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. The main advantages of the suggested approach include better resistance against uncertainties, deep learning, and good convergence.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Intelligent Automation \u0026 Soft Computing"},"translated_abstract":"In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. The main advantages of the suggested approach include better resistance against uncertainties, deep learning, and good convergence.","internal_url":"https://www.academia.edu/98875094/A_Non_singleton_Type_3_Fuzzy_Modeling_Optimized_by_Square_Root_Cubature_Kalman_Filter","translated_internal_url":"","created_at":"2023-03-20T21:04:17.326-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":100109215,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100109215/thumbnails/1.jpg","file_name":"20230321113055_88549.pdf","download_url":"https://www.academia.edu/attachments/100109215/download_file","bulk_download_file_name":"A_Non_singleton_Type_3_Fuzzy_Modeling_Op.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100109215/20230321113055_88549.pdf?1679371453=\u0026response-content-disposition=attachment%3B+filename%3DA_Non_singleton_Type_3_Fuzzy_Modeling_Op.pdf\u0026Expires=1738795242\u0026Signature=Qfk6uK-404KHu30GKw3v2rZ8CIDFuF6x9ksvv1myZ4PpBZ3FZGr60KE5oPwY3QCLNwYRIIM4Hm5eeyQCaD8LOC3RWUzOwJkOTvVqJM5OAFiMXEd~TUR2ZtjorS9fFmaptC1Sg0lUqB2SzvfGLOrJBaZ4~~wQ2YAgh4GPZBbv5-ZObA7FExNxiv445Uh8Hp3Tvcv3ecwu6~28vc3tWJwTvcxO3EUN1dseb2Cd8JuN6Nt0Ht5bSxT3-nc5K-7RL2iu6DIATNztdugoFfqDFxX763j8IY0vBKw198dYS0xcGWQeue8JWDVOpHUNtv5~RQHzIcYZpwCibCUJxTaiEkjDtQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Non_singleton_Type_3_Fuzzy_Modeling_Optimized_by_Square_Root_Cubature_Kalman_Filter","translated_slug":"","page_count":16,"language":"en","content_type":"Work","summary":"In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. The main advantages of the suggested approach include better resistance against uncertainties, deep learning, and good convergence.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":100109215,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100109215/thumbnails/1.jpg","file_name":"20230321113055_88549.pdf","download_url":"https://www.academia.edu/attachments/100109215/download_file","bulk_download_file_name":"A_Non_singleton_Type_3_Fuzzy_Modeling_Op.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100109215/20230321113055_88549.pdf?1679371453=\u0026response-content-disposition=attachment%3B+filename%3DA_Non_singleton_Type_3_Fuzzy_Modeling_Op.pdf\u0026Expires=1738795242\u0026Signature=Qfk6uK-404KHu30GKw3v2rZ8CIDFuF6x9ksvv1myZ4PpBZ3FZGr60KE5oPwY3QCLNwYRIIM4Hm5eeyQCaD8LOC3RWUzOwJkOTvVqJM5OAFiMXEd~TUR2ZtjorS9fFmaptC1Sg0lUqB2SzvfGLOrJBaZ4~~wQ2YAgh4GPZBbv5-ZObA7FExNxiv445Uh8Hp3Tvcv3ecwu6~28vc3tWJwTvcxO3EUN1dseb2Cd8JuN6Nt0Ht5bSxT3-nc5K-7RL2iu6DIATNztdugoFfqDFxX763j8IY0vBKw198dYS0xcGWQeue8JWDVOpHUNtv5~RQHzIcYZpwCibCUJxTaiEkjDtQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1023349,"name":"Fuzzy Image Modeling","url":"https://www.academia.edu/Documents/in/Fuzzy_Image_Modeling"},{"id":1695266,"name":"Cubature Kalman Filter","url":"https://www.academia.edu/Documents/in/Cubature_Kalman_Filter"},{"id":2915851,"name":"square root","url":"https://www.academia.edu/Documents/in/square_root"}],"urls":[{"id":29961118,"url":"https://www.techscience.com/iasc/online/detail/19104"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98874489"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98874489/An_Integrated_Fuzzy_Structured_Methodology_for_Performance_Evaluation_of_High_Schools_in_a_Group_Decision_Making_Problem"><img alt="Research paper thumbnail of An Integrated Fuzzy Structured Methodology for Performance Evaluation of High Schools in a Group Decision-Making Problem" class="work-thumbnail" src="https://attachments.academia-assets.com/100108709/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98874489/An_Integrated_Fuzzy_Structured_Methodology_for_Performance_Evaluation_of_High_Schools_in_a_Group_Decision_Making_Problem">An Integrated Fuzzy Structured Methodology for Performance Evaluation of High Schools in a Group Decision-Making Problem</a></div><div class="wp-workCard_item"><span>Systems</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Evaluating and ranking schools are noteworthy for parents of students and upstream institutions (...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Evaluating and ranking schools are noteworthy for parents of students and upstream institutions (in Iran, the Ministry of Education). In this process, quantitative criteria, including educational activities, human resources, space and equipment, and administrative-financial indicators, are commonly investigated. This process is carried out only by the upstream institutions and the view of the system from the perspective of another stakeholder, namely, the students’ parents, are ignored and qualitative-judgmental indicators do not involve the school evaluation results. Consequently, in this study, we used the opinions of five parents of students and five experienced school administrators to capture the perspectives of both key system stakeholders. In addition, to perform a more comprehensive analysis, we added three qualitative criteria that are less noticed within the problem (social environment, health, and students), along with their sub-criteria to the criteria obtained from the research background. We eliminated the less influential sub-criteria using the Delphi technique and continued the study with 10 criteria and 53 sub-criteria. Then, using two widely used methods in this field, AHP and TOPSIS, we determined the weight of the sub-criteria and the ranking based on the experts’ views. In addition, to deal with the ambiguity in experts’ judgments, we transformed the crisp data into fuzzy data. We applied the proposed methodology to rank 15 schools in Tehran, Iran. The results showed that the proposed quantitative criteria significantly impact the schools ranking. In addition, according to the sensitivity analysis results, it was found that ignoring the views of the system from another stakeholder can distort the results. Finally, directions for future research were suggested based on current research limitations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bfc3b7f53540eb41ec93be3c0f21bf8a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100108709,"asset_id":98874489,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100108709/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98874489"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98874489"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98874489; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98874489]").text(description); $(".js-view-count[data-work-id=98874489]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98874489; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98874489']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98539588"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98539588/New_applications_of_various_distance_techniques_to_multi_criteria_decision_making_challenges_for_ranking_vague_sets"><img alt="Research paper thumbnail of New applications of various distance techniques to multi-criteria decision-making challenges for ranking vague sets" class="work-thumbnail" src="https://attachments.academia-assets.com/99862111/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98539588/New_applications_of_various_distance_techniques_to_multi_criteria_decision_making_challenges_for_ranking_vague_sets">New applications of various distance techniques to multi-criteria decision-making challenges for ranking vague sets</a></div><div class="wp-workCard_item"><span>Aims Mathematics</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Using the Fermatean vague normal set (FVNS), problems requiring multiple attribute decision makin...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Using the Fermatean vague normal set (FVNS), problems requiring multiple attribute decision making (MADM) have been resolved in this article. This article focuses on the log Fermatean vague normal weighted averaging (log FVNWA), logarithmic Fermatean vague normal weighted geometric (log FVNWG), log generalized Fermatean vague normal weighted averaging (log GFVNWA) and log generalized Fermatean vague normal weighted geometric (log GFVNWG) operators. Described the scoring function, accuracy function and operational laws of the log FVNS. The Euclidean and Humming distance are extended with numerical examples. The features of the log FVNS based on the algebraic operations, including idempotency, boundedness, commutativity and monotonicity are also examined. A field of applied engineering called agricultural robotics has been compared to computer science and machine tool technology. Five distinct agricultural robotics including autonomous mobile robots, articulated robots, humanoid robots, cobot robots, and hybrid robots are randomly chosen. Findings can be compared to established criteria to determine which robotics are the most successful. The results of the models are expressed as a natural number α. We contrast several existing with those that have been developed in order to show the effectiveness and accuracy of the models.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1211bbd382efa8bba1546cd087c7bd80" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":99862111,"asset_id":98539588,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/99862111/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98539588"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98539588"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98539588; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="10126802" id="papers"><div class="js-work-strip profile--work_container" data-work-id="109368812"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109368812/A_Novel_Method_for_Determining_Tourism_Carrying_Capacity_in_a_Decision_Making_Context_Using_q_Rung_Orthopair_Fuzzy_Hypersoft_Environment"><img alt="Research paper thumbnail of A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment" class="work-thumbnail" src="https://attachments.academia-assets.com/107515302/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109368812/A_Novel_Method_for_Determining_Tourism_Carrying_Capacity_in_a_Decision_Making_Context_Using_q_Rung_Orthopair_Fuzzy_Hypersoft_Environment">A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment</a></div><div class="wp-workCard_item"><span>Computer Modeling in Engineering and Sciences</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tourism is a popular activity that allows individuals to escape their daily routines and explore ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (q−ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. With the advanced mathematical tools in this field, the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to support multiattribute decision-making algorithms. By implementing this technique, effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism. A case study of selected tourism carrying capacity will demonstrate the proposed methodology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="90948602bbb14cf46b73e05df0e77beb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515302,"asset_id":109368812,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515302/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368812"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368812"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368812; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368812]").text(description); $(".js-view-count[data-work-id=109368812]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368812; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368812']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "90948602bbb14cf46b73e05df0e77beb" } } $('.js-work-strip[data-work-id=109368812]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368812,"title":"A Novel Method for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment","translated_title":"","metadata":{"doi":"10.32604/cmes.2023.030896","issue":"2","volume":"138","abstract":"Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. 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A case study of selected tourism carrying capacity will demonstrate the proposed methodology.","ai_title_tag":"Assessing Tourism Carrying Capacity Using Fuzzy Hypersoft Sets","page_numbers":"1951-1979","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Computer Modeling in Engineering and Sciences"},"translated_abstract":"Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons, including leisure, pleasure, or business. A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set (q−ROFHS) to enhance the formal representation of human thought processes and evaluate tourism carrying capacity. This approach can capture the imprecision and ambiguity often present in human perception. 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Singh</a></span></div><div class="wp-workCard_item"><span>Computational Journal of Mathematical and Statistical Sciences</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Three parameters Birnbaum-Saunders, BS (3P), distribution is an important statistical model which...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Three parameters Birnbaum-Saunders, BS (3P), distribution is an important statistical model which is useable in the fields of both pure and applied sciences. Since its inception, Birnbaum-Saunders (BS) distribution has received a considerable attention in view of its wide applications in many areas of research in applied sciences, such as engineering science, earth science, environmental science, medical science, material fatigue and reliability studies. The objective of this paper is a statistical analysis of the three parameter Birnbaum-Saunders, BS (3P), distribution and to draw some inferences on it. Several new distributional properties of this distribution have been discussed. Based on these distributional properties, we have established several new characterization results of the three-parameter Birnbaum-Saunders distribution. Finally, applications to some real-life data sets are analyzed to show the usefulness of this distribution. The results of this article will be useful for the researchers, scientists, and statisticians in fields of theoretical and applied sciences.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f98fe42a63b712e90021fc0681e91d16" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515220,"asset_id":109368689,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515220/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368689"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368689"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368689; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368689]").text(description); $(".js-view-count[data-work-id=109368689]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368689; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368689']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f98fe42a63b712e90021fc0681e91d16" } } $('.js-work-strip[data-work-id=109368689]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368689,"title":"Some Inferences on Three Parameters Birnbaum-Saunders Distribution: Statistical Properties, Characterizations and Applications","translated_title":"","metadata":{"doi":"10.21608/CJMSS.2023.224583.1011","issue":"2","volume":"2","abstract":"Three parameters Birnbaum-Saunders, BS (3P), distribution is an important statistical model which is useable in the fields of both pure and applied sciences. Since its inception, Birnbaum-Saunders (BS) distribution has received a considerable attention in view of its wide applications in many areas of research in applied sciences, such as engineering science, earth science, environmental science, medical science, material fatigue and reliability studies. The objective of this paper is a statistical analysis of the three parameter Birnbaum-Saunders, BS (3P), distribution and to draw some inferences on it. Several new distributional properties of this distribution have been discussed. Based on these distributional properties, we have established several new characterization results of the three-parameter Birnbaum-Saunders distribution. Finally, applications to some real-life data sets are analyzed to show the usefulness of this distribution. The results of this article will be useful for the researchers, scientists, and statisticians in fields of theoretical and applied sciences.","ai_title_tag":"Statistical Properties of 3P Birnbaum-Saunders","page_numbers":"197-222","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Computational Journal of Mathematical and Statistical Sciences"},"translated_abstract":"Three parameters Birnbaum-Saunders, BS (3P), distribution is an important statistical model which is useable in the fields of both pure and applied sciences. Since its inception, Birnbaum-Saunders (BS) distribution has received a considerable attention in view of its wide applications in many areas of research in applied sciences, such as engineering science, earth science, environmental science, medical science, material fatigue and reliability studies. The objective of this paper is a statistical analysis of the three parameter Birnbaum-Saunders, BS (3P), distribution and to draw some inferences on it. Several new distributional properties of this distribution have been discussed. Based on these distributional properties, we have established several new characterization results of the three-parameter Birnbaum-Saunders distribution. Finally, applications to some real-life data sets are analyzed to show the usefulness of this distribution. The results of this article will be useful for the researchers, scientists, and statisticians in fields of theoretical and applied sciences.","internal_url":"https://www.academia.edu/109368689/Some_Inferences_on_Three_Parameters_Birnbaum_Saunders_Distribution_Statistical_Properties_Characterizations_and_Applications","translated_internal_url":"","created_at":"2023-11-18T22:40:32.650-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":40553728,"work_id":109368689,"tagging_user_id":29298824,"tagged_user_id":29401889,"co_author_invite_id":null,"email":"m***l@mdc.edu","display_order":1,"name":"M. Shakil","title":"Some Inferences on Three Parameters Birnbaum-Saunders Distribution: Statistical Properties, Characterizations and Applications"},{"id":40553729,"work_id":109368689,"tagging_user_id":29298824,"tagged_user_id":152651212,"co_author_invite_id":null,"email":"d***r@gpgc-atd.edu.pk","display_order":2,"name":"Mohammad Munir","title":"Some Inferences on Three Parameters Birnbaum-Saunders Distribution: Statistical Properties, Characterizations and Applications"},{"id":40553730,"work_id":109368689,"tagging_user_id":29298824,"tagged_user_id":294592419,"co_author_invite_id":7957086,"email":"j***h@barry.edu","display_order":3,"name":"J. Singh","title":"Some Inferences on Three Parameters Birnbaum-Saunders Distribution: Statistical Properties, Characterizations and Applications"}],"downloadable_attachments":[{"id":107515220,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515220/thumbnails/1.jpg","file_name":"2.pdf","download_url":"https://www.academia.edu/attachments/107515220/download_file","bulk_download_file_name":"Some_Inferences_on_Three_Parameters_Birn.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515220/2-libre.pdf?1700377386=\u0026response-content-disposition=attachment%3B+filename%3DSome_Inferences_on_Three_Parameters_Birn.pdf\u0026Expires=1738795241\u0026Signature=E~eRWh3CK4kErIOx0whV1zl7qDJQyz6RvbWAFh1ts9AdeqF16ytBYeu8oiT0Wb7ZDtkemC2QojzYjKVzQqY2unY84lkScH8hyd69Xr0KbACnmf-RnWFwI5U4eiwYSIgtHnbWualXXJaC~hXMVvVB~pONTcvI~J3WYDdkkO1DjNnJ0lJwYdWyLMAkBf67lNjmtu96KImN-7u059Kxb6~3I6WOp9XPmVfmNoJPnAZc5FW9ItKKMAV-SH5tsLKDl76bnMRzlpwuc5j4OioPXDggUEWMUb46q2Vz9QhRdZ8W3VWBVwfT4uL8uEB2d0Qp5ApXIeSqGvBBgbIJwCXbm1plmg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Some_Inferences_on_Three_Parameters_Birnbaum_Saunders_Distribution_Statistical_Properties_Characterizations_and_Applications","translated_slug":"","page_count":26,"language":"en","content_type":"Work","summary":"Three parameters Birnbaum-Saunders, BS (3P), distribution is an important statistical model which is useable in the fields of both pure and applied sciences. Since its inception, Birnbaum-Saunders (BS) distribution has received a considerable attention in view of its wide applications in many areas of research in applied sciences, such as engineering science, earth science, environmental science, medical science, material fatigue and reliability studies. The objective of this paper is a statistical analysis of the three parameter Birnbaum-Saunders, BS (3P), distribution and to draw some inferences on it. Several new distributional properties of this distribution have been discussed. Based on these distributional properties, we have established several new characterization results of the three-parameter Birnbaum-Saunders distribution. Finally, applications to some real-life data sets are analyzed to show the usefulness of this distribution. 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This problem has been approached in a number of ways, including use of the sequential Newton's method and the traditional Weierstrass simultaneous iterative scheme. To approximate all of the roots of a given nonlinear equation, sequential iterative algorithms must use a deflation strategy because rounding errors can produce inaccurate results. This study aims to develop an efficient numerical simultaneous scheme for approximating all nonlinear equations' roots of convergence order 12. The numerical outcomes of the considered engineering problems show that, in terms of accuracy, validations, error, computational CPU time, and residual error, recently developed simultaneous methods perform better than existing methods in the literature.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5e52ccb568db4959a965ef1537379288" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515193,"asset_id":109368633,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515193/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368633"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368633"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368633; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368633]").text(description); $(".js-view-count[data-work-id=109368633]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368633; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368633']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5e52ccb568db4959a965ef1537379288" } } $('.js-work-strip[data-work-id=109368633]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368633,"title":"Numerical scheme for estimating all roots of non-linear equations with applications","translated_title":"","metadata":{"doi":"10.3934/math.20231200","issue":"10","volume":"8","abstract":"The roots of non-linear equations are a major challenge in many scientific and professional fields. This problem has been approached in a number of ways, including use of the sequential Newton's method and the traditional Weierstrass simultaneous iterative scheme. To approximate all of the roots of a given nonlinear equation, sequential iterative algorithms must use a deflation strategy because rounding errors can produce inaccurate results. This study aims to develop an efficient numerical simultaneous scheme for approximating all nonlinear equations' roots of convergence order 12. The numerical outcomes of the considered engineering problems show that, in terms of accuracy, validations, error, computational CPU time, and residual error, recently developed simultaneous methods perform better than existing methods in the literature.","page_numbers":"23603-23620","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"AIMS Mathematics"},"translated_abstract":"The roots of non-linear equations are a major challenge in many scientific and professional fields. This problem has been approached in a number of ways, including use of the sequential Newton's method and the traditional Weierstrass simultaneous iterative scheme. To approximate all of the roots of a given nonlinear equation, sequential iterative algorithms must use a deflation strategy because rounding errors can produce inaccurate results. 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The numerical outcomes of the considered engineering problems show that, in terms of accuracy, validations, error, computational CPU time, and residual error, recently developed simultaneous methods perform better than existing methods in the literature.","internal_url":"https://www.academia.edu/109368633/Numerical_scheme_for_estimating_all_roots_of_non_linear_equations_with_applications","translated_internal_url":"","created_at":"2023-11-18T22:37:23.783-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":40553715,"work_id":109368633,"tagging_user_id":29298824,"tagged_user_id":252057293,"co_author_invite_id":null,"email":"g***o@yahoo.co.uk","display_order":1,"name":"Georgia Oros","title":"Numerical scheme for estimating all roots of non-linear equations with applications"},{"id":40553716,"work_id":109368633,"tagging_user_id":29298824,"tagged_user_id":null,"co_author_invite_id":47387,"email":"s***i@hku.edu.tr","display_order":2,"name":"Serkan Araci","title":"Numerical scheme for estimating all roots of non-linear equations with applications"}],"downloadable_attachments":[{"id":107515193,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515193/thumbnails/1.jpg","file_name":"3.pdf","download_url":"https://www.academia.edu/attachments/107515193/download_file","bulk_download_file_name":"Numerical_scheme_for_estimating_all_root.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515193/3-libre.pdf?1700377384=\u0026response-content-disposition=attachment%3B+filename%3DNumerical_scheme_for_estimating_all_root.pdf\u0026Expires=1738795241\u0026Signature=dSXzP2ABMGVeGIchOF~oPXmRIiZeFNpT4HKnTNPPHTbvgHpTC81cMzVlCo3cKzvHP8qPkzx5JQbI4BOgO0KYhMuQL6CF4EKquT4KjSkGdHFvYf~0RT1sfiS88-QOlnjX6iSnHH47ffDG2x~640kD~aQjU7iKTVMNrBBS16zwefSYACRvLnRrrFQ8M1uwlGC-Dyj3svMqAstjE75SihjBevxaAetL1jcUnkVfqUZTwHG979~7o9DrdGyiWiBWQ4V2kGtMokZqdWDtmd1qOz36o0~gI6mI7gY94yFlIfu6pzsKxpswZkhR3mc74NOnt~4dG4lBr0zUxykohV9hzTNFtw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Numerical_scheme_for_estimating_all_roots_of_non_linear_equations_with_applications","translated_slug":"","page_count":18,"language":"en","content_type":"Work","summary":"The roots of non-linear equations are a major challenge in many scientific and professional fields. 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Our results are extension or generalisation of results proved in the literature. The derived results are substantiated with suitable example and an application.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="788fe4aace7c247a0f92c61aefd4c571" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515113,"asset_id":109368535,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515113/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368535"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368535"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368535; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109368358"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109368358/A_Stable_Fuzzy_Based_Computational_Model_and_Control_for_Inductions_Motors"><img alt="Research paper thumbnail of A Stable Fuzzy-Based Computational Model and Control for Inductions Motors" class="work-thumbnail" src="https://attachments.academia-assets.com/107515044/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109368358/A_Stable_Fuzzy_Based_Computational_Model_and_Control_for_Inductions_Motors">A Stable Fuzzy-Based Computational Model and Control for Inductions Motors</a></div><div class="wp-workCard_item"><span>Computer Modeling in Engineering and Sciences</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is in...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99842c665c6390495756f550a770771a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107515044,"asset_id":109368358,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107515044/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368358"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368358"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368358; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368358]").text(description); $(".js-view-count[data-work-id=109368358]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368358; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368358']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "99842c665c6390495756f550a770771a" } } $('.js-work-strip[data-work-id=109368358]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368358,"title":"A Stable Fuzzy-Based Computational Model and Control for Inductions Motors","translated_title":"","metadata":{"doi":"10.32604/cmes.2023.028175","issue":"1","volume":"138","abstract":"In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.","page_numbers":" 793-812","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Computer Modeling in Engineering and Sciences"},"translated_abstract":"In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.","internal_url":"https://www.academia.edu/109368358/A_Stable_Fuzzy_Based_Computational_Model_and_Control_for_Inductions_Motors","translated_internal_url":"","created_at":"2023-11-18T22:28:11.619-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107515044,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515044/thumbnails/1.jpg","file_name":"5.pdf","download_url":"https://www.academia.edu/attachments/107515044/download_file","bulk_download_file_name":"A_Stable_Fuzzy_Based_Computational_Model.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515044/5-libre.pdf?1700377426=\u0026response-content-disposition=attachment%3B+filename%3DA_Stable_Fuzzy_Based_Computational_Model.pdf\u0026Expires=1738795241\u0026Signature=NURfPnokv01A1xSGdgeEO~CBCjZ3-XWh18U9wUNMjaNRQKErzewccikTZTene0GA5Qep~3V5tbccLjnej0FrT-~hnXvdQ4SC0aru58-szzpDxD8DM1OzOHCGp0gmQe~vidWbYZtDsLyVYRpotU5ZNAEaKqm9mTcpi7avewFhjX94z~yFgDuXH6amSx0fZKtpcLirqDA9sOzJnrYgaRG8j7sndCwk7jfhGfEv-lPtIEzaIP4ImA-AbbbRA7R1kUuIUta79YvElQkC5zPOVFy-Krshi6VdT4597f5B4QD-XeyHbn8btxHbUHtCjumnCLXj8rw~KOxTWHawyo1tf5rnKQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Stable_Fuzzy_Based_Computational_Model_and_Control_for_Inductions_Motors","translated_slug":"","page_count":20,"language":"en","content_type":"Work","summary":"In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":107515044,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107515044/thumbnails/1.jpg","file_name":"5.pdf","download_url":"https://www.academia.edu/attachments/107515044/download_file","bulk_download_file_name":"A_Stable_Fuzzy_Based_Computational_Model.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107515044/5-libre.pdf?1700377426=\u0026response-content-disposition=attachment%3B+filename%3DA_Stable_Fuzzy_Based_Computational_Model.pdf\u0026Expires=1738795241\u0026Signature=NURfPnokv01A1xSGdgeEO~CBCjZ3-XWh18U9wUNMjaNRQKErzewccikTZTene0GA5Qep~3V5tbccLjnej0FrT-~hnXvdQ4SC0aru58-szzpDxD8DM1OzOHCGp0gmQe~vidWbYZtDsLyVYRpotU5ZNAEaKqm9mTcpi7avewFhjX94z~yFgDuXH6amSx0fZKtpcLirqDA9sOzJnrYgaRG8j7sndCwk7jfhGfEv-lPtIEzaIP4ImA-AbbbRA7R1kUuIUta79YvElQkC5zPOVFy-Krshi6VdT4597f5B4QD-XeyHbn8btxHbUHtCjumnCLXj8rw~KOxTWHawyo1tf5rnKQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1621284,"name":"Mathematical Engineering","url":"https://www.academia.edu/Documents/in/Mathematical_Engineering"}],"urls":[{"id":35527468,"url":"https://www.techscience.com/CMES/v138n1/54264"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109368140"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109368140/Robotic_sensor_based_on_score_and_accuracy_values_in_q_rung_complex_diophatine_neutrosophic_normal_set_with_an_aggregation_operation"><img alt="Research paper thumbnail of Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation" class="work-thumbnail" src="https://attachments.academia-assets.com/107514868/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109368140/Robotic_sensor_based_on_score_and_accuracy_values_in_q_rung_complex_diophatine_neutrosophic_normal_set_with_an_aggregation_operation">Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation</a></div><div class="wp-workCard_item"><span>Alexandria Engineering Journal </span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex dioph...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (qrung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="916072114d332638ac930184b230ae1a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107514868,"asset_id":109368140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107514868/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109368140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109368140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109368140; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109368140]").text(description); $(".js-view-count[data-work-id=109368140]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109368140; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109368140']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "916072114d332638ac930184b230ae1a" } } $('.js-work-strip[data-work-id=109368140]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109368140,"title":"Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation","translated_title":"","metadata":{"doi":"10.1016/j.aej.2023.06.064","volume":"77","abstract":"The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (qrung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology.","ai_title_tag":"MADM Solutions with q-Rung CDNNS and Aggregation Operations","page_numbers":"149-164","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Alexandria Engineering Journal "},"translated_abstract":"The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (qrung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology.","internal_url":"https://www.academia.edu/109368140/Robotic_sensor_based_on_score_and_accuracy_values_in_q_rung_complex_diophatine_neutrosophic_normal_set_with_an_aggregation_operation","translated_internal_url":"","created_at":"2023-11-18T22:22:01.672-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":40553697,"work_id":109368140,"tagging_user_id":29298824,"tagged_user_id":null,"co_author_invite_id":7559360,"email":"h***g@thapar.edu","display_order":1,"name":"Harish Garg","title":"Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation"},{"id":40553698,"work_id":109368140,"tagging_user_id":29298824,"tagged_user_id":null,"co_author_invite_id":7957081,"email":"s***y@nor-off.no","display_order":2,"name":"Seifedine Kadry","title":"Robotic sensor based on score and accuracy values in q-rung complex diophatine neutrosophic normal set with an aggregation operation"}],"downloadable_attachments":[{"id":107514868,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514868/thumbnails/1.jpg","file_name":"6.pdf","download_url":"https://www.academia.edu/attachments/107514868/download_file","bulk_download_file_name":"Robotic_sensor_based_on_score_and_accura.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514868/6-libre.pdf?1700377470=\u0026response-content-disposition=attachment%3B+filename%3DRobotic_sensor_based_on_score_and_accura.pdf\u0026Expires=1738795241\u0026Signature=ERfZJlBLcCVZ7L9rbPLjB~fnpw5IK1NMFfBXzhil2u6oJ4P7gAUDrJWX5PbAPgHLf~g815FaLERfzBKgi~zdeESEVx5aUMEYFL7Sem-eGPeuhnAdQkkFFbVEw4tVNlqmwSu66wKXxwIaUOPs9n0oR-XsbXoY3woGKVoEj-W02c3heuoMee-iOWCX7UBxZkfo8AotLrg9g0pQ70-SnOGqRTmY0EMW31X5tJu0W8IunwG2~q2Nsg4JA9GBu0BO3tlfK4mTMocl5mw5JY-83EzDlfzqfXCXl65rnKz8b-bxjJlRg2MPXHu5df9eXac2OG0eO63ZtbaHhshJ9fRajRWDnw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robotic_sensor_based_on_score_and_accuracy_values_in_q_rung_complex_diophatine_neutrosophic_normal_set_with_an_aggregation_operation","translated_slug":"","page_count":16,"language":"en","content_type":"Work","summary":"The multiple-attribute decision-making (MADM) problem is resolved through the qrung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (qrung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":107514868,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514868/thumbnails/1.jpg","file_name":"6.pdf","download_url":"https://www.academia.edu/attachments/107514868/download_file","bulk_download_file_name":"Robotic_sensor_based_on_score_and_accura.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514868/6-libre.pdf?1700377470=\u0026response-content-disposition=attachment%3B+filename%3DRobotic_sensor_based_on_score_and_accura.pdf\u0026Expires=1738795241\u0026Signature=ERfZJlBLcCVZ7L9rbPLjB~fnpw5IK1NMFfBXzhil2u6oJ4P7gAUDrJWX5PbAPgHLf~g815FaLERfzBKgi~zdeESEVx5aUMEYFL7Sem-eGPeuhnAdQkkFFbVEw4tVNlqmwSu66wKXxwIaUOPs9n0oR-XsbXoY3woGKVoEj-W02c3heuoMee-iOWCX7UBxZkfo8AotLrg9g0pQ70-SnOGqRTmY0EMW31X5tJu0W8IunwG2~q2Nsg4JA9GBu0BO3tlfK4mTMocl5mw5JY-83EzDlfzqfXCXl65rnKz8b-bxjJlRg2MPXHu5df9eXac2OG0eO63ZtbaHhshJ9fRajRWDnw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109367977"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109367977/A_novel_approach_based_on_neutrosophic_Bonferroni_mean_operator_of_trapezoidal_and_triangular_neutrosophic_interval_environments_in_multi_attribute_group_decision_making"><img alt="Research paper thumbnail of A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making" class="work-thumbnail" src="https://attachments.academia-assets.com/107514747/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109367977/A_novel_approach_based_on_neutrosophic_Bonferroni_mean_operator_of_trapezoidal_and_triangular_neutrosophic_interval_environments_in_multi_attribute_group_decision_making">A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making</a></div><div class="wp-workCard_item"><span>Scientific Reports</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine seve...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy. Abbreviations NMAGDM Neutrosophic Multi-Attribute Group Decision Making DMs Decision makers TITENBM Trapezoidal and triangular neutrosophic Bonferroni mean TITRNWBM Trapezoidal and triangular neutrosophic weighted Bonferroni mean FMAGDM Fuzzy multi-attributes group decision-making OPEN 1</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5995d47a92abcf471de1020978c246e6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107514747,"asset_id":109367977,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107514747/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109367977"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109367977"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109367977; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109367977]").text(description); $(".js-view-count[data-work-id=109367977]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109367977; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109367977']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5995d47a92abcf471de1020978c246e6" } } $('.js-work-strip[data-work-id=109367977]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109367977,"title":"A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making","translated_title":"","metadata":{"doi":"10.1038/s41598-023-37497-z","issue":"1","volume":"13","abstract":"Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy. Abbreviations NMAGDM Neutrosophic Multi-Attribute Group Decision Making DMs Decision makers TITENBM Trapezoidal and triangular neutrosophic Bonferroni mean TITRNWBM Trapezoidal and triangular neutrosophic weighted Bonferroni mean FMAGDM Fuzzy multi-attributes group decision-making OPEN 1","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Scientific Reports"},"translated_abstract":"Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy. Abbreviations NMAGDM Neutrosophic Multi-Attribute Group Decision Making DMs Decision makers TITENBM Trapezoidal and triangular neutrosophic Bonferroni mean TITRNWBM Trapezoidal and triangular neutrosophic weighted Bonferroni mean FMAGDM Fuzzy multi-attributes group decision-making OPEN 1","internal_url":"https://www.academia.edu/109367977/A_novel_approach_based_on_neutrosophic_Bonferroni_mean_operator_of_trapezoidal_and_triangular_neutrosophic_interval_environments_in_multi_attribute_group_decision_making","translated_internal_url":"","created_at":"2023-11-18T22:15:10.240-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107514747,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514747/thumbnails/1.jpg","file_name":"7.pdf","download_url":"https://www.academia.edu/attachments/107514747/download_file","bulk_download_file_name":"A_novel_approach_based_on_neutrosophic_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514747/7-libre.pdf?1700377510=\u0026response-content-disposition=attachment%3B+filename%3DA_novel_approach_based_on_neutrosophic_B.pdf\u0026Expires=1738795241\u0026Signature=bklajB-h89T6~ukQuVIcvoBdZcAB5RbIJaXtsy4SA0-8RjZH7xIYo0y9mNLkwdr9iguYBkYGvJRkZHx7IFaRsSpTM-xKnfBFKb8taBdf5q4WeX9bA46EB-U3-U1wREXoUR3fYU5wqg5Jx8w3pD84GdP6r3gNkSZrhZC-y1dBX~E9J12gwI1Gm3TUq4p3HMqnB4DWNIbYQs7LbI5kOukWDyrf3zzR54KOIJ7J9cBBdP01nQvv73luKpzH32B6Q0awtYtRkyQXisC-q1XzKGsIWM2wXjvkqme8wb1eInIryPk-zdCO5gWcZqVaoJiouTBUD6Feoe0mysTDXUsm920g4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_novel_approach_based_on_neutrosophic_Bonferroni_mean_operator_of_trapezoidal_and_triangular_neutrosophic_interval_environments_in_multi_attribute_group_decision_making","translated_slug":"","page_count":11,"language":"en","content_type":"Work","summary":"Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy. Abbreviations NMAGDM Neutrosophic Multi-Attribute Group Decision Making DMs Decision makers TITENBM Trapezoidal and triangular neutrosophic Bonferroni mean TITRNWBM Trapezoidal and triangular neutrosophic weighted Bonferroni mean FMAGDM Fuzzy multi-attributes group decision-making OPEN 1","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":107514747,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107514747/thumbnails/1.jpg","file_name":"7.pdf","download_url":"https://www.academia.edu/attachments/107514747/download_file","bulk_download_file_name":"A_novel_approach_based_on_neutrosophic_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107514747/7-libre.pdf?1700377510=\u0026response-content-disposition=attachment%3B+filename%3DA_novel_approach_based_on_neutrosophic_B.pdf\u0026Expires=1738795241\u0026Signature=bklajB-h89T6~ukQuVIcvoBdZcAB5RbIJaXtsy4SA0-8RjZH7xIYo0y9mNLkwdr9iguYBkYGvJRkZHx7IFaRsSpTM-xKnfBFKb8taBdf5q4WeX9bA46EB-U3-U1wREXoUR3fYU5wqg5Jx8w3pD84GdP6r3gNkSZrhZC-y1dBX~E9J12gwI1Gm3TUq4p3HMqnB4DWNIbYQs7LbI5kOukWDyrf3zzR54KOIJ7J9cBBdP01nQvv73luKpzH32B6Q0awtYtRkyQXisC-q1XzKGsIWM2wXjvkqme8wb1eInIryPk-zdCO5gWcZqVaoJiouTBUD6Feoe0mysTDXUsm920g4Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"}],"urls":[{"id":35527213,"url":"https://www.nature.com/articles/s41598-023-37497-z"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109367885"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109367885/CHARACTERIZING_HYPERGROUPOIDS_THROUGH_M_RELATIONS_AND_M_CONSISTENCIES"><img alt="Research paper thumbnail of CHARACTERIZING HYPERGROUPOIDS THROUGH M -RELATIONS AND M -CONSISTENCIES" class="work-thumbnail" src="https://attachments.academia-assets.com/107514665/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109367885/CHARACTERIZING_HYPERGROUPOIDS_THROUGH_M_RELATIONS_AND_M_CONSISTENCIES">CHARACTERIZING HYPERGROUPOIDS THROUGH M -RELATIONS AND M -CONSISTENCIES</a></div><div class="wp-workCard_item"><span>JOURNAL OF THE INTERNATIONAL MATHEMATICAL VIRTUAL INSTITUTE</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this article, we define the m-right consistent/m-left consistent and m-consistent hypergroupoi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In this article, we define the m-right consistent/m-left consistent and m-consistent hypergroupoids. We also define the m-intra-consistent hypergroupoid. Along these line, we define the Green's m-relations namely mright relation, m-left relations, and m-relation. The other three relations, namely m-reflexive, m-symmetric and m-transitive, are also defined. The idea of m-equivalence relation is also given. We present different characterization of hypergroupoids in the article through these concepts.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="15ea2471f3efa4252706e426455b26ba" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107514665,"asset_id":109367885,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107514665/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109367885"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109367885"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109367885; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="109367585"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109367585/Medical_robotic_engineering_selection_based_on_square_root_neutrosophic_normal_interval_valued_sets_and_their_aggregated_operators"><img alt="Research paper thumbnail of Medical robotic engineering selection based on square root neutrosophic normal interval-valued sets and their aggregated operators" class="work-thumbnail" src="https://attachments.academia-assets.com/107514462/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109367585/Medical_robotic_engineering_selection_based_on_square_root_neutrosophic_normal_interval_valued_sets_and_their_aggregated_operators">Medical robotic engineering selection based on square root neutrosophic normal interval-valued sets and their aggregated operators</a></div><div class="wp-workCard_item"><span>AIMS Mathematics</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We introduce the concepts of multiple attribute decision-making (MADM) using square root neutroso...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We introduce the concepts of multiple attribute decision-making (MADM) using square root neutrosophic normal interval-valued sets (SRNSNIVS). The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer ∆. A medical robotics system is described as combining computer science and 17403 machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="915c027f30d201391c11e3c9be7693eb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107514462,"asset_id":109367585,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107514462/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109367585"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109367585"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109367585; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109367585]").text(description); $(".js-view-count[data-work-id=109367585]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109367585; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109367585']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "915c027f30d201391c11e3c9be7693eb" } } $('.js-work-strip[data-work-id=109367585]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109367585,"title":"Medical robotic engineering selection based on square root neutrosophic normal interval-valued sets and their aggregated operators","translated_title":"","metadata":{"doi":"10.3934/math.2023889","issue":"8","volume":"8","abstract":"We introduce the concepts of multiple attribute decision-making (MADM) using square root neutrosophic normal interval-valued sets (SRNSNIVS). 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A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.","ai_title_tag":"MADM for Medical Robotics Using Neutrosophic Sets","page_numbers":"17402–17432.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"AIMS Mathematics"},"translated_abstract":"We introduce the concepts of multiple attribute decision-making (MADM) using square root neutrosophic normal interval-valued sets (SRNSNIVS). The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer ∆. A medical robotics system is described as combining computer science and 17403 machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. 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The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer ∆. A medical robotics system is described as combining computer science and 17403 machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105590790"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105590790/Characterizations_of_non_associative_ordered_semigroups_by_their_fuzzy_bi_ideals"><img alt="Research paper thumbnail of Characterizations of non-associative ordered semigroups by their fuzzy bi-ideals" class="work-thumbnail" src="https://attachments.academia-assets.com/105002462/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105590790/Characterizations_of_non_associative_ordered_semigroups_by_their_fuzzy_bi_ideals">Characterizations of non-associative ordered semigroups by their fuzzy bi-ideals</a></div><div class="wp-workCard_item"><span>Theoretical Computer Science</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The aim of this paper is to investigate the characterizations of different classes of nonassociat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The aim of this paper is to investigate the characterizations of different classes of nonassociative and non-commutative ordered semigroups in terms of fuzzy left (right, bi-, generalized bi-, (1, 2)-) ideals.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="631ce284c10f49e0b733e0897d3e87bf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105002462,"asset_id":105590790,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105002462/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105590790"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105590790"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105590790; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101951836"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101951836/Techniques_for_Finding_Analytical_Solution_of_Generalized_Fuzzy_Differential_Equations_with_Applications"><img alt="Research paper thumbnail of Techniques for Finding Analytical Solution of Generalized Fuzzy Differential Equations with Applications" class="work-thumbnail" src="https://attachments.academia-assets.com/102349051/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101951836/Techniques_for_Finding_Analytical_Solution_of_Generalized_Fuzzy_Differential_Equations_with_Applications">Techniques for Finding Analytical Solution of Generalized Fuzzy Differential Equations with Applications</a></div><div class="wp-workCard_item"><span>COMLEXITY </span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Engineering and applied mathematics disciplines that involve diferential equations include classi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Engineering and applied mathematics disciplines that involve diferential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufcient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are signifcantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at diferential equations in a generalized intuitionistic fuzzy environment. We used the modifed Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. Te generalized modifed Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. Te outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modifed Adomian decomposition method is efcient, robotic, and reliable, as well as simple to implement.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2c2f26d3c755792284edaad98df1f9fe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":102349051,"asset_id":101951836,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/102349051/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101951836"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101951836"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101951836; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101951836]").text(description); $(".js-view-count[data-work-id=101951836]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101951836; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101951836']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2c2f26d3c755792284edaad98df1f9fe" } } $('.js-work-strip[data-work-id=101951836]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101951836,"title":"Techniques for Finding Analytical Solution of Generalized Fuzzy Differential Equations with Applications","translated_title":"","metadata":{"doi":"10.1155/2023/3000653","volume":"2023","abstract":"Engineering and applied mathematics disciplines that involve diferential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufcient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are signifcantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at diferential equations in a generalized intuitionistic fuzzy environment. We used the modifed Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. Te generalized modifed Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. Te outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modifed Adomian decomposition method is efcient, robotic, and reliable, as well as simple to implement.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"COMLEXITY "},"translated_abstract":"Engineering and applied mathematics disciplines that involve diferential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufcient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are signifcantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at diferential equations in a generalized intuitionistic fuzzy environment. We used the modifed Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. Te generalized modifed Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. Te outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modifed Adomian decomposition method is efcient, robotic, and reliable, as well as simple to implement.","internal_url":"https://www.academia.edu/101951836/Techniques_for_Finding_Analytical_Solution_of_Generalized_Fuzzy_Differential_Equations_with_Applications","translated_internal_url":"","created_at":"2023-05-17T20:39:22.208-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":102349051,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102349051/thumbnails/1.jpg","file_name":"4.pdf","download_url":"https://www.academia.edu/attachments/102349051/download_file","bulk_download_file_name":"Techniques_for_Finding_Analytical_Soluti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102349051/4-libre.pdf?1684381990=\u0026response-content-disposition=attachment%3B+filename%3DTechniques_for_Finding_Analytical_Soluti.pdf\u0026Expires=1738795241\u0026Signature=ai-S4J2AoDjbi00y4RCFytYOWYLOf02GIwwcc5aI1h3iRIFJnDkYu5zHZ4Efo8w1dvBcb0ZqjGsLgXQJZ5ZCPQgQFu5KOust~W4aIwS9HfQpJbvsX-WDhOXjcsVS9hZhLD2tU57o9SZWs7phZ6MM8AhmtFNZPI5yKolU2Ir097cqMPJ917CHfTJUC2Q2M9Gs1iuXqP~0IE2NuiDFa42S9U~eGz578JjxpfYYYECFircB6lApK7K4mzq9OdV3gXou-WrN9zMod7WrIyaCTXW1P9XABr-KEdmNlexHeosqIN8NFYxvmZi2abFnWcOMxlLBCFnyfpBMUyXXYf8mpIZWXw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Techniques_for_Finding_Analytical_Solution_of_Generalized_Fuzzy_Differential_Equations_with_Applications","translated_slug":"","page_count":31,"language":"en","content_type":"Work","summary":"Engineering and applied mathematics disciplines that involve diferential equations include classical mechanics, thermodynamics, electrodynamics, and general relativity. Modelling a wide range of real-world situations sometimes comprises ambiguous, imprecise, or insufcient situational information, as well as multiindex, uncertainty, or restriction dynamics. As a result, intuitionistic fuzzy set models are signifcantly more useful and versatile in dealing with this type of data than fuzzy set models, triangular, or trapezoidal fuzzy set models. In this research, we looked at diferential equations in a generalized intuitionistic fuzzy environment. We used the modifed Adomian decomposition technique to solve generalized intuitionistic fuzzy initial value problems. Te generalized modifed Adomian decomposition technique is used to solve various higher-order generalized trapezoidal intuitionistic fuzzy initial value problems, circuit analysis problems, mass-spring systems, steam supply control sliding value problems, and some other problems in physical science. Te outcomes of numerical test applications were compared to exact technique solutions, and it was shown that our generalized modifed Adomian decomposition method is efcient, robotic, and reliable, as well as simple to implement.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":102349051,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102349051/thumbnails/1.jpg","file_name":"4.pdf","download_url":"https://www.academia.edu/attachments/102349051/download_file","bulk_download_file_name":"Techniques_for_Finding_Analytical_Soluti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102349051/4-libre.pdf?1684381990=\u0026response-content-disposition=attachment%3B+filename%3DTechniques_for_Finding_Analytical_Soluti.pdf\u0026Expires=1738795241\u0026Signature=ai-S4J2AoDjbi00y4RCFytYOWYLOf02GIwwcc5aI1h3iRIFJnDkYu5zHZ4Efo8w1dvBcb0ZqjGsLgXQJZ5ZCPQgQFu5KOust~W4aIwS9HfQpJbvsX-WDhOXjcsVS9hZhLD2tU57o9SZWs7phZ6MM8AhmtFNZPI5yKolU2Ir097cqMPJ917CHfTJUC2Q2M9Gs1iuXqP~0IE2NuiDFa42S9U~eGz578JjxpfYYYECFircB6lApK7K4mzq9OdV3gXou-WrN9zMod7WrIyaCTXW1P9XABr-KEdmNlexHeosqIN8NFYxvmZi2abFnWcOMxlLBCFnyfpBMUyXXYf8mpIZWXw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":12022,"name":"Numerical Analysis","url":"https://www.academia.edu/Documents/in/Numerical_Analysis"},{"id":30888,"name":"Differential Equations","url":"https://www.academia.edu/Documents/in/Differential_Equations"},{"id":31900,"name":"Fuzzy","url":"https://www.academia.edu/Documents/in/Fuzzy"}],"urls":[{"id":31534825,"url":"https://www.hindawi.com/journals/complexity/2023/3000653/"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101951706"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101951706/Robot_sensors_process_based_on_generalized_Fermatean_normal_different_aggregation_operators_framework"><img alt="Research paper thumbnail of Robot sensors process based on generalized Fermatean normal different aggregation operators framework" class="work-thumbnail" src="https://attachments.academia-assets.com/102348946/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101951706/Robot_sensors_process_based_on_generalized_Fermatean_normal_different_aggregation_operators_framework">Robot sensors process based on generalized Fermatean normal different aggregation operators framework</a></div><div class="wp-workCard_item"><span>Aims Mathematics</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Novel methods for multiple attribute decision-making problems are presented in this paper using T...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number δ. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="08d23cd26bfec78f325b1f5f9a02a4d6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":102348946,"asset_id":101951706,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/102348946/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101951706"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101951706"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101951706; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101951706]").text(description); $(".js-view-count[data-work-id=101951706]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101951706; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101951706']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "08d23cd26bfec78f325b1f5f9a02a4d6" } } $('.js-work-strip[data-work-id=101951706]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101951706,"title":"Robot sensors process based on generalized Fermatean normal different aggregation operators framework","translated_title":"","metadata":{"doi":"10.3934/math.2023832","issue":"7","volume":"8","abstract":"Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number δ. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.","page_numbers":"16252-16277","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Aims Mathematics"},"translated_abstract":"Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number δ. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.","internal_url":"https://www.academia.edu/101951706/Robot_sensors_process_based_on_generalized_Fermatean_normal_different_aggregation_operators_framework","translated_internal_url":"","created_at":"2023-05-17T20:35:12.393-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":102348946,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348946/thumbnails/1.jpg","file_name":"3.pdf","download_url":"https://www.academia.edu/attachments/102348946/download_file","bulk_download_file_name":"Robot_sensors_process_based_on_generaliz.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348946/3-libre.pdf?1684382008=\u0026response-content-disposition=attachment%3B+filename%3DRobot_sensors_process_based_on_generaliz.pdf\u0026Expires=1738795241\u0026Signature=U6UQU-ug2fys9DWpBPidB8QSfPp~-g-R1LLx~A1JZR~OzDO8lZLl9qQfFwYtYDlcQGuxPlZgUZ4lDXIscWgXB36d0ZB0iXbOv0H~MHlROpMbh7VQ60wiND9wMFbteGaYWRtcsP4EwsTXjicrs1ex8bIO20qvsL7a7cBllHZjFLAZ4Yvo3xP7nLGr1NRehAtbdTS4TBvrbe3kYlC8Hzo7v6c-VjB8IsFYRLjTOYsGLFn~6ISGefMUGZg~GvUahRo0d23ENEYTXDkfej~hEkHfxHeX6Jlpr1pAYvW1aUhv7Rr2EFv812wFoQTo0XJLT53sutnjsgk5ZAYQ2BNhEmd04Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Robot_sensors_process_based_on_generalized_Fermatean_normal_different_aggregation_operators_framework","translated_slug":"","page_count":26,"language":"en","content_type":"Work","summary":"Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number δ. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":102348946,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348946/thumbnails/1.jpg","file_name":"3.pdf","download_url":"https://www.academia.edu/attachments/102348946/download_file","bulk_download_file_name":"Robot_sensors_process_based_on_generaliz.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348946/3-libre.pdf?1684382008=\u0026response-content-disposition=attachment%3B+filename%3DRobot_sensors_process_based_on_generaliz.pdf\u0026Expires=1738795241\u0026Signature=U6UQU-ug2fys9DWpBPidB8QSfPp~-g-R1LLx~A1JZR~OzDO8lZLl9qQfFwYtYDlcQGuxPlZgUZ4lDXIscWgXB36d0ZB0iXbOv0H~MHlROpMbh7VQ60wiND9wMFbteGaYWRtcsP4EwsTXjicrs1ex8bIO20qvsL7a7cBllHZjFLAZ4Yvo3xP7nLGr1NRehAtbdTS4TBvrbe3kYlC8Hzo7v6c-VjB8IsFYRLjTOYsGLFn~6ISGefMUGZg~GvUahRo0d23ENEYTXDkfej~hEkHfxHeX6Jlpr1pAYvW1aUhv7Rr2EFv812wFoQTo0XJLT53sutnjsgk5ZAYQ2BNhEmd04Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":667751,"name":"Aggregation Operators","url":"https://www.academia.edu/Documents/in/Aggregation_Operators"}],"urls":[{"id":31534768,"url":"https://www.aimspress.com/article/doi/10.3934/math.2023832"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101951613"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101951613/Financial_Performance_Assessment_by_a_Type_2_Fuzzy_Logic_Approach"><img alt="Research paper thumbnail of Financial Performance Assessment by a Type-2 Fuzzy Logic Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/102348876/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101951613/Financial_Performance_Assessment_by_a_Type_2_Fuzzy_Logic_Approach">Financial Performance Assessment by a Type-2 Fuzzy Logic Approach</a></div><div class="wp-workCard_item"><span>Mathematical Problems in Engineering</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Any company must constantly innovate if they want to maintain its market share in the present cut...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big infuence on consumer behavior, yet it goes against the principles of sustainability. Te issue of sustainability has become crucial to their company's growth. In order to evaluate a business frm's sustainability performance statistically, a new and efective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. Te judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. Te main input variables include corporate environmental performance (CEP) and corporate fnancial performance (CFP). Te suggested approach can efectively examine a corporation's sustainable performance, according to experimental fndings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. Te recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="591225f0fee971903c49de9d2b59631c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":102348876,"asset_id":101951613,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/102348876/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101951613"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101951613"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101951613; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101951613]").text(description); $(".js-view-count[data-work-id=101951613]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101951613; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101951613']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "591225f0fee971903c49de9d2b59631c" } } $('.js-work-strip[data-work-id=101951613]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101951613,"title":"Financial Performance Assessment by a Type-2 Fuzzy Logic Approach","translated_title":"","metadata":{"doi":"10.1155/2023/5926162","volume":"2023","abstract":"Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big infuence on consumer behavior, yet it goes against the principles of sustainability. Te issue of sustainability has become crucial to their company's growth. In order to evaluate a business frm's sustainability performance statistically, a new and efective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. Te judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. Te main input variables include corporate environmental performance (CEP) and corporate fnancial performance (CFP). Te suggested approach can efectively examine a corporation's sustainable performance, according to experimental fndings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. Te recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.","ai_title_tag":"Type-2 Fuzzy Logic for Financial Assessment","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Mathematical Problems in Engineering"},"translated_abstract":"Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big infuence on consumer behavior, yet it goes against the principles of sustainability. Te issue of sustainability has become crucial to their company's growth. In order to evaluate a business frm's sustainability performance statistically, a new and efective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. Te judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. Te main input variables include corporate environmental performance (CEP) and corporate fnancial performance (CFP). Te suggested approach can efectively examine a corporation's sustainable performance, according to experimental fndings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. Te recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.","internal_url":"https://www.academia.edu/101951613/Financial_Performance_Assessment_by_a_Type_2_Fuzzy_Logic_Approach","translated_internal_url":"","created_at":"2023-05-17T20:33:06.284-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":102348876,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348876/thumbnails/1.jpg","file_name":"2.pdf","download_url":"https://www.academia.edu/attachments/102348876/download_file","bulk_download_file_name":"Financial_Performance_Assessment_by_a_Ty.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348876/2-libre.pdf?1684382012=\u0026response-content-disposition=attachment%3B+filename%3DFinancial_Performance_Assessment_by_a_Ty.pdf\u0026Expires=1738795241\u0026Signature=cKwcUxc5iUGiCrZTOkuRkPrW~IzC8uFdWuthF5c5YaWxz23G57KnmY~nWYar~0HltY2wv~zhthhnS-0MLXzGndFNirm6CdEsB3MUnPrvyclvlxaHHMoWSbqasOlbIdKdGnnyULua2aoLVNeJ-ZRvt7gbeL-Pyvbq12QH5e9v4mRPgbDlSg7yM9Y27UWVBarBLCxJC~OSg8oyVHcAFvPi~5M81doSc6pNIrZb6mMZzPrKNH5baK7GB5pjOwkhB2N5HTOzRemkDz26Wup3lCcgNJPnungoyP45sA6dfZqXkh3H1rlvhCBstaE1b2kZetPbG4rVXpEbz0EuWZp8yzw8pw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Financial_Performance_Assessment_by_a_Type_2_Fuzzy_Logic_Approach","translated_slug":"","page_count":8,"language":"en","content_type":"Work","summary":"Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big infuence on consumer behavior, yet it goes against the principles of sustainability. Te issue of sustainability has become crucial to their company's growth. In order to evaluate a business frm's sustainability performance statistically, a new and efective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. Te judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. Te main input variables include corporate environmental performance (CEP) and corporate fnancial performance (CFP). Te suggested approach can efectively examine a corporation's sustainable performance, according to experimental fndings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. Te recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":102348876,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348876/thumbnails/1.jpg","file_name":"2.pdf","download_url":"https://www.academia.edu/attachments/102348876/download_file","bulk_download_file_name":"Financial_Performance_Assessment_by_a_Ty.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348876/2-libre.pdf?1684382012=\u0026response-content-disposition=attachment%3B+filename%3DFinancial_Performance_Assessment_by_a_Ty.pdf\u0026Expires=1738795241\u0026Signature=cKwcUxc5iUGiCrZTOkuRkPrW~IzC8uFdWuthF5c5YaWxz23G57KnmY~nWYar~0HltY2wv~zhthhnS-0MLXzGndFNirm6CdEsB3MUnPrvyclvlxaHHMoWSbqasOlbIdKdGnnyULua2aoLVNeJ-ZRvt7gbeL-Pyvbq12QH5e9v4mRPgbDlSg7yM9Y27UWVBarBLCxJC~OSg8oyVHcAFvPi~5M81doSc6pNIrZb6mMZzPrKNH5baK7GB5pjOwkhB2N5HTOzRemkDz26Wup3lCcgNJPnungoyP45sA6dfZqXkh3H1rlvhCBstaE1b2kZetPbG4rVXpEbz0EuWZp8yzw8pw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":595898,"name":"Financial Performance Analysis","url":"https://www.academia.edu/Documents/in/Financial_Performance_Analysis"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="101951552"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/101951552/A_Fuzzy_Intelligent_Computing_Approach_for_Energy_Voltage_Control_of_Microgrids"><img alt="Research paper thumbnail of A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids" class="work-thumbnail" src="https://attachments.academia-assets.com/102348834/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/101951552/A_Fuzzy_Intelligent_Computing_Approach_for_Energy_Voltage_Control_of_Microgrids">A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids</a></div><div class="wp-workCard_item"><span>Journal of Mathematics</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Te control and energy management problems of microgrids (MGs) are challenging due to the high lev...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Te control and energy management problems of microgrids (MGs) are challenging due to the high level of uncertainties and disturbances such as changes in demands, mechanical powers, and solar energies. So, intelligent computing is needed to be developed for these systems. Tis paper uses an optimal and robust fuzzy controller for automatic voltage and frequency regulation. Te fuzzy logic develops the resistance against uncertainties and disturbances such as irradiation, wind power changes, and load demand variation. Te introduced controller uses appropriate and efective criteria that include rising time, settling time, overshoot, and the degree of resistance of the control system to uncertainties and perturbation efects. Trough simulations and compassion with conventional regulators, the better accuracy of the suggested approach is demonstrated.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f65bf5acf2f04b82e76b9618af22da1c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":102348834,"asset_id":101951552,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/102348834/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="101951552"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="101951552"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 101951552; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=101951552]").text(description); $(".js-view-count[data-work-id=101951552]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 101951552; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='101951552']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f65bf5acf2f04b82e76b9618af22da1c" } } $('.js-work-strip[data-work-id=101951552]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":101951552,"title":"A Fuzzy Intelligent Computing Approach for Energy/Voltage Control of Microgrids","translated_title":"","metadata":{"doi":"10.1155/2023/5289114","volume":"2023","abstract":"Te control and energy management problems of microgrids (MGs) are challenging due to the high level of uncertainties and disturbances such as changes in demands, mechanical powers, and solar energies. 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So, intelligent computing is needed to be developed for these systems. Tis paper uses an optimal and robust fuzzy controller for automatic voltage and frequency regulation. Te fuzzy logic develops the resistance against uncertainties and disturbances such as irradiation, wind power changes, and load demand variation. Te introduced controller uses appropriate and efective criteria that include rising time, settling time, overshoot, and the degree of resistance of the control system to uncertainties and perturbation efects. Trough simulations and compassion with conventional regulators, the better accuracy of the suggested approach is demonstrated.","owner":{"id":29298824,"first_name":"Nasreen","middle_initials":null,"last_name":"Kausar","page_name":"NasreenKausar","domain_name":"yildiz","created_at":"2015-04-09T01:52:16.971-07:00","display_name":"Nasreen Kausar","url":"https://yildiz.academia.edu/NasreenKausar"},"attachments":[{"id":102348834,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/102348834/thumbnails/1.jpg","file_name":"1.pdf","download_url":"https://www.academia.edu/attachments/102348834/download_file","bulk_download_file_name":"A_Fuzzy_Intelligent_Computing_Approach_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/102348834/1-libre.pdf?1684382026=\u0026response-content-disposition=attachment%3B+filename%3DA_Fuzzy_Intelligent_Computing_Approach_f.pdf\u0026Expires=1738795242\u0026Signature=W1WcVdMotu2~ACaLnKQBo61TSO2ODJLZrchbRezzlBVUMT9DS0nNdaEeV64OEBSCxDzqTMpk20LK6f8jQOBIGLSA9LiDJqrv7GWnjJ9ajM2Rh18lm8FMoMvkN27J9saXYie~-V7gzonEoJ1hhK7cqq50NJdI5jd3~8ULPXnU4TwvCpiixIkV4ygBYwWqoUWP8yl5rVQjTtpWhdGJYa4Y3dXRgtSutqM5ebuYo3G2-~VbuWtCdJI5ntgn9~Wd-KjGHnR8ahQ0YHHuN6wxlxSDq11MRlCWvA7wt6s8x44MiPIIhCBGJv-tYDsjDg~zeC0bKc18ChR9Uuevc2dCwL0K2A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":5412,"name":"Energy","url":"https://www.academia.edu/Documents/in/Energy"},{"id":31812,"name":"Fuzzy Control","url":"https://www.academia.edu/Documents/in/Fuzzy_Control"},{"id":470532,"name":"Microgrids","url":"https://www.academia.edu/Documents/in/Microgrids"}],"urls":[{"id":31534651,"url":"https://www.hindawi.com/journals/jmath/2023/5289114/"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="99950188"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/99950188/OPTIMIZING_TRANSPORTATION_COST_FOR_BIOMASS_SUPPLY_CHAIN"><img alt="Research paper thumbnail of OPTIMIZING TRANSPORTATION COST FOR BIOMASS SUPPLY CHAIN" class="work-thumbnail" src="https://attachments.academia-assets.com/100903321/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/99950188/OPTIMIZING_TRANSPORTATION_COST_FOR_BIOMASS_SUPPLY_CHAIN">OPTIMIZING TRANSPORTATION COST FOR BIOMASS SUPPLY CHAIN</a></div><div class="wp-workCard_item"><span>Thermal Science </span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Biomass conversion is largely impacted by the cost of transporting biomass materials. As a result...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Biomass conversion is largely impacted by the cost of transporting biomass materials. As a result, businesses need optimization solutions to optimize their transport operations, allocate resources effectively, and reduce their environmental impact. As part of the process of biomass conversion, this paper discusses the transport and biomass optimization problem in detail. The paper presents optimization of transportation cost of two biomass products, natural gas, and bio fuel during the process of biomass conversion final products depending on the transport routes and other factors.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5d9a03840e0f3ceeb7b1fe4c30f961d4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100903321,"asset_id":99950188,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100903321/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="99950188"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="99950188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 99950188; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="99950041"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/99950041/Conormal_Product_for_Intutionistic_Anti_Fuzzy_Graphs"><img alt="Research paper thumbnail of Conormal Product for Intutionistic Anti-Fuzzy Graphs" class="work-thumbnail" src="https://attachments.academia-assets.com/100903206/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/99950041/Conormal_Product_for_Intutionistic_Anti_Fuzzy_Graphs">Conormal Product for Intutionistic Anti-Fuzzy Graphs</a></div><div class="wp-workCard_item"><span>International Journal of Fuzzy Logic and Intelligent Systems</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study introduces and analyzes the conormal product of intuitionistic anti-fuzzy graphs (IAFG...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This study introduces and analyzes the conormal product of intuitionistic anti-fuzzy graphs (IAFGs) and analyzes certain fundamental theorems and applications. Further, new notions on complete and regular IAFGs were introduced, and the conormal product operation was applied to these IAFGs. We showed that the conormal product of two IAFGs could be used and analyzed important results showing that the conormal product of complete, regular, and strong IAFGs is an IAFG.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d8153cb6f1060ba6f2072043315e6ffc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100903206,"asset_id":99950041,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100903206/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="99950041"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="99950041"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 99950041; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98878289"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98878289/Construction_of_Nilpotent_and_Solvable_Lie_Algebra_in_Picture_Fuzzy_Environment"><img alt="Research paper thumbnail of Construction of Nilpotent and Solvable Lie Algebra in Picture Fuzzy Environment" class="work-thumbnail" src="https://attachments.academia-assets.com/100111787/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98878289/Construction_of_Nilpotent_and_Solvable_Lie_Algebra_in_Picture_Fuzzy_Environment">Construction of Nilpotent and Solvable Lie Algebra in Picture Fuzzy Environment</a></div><div class="wp-workCard_item"><span>International Journal of Computational Intelligence Systems</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The picture fuzzy set was introduced by Coung. It is a generalization of the intuitionistic fuzzy...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The picture fuzzy set was introduced by Coung. It is a generalization of the intuitionistic fuzzy set, giving the notion of neutral membership degrees along with the positive and negative ones. Lie groups and Lie algebras have become indispensable for a lot of fields in mathematical and intellectual physics. In 1872, Lie began his work in the field of continuous transformation groups, later named after him as Lie groups. These have become a fundamental body of interest in themselves. In this paper, the authors presented the notion of the picture fuzzy Lie algebra, picture fuzzy Lie sub-algebra, ideal, and homomorphism. Derived and lower central series of picture fuzzy Lie ideals are constructed to define and analyse solvable and nilpotent picture fuzzy Lie ideals.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c72f3533594dff1eefced1a831bf8e2d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100111787,"asset_id":98878289,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100111787/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98878289"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98878289"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98878289; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98875094"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98875094/A_Non_singleton_Type_3_Fuzzy_Modeling_Optimized_by_Square_Root_Cubature_Kalman_Filter"><img alt="Research paper thumbnail of A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter" class="work-thumbnail" src="https://attachments.academia-assets.com/100109215/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98875094/A_Non_singleton_Type_3_Fuzzy_Modeling_Optimized_by_Square_Root_Cubature_Kalman_Filter">A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter</a></div><div class="wp-workCard_item"><span>Intelligent Automation & Soft Computing</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In many problems, to analyze the process/metabolism behavior, a model of the system is identified...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. The main advantages of the suggested approach include better resistance against uncertainties, deep learning, and good convergence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9215d2cfa9f498f9dd3f77c53d86124f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100109215,"asset_id":98875094,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100109215/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98875094"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98875094"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98875094; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98875094]").text(description); $(".js-view-count[data-work-id=98875094]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98875094; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98875094']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9215d2cfa9f498f9dd3f77c53d86124f" } } $('.js-work-strip[data-work-id=98875094]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":98875094,"title":"A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter","translated_title":"","metadata":{"doi":"10.32604/iasc.2023.036623","abstract":"In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. The main advantages of the suggested approach include better resistance against uncertainties, deep learning, and good convergence.","publication_date":{"day":null,"month":null,"year":2023,"errors":{}},"publication_name":"Intelligent Automation \u0026 Soft Computing"},"translated_abstract":"In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. The main advantages of the suggested approach include better resistance against uncertainties, deep learning, and good convergence.","internal_url":"https://www.academia.edu/98875094/A_Non_singleton_Type_3_Fuzzy_Modeling_Optimized_by_Square_Root_Cubature_Kalman_Filter","translated_internal_url":"","created_at":"2023-03-20T21:04:17.326-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":29298824,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":100109215,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100109215/thumbnails/1.jpg","file_name":"20230321113055_88549.pdf","download_url":"https://www.academia.edu/attachments/100109215/download_file","bulk_download_file_name":"A_Non_singleton_Type_3_Fuzzy_Modeling_Op.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100109215/20230321113055_88549.pdf?1679371453=\u0026response-content-disposition=attachment%3B+filename%3DA_Non_singleton_Type_3_Fuzzy_Modeling_Op.pdf\u0026Expires=1738795242\u0026Signature=Qfk6uK-404KHu30GKw3v2rZ8CIDFuF6x9ksvv1myZ4PpBZ3FZGr60KE5oPwY3QCLNwYRIIM4Hm5eeyQCaD8LOC3RWUzOwJkOTvVqJM5OAFiMXEd~TUR2ZtjorS9fFmaptC1Sg0lUqB2SzvfGLOrJBaZ4~~wQ2YAgh4GPZBbv5-ZObA7FExNxiv445Uh8Hp3Tvcv3ecwu6~28vc3tWJwTvcxO3EUN1dseb2Cd8JuN6Nt0Ht5bSxT3-nc5K-7RL2iu6DIATNztdugoFfqDFxX763j8IY0vBKw198dYS0xcGWQeue8JWDVOpHUNtv5~RQHzIcYZpwCibCUJxTaiEkjDtQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Non_singleton_Type_3_Fuzzy_Modeling_Optimized_by_Square_Root_Cubature_Kalman_Filter","translated_slug":"","page_count":16,"language":"en","content_type":"Work","summary":"In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalman filter (SCKF). In the learning algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon capture and sequestration problem using real-world data sets. Through various analyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. 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In this process, quantitative criteria, including educational activities, human resources, space and equipment, and administrative-financial indicators, are commonly investigated. This process is carried out only by the upstream institutions and the view of the system from the perspective of another stakeholder, namely, the students’ parents, are ignored and qualitative-judgmental indicators do not involve the school evaluation results. Consequently, in this study, we used the opinions of five parents of students and five experienced school administrators to capture the perspectives of both key system stakeholders. In addition, to perform a more comprehensive analysis, we added three qualitative criteria that are less noticed within the problem (social environment, health, and students), along with their sub-criteria to the criteria obtained from the research background. We eliminated the less influential sub-criteria using the Delphi technique and continued the study with 10 criteria and 53 sub-criteria. Then, using two widely used methods in this field, AHP and TOPSIS, we determined the weight of the sub-criteria and the ranking based on the experts’ views. In addition, to deal with the ambiguity in experts’ judgments, we transformed the crisp data into fuzzy data. We applied the proposed methodology to rank 15 schools in Tehran, Iran. The results showed that the proposed quantitative criteria significantly impact the schools ranking. In addition, according to the sensitivity analysis results, it was found that ignoring the views of the system from another stakeholder can distort the results. Finally, directions for future research were suggested based on current research limitations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bfc3b7f53540eb41ec93be3c0f21bf8a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100108709,"asset_id":98874489,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100108709/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98874489"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98874489"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98874489; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98874489]").text(description); $(".js-view-count[data-work-id=98874489]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98874489; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98874489']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="98539588"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98539588/New_applications_of_various_distance_techniques_to_multi_criteria_decision_making_challenges_for_ranking_vague_sets"><img alt="Research paper thumbnail of New applications of various distance techniques to multi-criteria decision-making challenges for ranking vague sets" class="work-thumbnail" src="https://attachments.academia-assets.com/99862111/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98539588/New_applications_of_various_distance_techniques_to_multi_criteria_decision_making_challenges_for_ranking_vague_sets">New applications of various distance techniques to multi-criteria decision-making challenges for ranking vague sets</a></div><div class="wp-workCard_item"><span>Aims Mathematics</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Using the Fermatean vague normal set (FVNS), problems requiring multiple attribute decision makin...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Using the Fermatean vague normal set (FVNS), problems requiring multiple attribute decision making (MADM) have been resolved in this article. This article focuses on the log Fermatean vague normal weighted averaging (log FVNWA), logarithmic Fermatean vague normal weighted geometric (log FVNWG), log generalized Fermatean vague normal weighted averaging (log GFVNWA) and log generalized Fermatean vague normal weighted geometric (log GFVNWG) operators. Described the scoring function, accuracy function and operational laws of the log FVNS. The Euclidean and Humming distance are extended with numerical examples. The features of the log FVNS based on the algebraic operations, including idempotency, boundedness, commutativity and monotonicity are also examined. A field of applied engineering called agricultural robotics has been compared to computer science and machine tool technology. Five distinct agricultural robotics including autonomous mobile robots, articulated robots, humanoid robots, cobot robots, and hybrid robots are randomly chosen. Findings can be compared to established criteria to determine which robotics are the most successful. The results of the models are expressed as a natural number α. We contrast several existing with those that have been developed in order to show the effectiveness and accuracy of the models.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1211bbd382efa8bba1546cd087c7bd80" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":99862111,"asset_id":98539588,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/99862111/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98539588"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98539588"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98539588; 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