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data-dom-id="ProfileCheckPaperUpdate-react-component-1e07b8a6-8648-4870-bfec-40282b3c85ac"></div> <div id="ProfileCheckPaperUpdate-react-component-1e07b8a6-8648-4870-bfec-40282b3c85ac"></div> <div class="DesignSystem"><div class="onsite-ping" id="onsite-ping"></div></div><div class="profile-user-info DesignSystem"><div class="social-profile-container"><div class="left-panel-container"><div class="user-info-component-wrapper"><div class="user-summary-cta-container"><div class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Chrysostomos Stylios</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://teiep.academia.edu/">Technological Educational Institute of Epirus</a>, <a class="u-tcGrayDarker" href="https://teiep.academia.edu/Departments/Computer_Engineering/Documents">Computer Engineering</a>, <span class="u-tcGrayDarker">Faculty Member</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Chrysostomos" data-follow-user-id="713166" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="713166"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p class="label">Followers</p><p class="data">351</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">15</p></div></a><a><div class="stat-container js-profile-coauthors" data-broccoli-component="user-info.coauthors-count" data-click-track="profile-expand-user-info-coauthors"><p class="label">Co-authors</p><p class="data">13</p></div></a><div class="js-mentions-count-container" style="display: none;"><a href="/Chrysostomosstylios/mentions"><div class="stat-container"><p class="label">Mentions</p><p class="data"></p></div></a></div><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></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://ucsb.academia.edu/JudithGreen"><img class="profile-avatar u-positionAbsolute" alt="Judith L Green" 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" src="https://0.academia-photos.com/10819/3653/21016275/s200_judith.green.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ucsb.academia.edu/JudithGreen">Judith L Green</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of California, Santa Barbara</p></div></div><div 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Harun Küçük</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Pennsylvania</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://sfu-kras.academia.edu/OlegVorobyev"><img class="profile-avatar u-positionAbsolute" alt="Oleg Yu Vorobyev" 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" src="https://0.academia-photos.com/38890/12977/347919/s200_oleg.vorobyev.gif" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://sfu-kras.academia.edu/OlegVorobyev">Oleg Yu Vorobyev</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Siberian Federal University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ksu.academia.edu/DavidSeamon"><img class="profile-avatar u-positionAbsolute" alt="David Seamon" 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" src="https://0.academia-photos.com/93547/25922/29662134/s200_david.seamon.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ksu.academia.edu/DavidSeamon">David Seamon</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Kansas State University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://cria.academia.edu/ArmandoMarquesGuedes"><img class="profile-avatar u-positionAbsolute" alt="Armando Marques-Guedes" 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" src="https://0.academia-photos.com/134181/3401094/148494125/s200_armando.marques-guedes.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://cria.academia.edu/ArmandoMarquesGuedes">Armando Marques-Guedes</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">UNL - New University of Lisbon</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://boisestate.academia.edu/PatrickLowenthal"><img class="profile-avatar u-positionAbsolute" alt="Patrick Lowenthal" 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" src="https://0.academia-photos.com/191513/46247/3697816/s200_patrick.lowenthal.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://boisestate.academia.edu/PatrickLowenthal">Patrick Lowenthal</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Boise State University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://hacettepe.academia.edu/SadiSeferoglu"><img class="profile-avatar u-positionAbsolute" alt="S. Sadi SEFEROGLU" 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" src="https://0.academia-photos.com/215455/49239/54403211/s200_s._sadi.seferoglu.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://hacettepe.academia.edu/SadiSeferoglu">S. Sadi SEFEROGLU</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Hacettepe University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://oxfordbrookes.academia.edu/FabioCuzzolin"><img class="profile-avatar u-positionAbsolute" alt="Fabio Cuzzolin" 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" src="https://0.academia-photos.com/366407/112374/61740579/s200_fabio.cuzzolin.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://oxfordbrookes.academia.edu/FabioCuzzolin">Fabio Cuzzolin</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Oxford Brookes University</p></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span><a class="ri-more-link js-profile-ri-list-card" data-click-track="profile-user-info-primary-research-interest" data-has-card-for-ri-list="713166">View All (13)</a></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="713166" href="https://www.academia.edu/Documents/in/Fuzzy_Cognitive_Maps"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://teiep.academia.edu/Chrysostomosstylios","location":"/Chrysostomosstylios","scheme":"https","host":"teiep.academia.edu","port":null,"pathname":"/Chrysostomosstylios","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Fuzzy Cognitive Maps"]}" data-trace="false" 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data-dom-id="Pill-react-component-a02bc227-2f87-4024-9344-2fc946bf3acd"></div> <div id="Pill-react-component-a02bc227-2f87-4024-9344-2fc946bf3acd"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="nav-container backbone-profile-documents-nav hidden-xs"><ul class="nav-tablist" role="tablist"><li class="nav-chip active" role="presentation"><a data-section-name="" data-toggle="tab" href="#all" role="tab">all</a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Papers" data-toggle="tab" href="#papers" role="tab" title="Papers"><span>45</span> <span class="ds2-5-body-sm-bold">Papers</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Books" data-toggle="tab" href="#books" role="tab" title="Books"><span>4</span> <span class="ds2-5-body-sm-bold">Books</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Conference-Presentations" data-toggle="tab" href="#conferencepresentations" role="tab" title="Conference Presentations"><span>87</span> <span class="ds2-5-body-sm-bold">Conference Presentations</span></a></li><li class="nav-chip more-tab" role="presentation"><a class="js-profile-documents-more-tab link-unstyled u-textTruncate" data-toggle="dropdown" role="tab">More  <i class="fa fa-chevron-down"></i></a><ul class="js-profile-documents-more-dropdown dropdown-menu dropdown-menu-right profile-documents-more-dropdown" role="menu"><li role="presentation"><a data-click-track="profile-works-tab" data-section-name="Ontology-and-multi-agent-systems" data-toggle="tab" href="#ontologyandmultiagentsystems" role="tab" style="border: none;"><span>1</span> Ontology and multi-agent systems</a></li></ul></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Chrysostomos Stylios</h3></div><div class="js-work-strip profile--work_container" data-work-id="45780173"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/45780173/Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity"><img alt="Research paper thumbnail of Semi-Automated Annotation of Phasic Electromyographic Activity" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/45780173/Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity">Semi-Automated Annotation of Phasic Electromyographic Activity</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://gatech.academia.edu/JacquelineFairley">Jacqueline Fairley</a></span></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="45780173"><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="45780173"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 45780173; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=45780173]").text(description); 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from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=45780173]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":45780173,"title":"Semi-Automated Annotation of Phasic Electromyographic Activity","internal_url":"https://www.academia.edu/45780173/Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity","owner_id":2144406,"coauthors_can_edit":true,"owner":{"id":2144406,"first_name":"Jacqueline","middle_initials":null,"last_name":"Fairley","page_name":"JacquelineFairley","domain_name":"gatech","created_at":"2012-07-18T02:34:42.408-07:00","display_name":"Jacqueline Fairley","url":"https://gatech.academia.edu/JacquelineFairley"},"attachments":[]}, 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="31785676"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/31785676/Combining_latent_class_analysis_labeling_with_multiclass_approach_for_fetal_heart_rate_categorization"><img alt="Research paper thumbnail of Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/31785676/Combining_latent_class_analysis_labeling_with_multiclass_approach_for_fetal_heart_rate_categorization">Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization</a></div><div class="wp-workCard_item"><span>Physiological measurement</span><span>, Jan 20, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The most common approach to assess fetal well-being during delivery is monitoring of fetal heart ...</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 most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractions-the cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natura...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="31785676"><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="31785676"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31785676; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31785676]").text(description); 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$(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="31459584"><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/31459584/Supervisory_Fuzzy_Cognitive_Map_Structure_for_Triage_Assessment_and_Decision_Support_in_the_Emergency_Department"><img alt="Research paper thumbnail of Supervisory Fuzzy Cognitive Map Structure for Triage Assessment and Decision Support in the Emergency Department" class="work-thumbnail" src="https://attachments.academia-assets.com/51816572/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/31459584/Supervisory_Fuzzy_Cognitive_Map_Structure_for_Triage_Assessment_and_Decision_Support_in_the_Emergency_Department">Supervisory Fuzzy Cognitive Map Structure for Triage Assessment and Decision Support in the Emergency Department</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Soft Computing techniques, such as Fuzzy Cognitive Maps (FCMs), can handle uncertainties in model...</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">Soft Computing techniques, such as Fuzzy Cognitive Maps (FCMs), can handle uncertainties in modeling complex situations using abstract inference mechanisms; they have been successfully used to select among different suggestions, to lead to a decision and to develop Medical Decision Support Systems for many medical-discipline applications. FCM models have great ability to handle complexity, uncertainty and abstract inference as is the case in the health care sector. Here is examined the case of the triage procedure in the Emergency Department (ED), where a decision supporting mechanism is quite invaluable. A Hierarchical structure is proposed within an integrated computerized health system where the Supervisor is modeled as an abstract FCM to support the triaging procedure and assessment of the health condition of people with communication difficulties such as the elderly arriving at the ED. There is also the lower level of the hierarchical structure where a FCM-ESI DSS has been developed and used to assign the Triage ESI level of every patient. Here a new methodology for designing and developing the FCM-ESI DSS is presented so to ensure the active involvement of human experts during the FCM-ESI construction procedure.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3f7e44dd0b81705cc9e8b4a44b178322" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51816572,"asset_id":31459584,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51816572/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="31459584"><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="31459584"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31459584; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31459584]").text(description); $(".js-view-count[data-work-id=31459584]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31459584; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31459584']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="31459583"><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/31459583/A_View_of_SME_Clusters_and_Networks_in_Europe_Science_Parks_in_Greece"><img alt="Research paper thumbnail of A View of SME Clusters and Networks in Europe: Science Parks in Greece" class="work-thumbnail" src="https://attachments.academia-assets.com/51816573/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/31459583/A_View_of_SME_Clusters_and_Networks_in_Europe_Science_Parks_in_Greece">A View of SME Clusters and Networks in Europe: Science Parks in Greece</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is a matter of course that each country in the large European Union presents specific characte...</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">It is a matter of course that each country in the large European Union presents specific characters and individual features of its own industrial environment. However, a common peculiarity can be recognized, evidenced by two numbers: the percentage of SMEs in any national industrial system, always close to 90% of the total number of enterprises, and the percentage of personnel employed in SMEs, greater than 60% of the active population. What can also be widely recognized in almost all European countries are the recent crises, which have affected SMEs, and the attempt by SMEs to counteract their difficult position by searching for agreements and cooperation. One type of reciprocal support SMEs looked for in a crisis was contracts with larger enterprises: this gave rise to supply chains. But often the desire of SMEs was to have collaborative links with other SMEs, operating in the same industrial sector and mainly located in the same region: this resulted in the rise of networks and districts. In the last decade, the European Commission has started to promote studies devoted solely to supporting these types of clustering. Some countries have also launched programs to finance SME aggregations, defining agencies for pushing the establishment of new SMEs groups. This chapter offers an outline of a number of different national situations, concerning the rise and, sometimes, the fall of SME clusters and networks. Obviously, the scope of this chapter is not to give an exhaustive presentation of the European situation of SME aggregations: it aims to force the reader to recognize similarities, weakness and strength aspects, and to apply these to an analysis of the SME aggregations performance. RESEARCH CETIM, Pôle Productique Rhône-Alpes, Centre du Design Rhône-Alpes, INGRID, Pôle Optique Rhône-Alpes</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a360cd7c2116ec9337e2f8609054031a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51816573,"asset_id":31459583,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51816573/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="31459583"><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="31459583"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31459583; 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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="31459582"><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/31459582/iCT_Tools_and_approaches_to_Support_and_enhance_Case_Based_Learning"><img alt="Research paper thumbnail of iCT Tools and approaches to Support and enhance Case Based Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/51816569/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/31459582/iCT_Tools_and_approaches_to_Support_and_enhance_Case_Based_Learning">iCT Tools and approaches to Support and enhance Case Based Learning</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5037b767ea4f50548552ce6ccdc02328" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51816569,"asset_id":31459582,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51816569/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="31459582"><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="31459582"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31459582; 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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="31459580"><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/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems"><img alt="Research paper thumbnail of Fuzzy Cognitive Maps Structure for Medical Decision Support Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/51816568/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/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems">Fuzzy Cognitive Maps Structure for Medical Decision Support Systems</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fuzzy Cognitive Maps (FCMs) are a soft computing technique that follows an approach similar to hu...</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">Fuzzy Cognitive Maps (FCMs) are a soft computing technique that follows an approach similar to human reasoning and human decision-making process, considering them a valuable modeling and simulation methodology. FCMs can successfully represent knowledge and experience, introducing concepts for the essential elements and through the use of cause and effect relationships among the concepts Medical Decision Systems are complex systems consisting of irrelevant and relevant subsystems and elements, taking into consideration many factors that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall diagnosis with a different degree. Thus, FCMs are suitable to model Medical Decision Support Systems and the appropriate FCM structures are developed as well as corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5a014d25be63bfb58f7504b35f848b2e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51816568,"asset_id":31459580,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51816568/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="31459580"><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="31459580"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31459580; 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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="31459577"><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/31459577/Augmented_FCM_Supplemented_with_Case_Based_Reasoning_for_Advanced_Medical_Decision_Support"><img alt="Research paper thumbnail of Augmented FCM Supplemented with Case Based Reasoning for Advanced Medical Decision Support" class="work-thumbnail" src="https://attachments.academia-assets.com/51816570/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/31459577/Augmented_FCM_Supplemented_with_Case_Based_Reasoning_for_Advanced_Medical_Decision_Support">Augmented FCM Supplemented with Case Based Reasoning for Advanced Medical Decision Support</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fuzzy Cognitive Maps (FCMs) have been used to design Decision Support Systems and particularly fo...</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">Fuzzy Cognitive Maps (FCMs) have been used to design Decision Support Systems and particularly for medical informatics to develop Intelligent Diagnosis Systems. Even though they have been successfully used in many dif ferent areas, there are situations where incomplete and vague input information may present difficulty in reaching a decision. In this chapter the idea of using the Case Based Reasoning technique to augment FCMs is presented leading to the de velopment of an Advanced Medical Decision Support System. This system is ap plied in the speech pathology area to diagnose language impairments..</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="86f4367f1d20e816972f3478d41e559c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51816570,"asset_id":31459577,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51816570/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="31459577"><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="31459577"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31459577; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31459576]").text(description); $(".js-view-count[data-work-id=31459576]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31459576; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31459576']"); 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: "6839f1949b0b2e32993703184654b6e6" } } $('.js-work-strip[data-work-id=31459576]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31459576,"title":"Case Based Learning Approaches used in Business Schools in Western Greece: The Experiences, the Values, the Good Practices","internal_url":"https://www.academia.edu/31459576/Case_Based_Learning_Approaches_used_in_Business_Schools_in_Western_Greece_The_Experiences_the_Values_the_Good_Practices","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":51816565,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/51816565/thumbnails/1.jpg","file_name":"Case_Based_Learning_Approaches_used_in_Business_Schools_in_Western_Greece.pdf","download_url":"https://www.academia.edu/attachments/51816565/download_file","bulk_download_file_name":"Case_Based_Learning_Approaches_used_in_B.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/51816565/Case_Based_Learning_Approaches_used_in_Business_Schools_in_Western_Greece-libre.pdf?1487232298=\u0026response-content-disposition=attachment%3B+filename%3DCase_Based_Learning_Approaches_used_in_B.pdf\u0026Expires=1739739884\u0026Signature=EUdRzzeHWL8oEdVUXPRanP6wwTOgiBrBxc4ttZ9McmV01g~IW3ktXEJtm0lEUvTmoTf4kfkHvheKIEF9IkyDI5Hk-FrjwzADORX4Nq6T0NzkkyyXO8Ul8NWDA6e0Klqial1~ilbtTjXny~CRvNW5Jl61944QErH1KyHqwS2PdGYcaoqs~gGY-iM-1ji1M-XYD~o4I457u734NS5Wixq1nLsLuau0KxG9YT5ZLfClnClrii7UGogFAFc6ruWPwRijLrfyCENmLl0sfcHYc2dQWK4eTos6WZ1SKRpBm6Pu6qGGQb~4KEhP7lAy6-Hszq8zZKBrW0K6Oj-S0Jzr19JWZQ__\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="15168161"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/15168161/A_WebGIS_platform_to_monitor_environmental_conditions_in_ports_and_their_surroundings_in_South_Eastern_Europe"><img alt="Research paper thumbnail of A WebGIS platform to monitor environmental conditions in ports and their surroundings in South Eastern Europe" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/15168161/A_WebGIS_platform_to_monitor_environmental_conditions_in_ports_and_their_surroundings_in_South_Eastern_Europe">A WebGIS platform to monitor environmental conditions in ports and their surroundings in South Eastern Europe</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The scope of this work is to describe the design and development of a web-based Geographic Inform...</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 scope of this work is to describe the design and development of a web-based Geographic Information System (GIS) application and highlight its usefulness regarding monitoring and evaluating environmental conditions in several ports and their surroundings in the greater South East Europe (SEE). The system receives inputs and handles two kinds of data that are processed and illustrated through maps and graphs at various temporal and spatial scales in this informational platform. The aforementioned data consists of point measurements from stations operating in the area of SEE ports as well as satellite date sets derived monthly for a period of 10 to 12 years, in terms of sea surface temperature, chlorophyll a, and colored dissolved organic matter (CDOM). The WebGIS platform is based on the client–server model and uses Google Maps API services for data plotting. Advanced designing and development tools and methodologies are used. The available valuable data render the application into a trustful and accurate provider of visual environmental interest information regarding the main ports of southeastern Europe and their surroundings that would operate as a guide for an environmentally sustainable future of ports and sea corridors in SEE.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="15168161"><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="15168161"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 15168161; 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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="4195248"><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/4195248/Principal_Component_Analysis_for_the_start_up_transient_and_Hidden_Markov_Modeling_for_broken_rotor_bar_fault_diagnosis_in_asynchronous_machines"><img alt="Research paper thumbnail of Principal Component Analysis for the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines" class="work-thumbnail" src="https://attachments.academia-assets.com/31691477/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/4195248/Principal_Component_Analysis_for_the_start_up_transient_and_Hidden_Markov_Modeling_for_broken_rotor_bar_fault_diagnosis_in_asynchronous_machines">Principal Component Analysis for the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/GeorgeNikolakopoulos">George Nikolakopoulos</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This article presents a novel computational method for the diagnosis of broken rotor bars in thre...</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 article presents a novel computational method for the diagnosis of broken rotor bars in three phase <br />asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is <br />applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient <br />because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s <br />current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially <br />utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken <br />bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two <br />schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed <br />schemes is evaluated by multiple experimental test cases. The results obtained indicate that the sug- <br />gested approaches based on the combination of PCA and HMMs, can be successfully utilized not only <br />for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) <br />of the fault.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="387b9a4b3ce5c5eefbe8e83f76729027" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31691477,"asset_id":4195248,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31691477/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="4195248"><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="4195248"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4195248; 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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="4195236"><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/4195236/_Seismic_mass_density_based_algorithm_for_spatio_temporal_clustering"><img alt="Research paper thumbnail of "Seismic-mass" density-based algorithm for spatio-temporal clustering" class="work-thumbnail" src="https://attachments.academia-assets.com/31691468/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/4195236/_Seismic_mass_density_based_algorithm_for_spatio_temporal_clustering">"Seismic-mass" density-based algorithm for spatio-temporal clustering</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this research work a new hybrid approach to spatio-temporal seismic clustering is proposed. Th...</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 research work a new hybrid approach to spatio-temporal seismic clustering is proposed. The <br />method builds upon a novel density based clustering scheme that explicitly takes into account earthquake’s <br />magnitude during the density estimation. The new density based clustering algorithm considers <br />both time and spatial information during cluster formation. Therefore clusters lie in a spatio-temporal <br />space. A hierarchical agglomerative clustering algorithm acts upon the identified clusters after dropping <br />the time information in order to come up only with the spatial description of seismic events. The <br />approach is demonstrated using data from the vicinity of the Hellenic seismic arc in order to enable its <br />comparison with some of the state-of-the-art distinct seismic region identification methodologies. The <br />presented results indicate that the combination of the two clustering stages could be potentially used <br />for an automatic definition of major seismic sources.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c2b595ad1d8f243ec0d3e99e196d4a93" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31691468,"asset_id":4195236,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31691468/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="4195236"><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="4195236"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4195236; 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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="4195226"><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/4195226/Bearing_fault_detection_based_on_hybrid_ensemble_detector_and_Empirical_Mode_Decomposition"><img alt="Research paper thumbnail of Bearing fault detection based on hybrid ensemble detector and Empirical Mode Decomposition" class="work-thumbnail" src="https://attachments.academia-assets.com/31691462/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/4195226/Bearing_fault_detection_based_on_hybrid_ensemble_detector_and_Empirical_Mode_Decomposition">Bearing fault detection based on hybrid ensemble detector and Empirical Mode Decomposition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly detec...</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">Aiming at more efficient fault diagnosis, this research work presents an integrated<br />anomaly detection approach for seeded bearing faults.Vibration signals from normal<br />bearings and bearings with three different faultl ocations, as well as different fault sizes and loading conditions are examined.The Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set.Then, a hybrid ensemble detector is trained using data coming only from the normal bearings and it is successfully applied for the detection of any deviation from the normal condition.The results prove the potential use of the proposed scheme as a first stage of an alarm signalling system for the detection of bearing faults irrespective of their loading condition.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="445ca45d4d7f595af89e13c87ff52b38" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31691462,"asset_id":4195226,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31691462/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="4195226"><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="4195226"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4195226; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=4195226]").text(description); $(".js-view-count[data-work-id=4195226]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 4195226; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='4195226']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="4195138"><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/4195138/Identification_of_land_cover_land_use_changes_in_the_greater_area_of_the_Preveza_peninsula_in_Greece_using_Landsat_satellite_data"><img alt="Research paper thumbnail of Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data" class="work-thumbnail" src="https://attachments.academia-assets.com/31691362/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/4195138/Identification_of_land_cover_land_use_changes_in_the_greater_area_of_the_Preveza_peninsula_in_Greece_using_Landsat_satellite_data">Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study investigates the Land Use & Land Cover (LULC) changes in a coastal area of the southwe...</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 investigates the Land Use & Land Cover (LULC) changes in a coastal area of the southwest part <br />of Epirus region, called Preveza, situated in North-western Greece. Remote sensing imagery coming from <br />the Enhanced Thematic Mapper (ETMþ) sensor on board at the Landsat 7 satellite platform is used for <br />this purpose. More specifically, we identified LULC changes in this environmentally sensitive coastal area, <br />using Landsat image scenes for the dates of June 19th, 2000 and July 22nd, 2009. During this period, <br />there was an increasing tourist activity and a high growth in the construction sector of the study area. <br />The land-use changes were identified, examining several vegetation indices and band combinations, <br />along with the implementation of different well-known classification techniques. The Normalized Difference <br />Vegetation Index (NDVI) and the Brightness Index (BI) have proved to be the most suitable <br />indices to successfully identify discrete land surface classes for this study area. Regarding the classifiers, a <br />series of traditional and modern algorithms were tested. The Artificial Neural Networks (ANNs) and the <br />Support Vector Machines (SVMs) gave improved results in comparison to other more traditional classification <br />techniques. The best overall accuracy for the study area was achieved with the SVM classifier <br />and reached 96.25% and 97.15% on the dates of June 19th, 2000 and July 22nd, 2009 respectively. The <br />classification results depicted notable urbanization, small deforestation and important LULC changes in <br />the agriculture sector, indicating a rapid coastal environment change in the region of interest</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f6500d328ef744741c2e86e3361222b7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31691362,"asset_id":4195138,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31691362/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="4195138"><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="4195138"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4195138; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f6500d328ef744741c2e86e3361222b7" } } $('.js-work-strip[data-work-id=4195138]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":4195138,"title":"Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data","internal_url":"https://www.academia.edu/4195138/Identification_of_land_cover_land_use_changes_in_the_greater_area_of_the_Preveza_peninsula_in_Greece_using_Landsat_satellite_data","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":31691362,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/31691362/thumbnails/1.jpg","file_name":"Identification_of_land_cover_land_use_changes_in_the_greater_area_of_the_Preveza_peninsula.pdf","download_url":"https://www.academia.edu/attachments/31691362/download_file","bulk_download_file_name":"Identification_of_land_cover_land_use_ch.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/31691362/Identification_of_land_cover_land_use_changes_in_the_greater_area_of_the_Preveza_peninsula-libre.pdf?1391450738=\u0026response-content-disposition=attachment%3B+filename%3DIdentification_of_land_cover_land_use_ch.pdf\u0026Expires=1739739884\u0026Signature=ebphqs7gMCW43nUSgm~6EnHFQED-HUS8U7SHXCx5QHH~0VLFpa7bZGC8XNOtMhBggj2s2lUZxCLjDzkCu-UEfN5uzGuuuLL0cXWi5hy59igvOkWHKw7HMrIIP2xj-EMqMlXc0k8lM6KV84q-OehNidTAi~vcU2o4pw3CtLk9zyfq1WGh5CQDyFDhDENtWqJmTQz3YJgHJn44M0QZ3jrTlBdFnK56USpI-PFdKG0lKdqFjIRmBTJE-h-Uf3IWCe5uip6FVADy7qmhPyKJvbLSZwG4cgM~SMDHi5v6qgGuKB7c~~nR0MJRvwXS89bDHTacsPa~QP~B27BHKA4Au4Ow1w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(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="664513" id="papers"><div class="js-work-strip profile--work_container" data-work-id="45780173"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/45780173/Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity"><img alt="Research paper thumbnail of Semi-Automated Annotation of Phasic Electromyographic Activity" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/45780173/Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity">Semi-Automated Annotation of Phasic Electromyographic Activity</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://gatech.academia.edu/JacquelineFairley">Jacqueline Fairley</a></span></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="45780173"><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="45780173"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 45780173; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=45780173]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":45780173,"title":"Semi-Automated Annotation of Phasic Electromyographic Activity","internal_url":"https://www.academia.edu/45780173/Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity","owner_id":2144406,"coauthors_can_edit":true,"owner":{"id":2144406,"first_name":"Jacqueline","middle_initials":null,"last_name":"Fairley","page_name":"JacquelineFairley","domain_name":"gatech","created_at":"2012-07-18T02:34:42.408-07:00","display_name":"Jacqueline Fairley","url":"https://gatech.academia.edu/JacquelineFairley"},"attachments":[]}, 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="31785676"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/31785676/Combining_latent_class_analysis_labeling_with_multiclass_approach_for_fetal_heart_rate_categorization"><img alt="Research paper thumbnail of Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/31785676/Combining_latent_class_analysis_labeling_with_multiclass_approach_for_fetal_heart_rate_categorization">Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization</a></div><div class="wp-workCard_item"><span>Physiological measurement</span><span>, Jan 20, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The most common approach to assess fetal well-being during delivery is monitoring of fetal heart ...</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 most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractions-the cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natura...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="31785676"><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="31785676"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31785676; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31785676]").text(description); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "96334ee34e7fbe064b134614d2d20b47" } } $('.js-work-strip[data-work-id=31459585]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31459585,"title":"The Soft Computing Technique of FCM for Decision Making in Radiotherapy","internal_url":"https://www.academia.edu/31459585/The_Soft_Computing_Technique_of_FCM_for_Decision_Making_in_Radiotherapy","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":51816576,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/51816576/thumbnails/1.jpg","file_name":"The_Soft_Computing_Technique_of_FCM_for_Decision_Making_in_Radiotherapy.pdf","download_url":"https://www.academia.edu/attachments/51816576/download_file","bulk_download_file_name":"The_Soft_Computing_Technique_of_FCM_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/51816576/The_Soft_Computing_Technique_of_FCM_for_Decision_Making_in_Radiotherapy-libre.pdf?1487232304=\u0026response-content-disposition=attachment%3B+filename%3DThe_Soft_Computing_Technique_of_FCM_for.pdf\u0026Expires=1739739883\u0026Signature=Ua1G1ImBgly1PDs1BWDnHCy5-6kVOdMOnAK-H3ZVkUsuGdHFTbbjzvc6wgL3n8gTE7qIuzdsre8JcVRLjQTiVguumoR3LfZDfuuyk3BBTv50jmXsexDtXv~lnj7WqzvzUQPJ4QA1VPC11M6wbck93O7i6FmAFMxMgy9W98xR20mdrkjVJI4Vz-1qVNx~c6hRkJJwICYFyUU54JDDAF1X-J4AWCLpqma3CDXlVLEakSKajV~0boPgHlugmaHhYyGWH-pkctmmcr1yuV6m-Hcmv2bzjqxYumVUSBexfPY5RwlYY1pRBq~sfKxsYIE85uWvZ349-GbDy8iqAgxU~ZEkOw__\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="31459584"><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/31459584/Supervisory_Fuzzy_Cognitive_Map_Structure_for_Triage_Assessment_and_Decision_Support_in_the_Emergency_Department"><img alt="Research paper thumbnail of Supervisory Fuzzy Cognitive Map Structure for Triage Assessment and Decision Support in the Emergency Department" class="work-thumbnail" src="https://attachments.academia-assets.com/51816572/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/31459584/Supervisory_Fuzzy_Cognitive_Map_Structure_for_Triage_Assessment_and_Decision_Support_in_the_Emergency_Department">Supervisory Fuzzy Cognitive Map Structure for Triage Assessment and Decision Support in the Emergency Department</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Soft Computing techniques, such as Fuzzy Cognitive Maps (FCMs), can handle uncertainties in model...</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">Soft Computing techniques, such as Fuzzy Cognitive Maps (FCMs), can handle uncertainties in modeling complex situations using abstract inference mechanisms; they have been successfully used to select among different suggestions, to lead to a decision and to develop Medical Decision Support Systems for many medical-discipline applications. FCM models have great ability to handle complexity, uncertainty and abstract inference as is the case in the health care sector. Here is examined the case of the triage procedure in the Emergency Department (ED), where a decision supporting mechanism is quite invaluable. A Hierarchical structure is proposed within an integrated computerized health system where the Supervisor is modeled as an abstract FCM to support the triaging procedure and assessment of the health condition of people with communication difficulties such as the elderly arriving at the ED. There is also the lower level of the hierarchical structure where a FCM-ESI DSS has been developed and used to assign the Triage ESI level of every patient. Here a new methodology for designing and developing the FCM-ESI DSS is presented so to ensure the active involvement of human experts during the FCM-ESI construction procedure.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3f7e44dd0b81705cc9e8b4a44b178322" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51816572,"asset_id":31459584,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51816572/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="31459584"><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="31459584"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31459584; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31459584]").text(description); $(".js-view-count[data-work-id=31459584]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31459584; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31459584']"); 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: "3f7e44dd0b81705cc9e8b4a44b178322" } } $('.js-work-strip[data-work-id=31459584]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31459584,"title":"Supervisory Fuzzy Cognitive Map Structure for Triage Assessment and Decision Support in the Emergency Department","internal_url":"https://www.academia.edu/31459584/Supervisory_Fuzzy_Cognitive_Map_Structure_for_Triage_Assessment_and_Decision_Support_in_the_Emergency_Department","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":51816572,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/51816572/thumbnails/1.jpg","file_name":"Supervisory_FCM_Structure_for_Triage_Assessment_and_Decision_Support_in_the_Emergency_Department.pdf","download_url":"https://www.academia.edu/attachments/51816572/download_file","bulk_download_file_name":"Supervisory_Fuzzy_Cognitive_Map_Structur.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/51816572/Supervisory_FCM_Structure_for_Triage_Assessment_and_Decision_Support_in_the_Emergency_Department-libre.pdf?1487232296=\u0026response-content-disposition=attachment%3B+filename%3DSupervisory_Fuzzy_Cognitive_Map_Structur.pdf\u0026Expires=1739798197\u0026Signature=W1vP-2FPhn8BIc5tkfNP7pSNubHuDNQJp4KS6Kuhe~Lr79oHJDZlAAJ1~vo6JnaMomYvC4v9M1ZLi6gw7z2EdPUE26QsZLidXW1SMz3fybTP56OPuUoknjMim-l9Zrs-nfcDzJL4fCdMRix8AGR5a4C7pLKlUyO9Dp9-k~gqBtBrhNg0NXiYkyRwFqz2x0Ag9H52mqPmbYdZHwxo2gnAeCNBca9OM4bCk~5N0flhuT0ELPCNxicKM97tWRo72lOKdJN3GxgH9qqrf2j5DnhSYf6rzypfCG-3YV3CzwqIxbZvj8XrdYiV8dl0IdJlp~R42nW3inM2Td9ZaXI5etigsw__\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="31459583"><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/31459583/A_View_of_SME_Clusters_and_Networks_in_Europe_Science_Parks_in_Greece"><img alt="Research paper thumbnail of A View of SME Clusters and Networks in Europe: Science Parks in Greece" class="work-thumbnail" src="https://attachments.academia-assets.com/51816573/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/31459583/A_View_of_SME_Clusters_and_Networks_in_Europe_Science_Parks_in_Greece">A View of SME Clusters and Networks in Europe: Science Parks in Greece</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is a matter of course that each country in the large European Union presents specific characte...</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">It is a matter of course that each country in the large European Union presents specific characters and individual features of its own industrial environment. However, a common peculiarity can be recognized, evidenced by two numbers: the percentage of SMEs in any national industrial system, always close to 90% of the total number of enterprises, and the percentage of personnel employed in SMEs, greater than 60% of the active population. What can also be widely recognized in almost all European countries are the recent crises, which have affected SMEs, and the attempt by SMEs to counteract their difficult position by searching for agreements and cooperation. One type of reciprocal support SMEs looked for in a crisis was contracts with larger enterprises: this gave rise to supply chains. But often the desire of SMEs was to have collaborative links with other SMEs, operating in the same industrial sector and mainly located in the same region: this resulted in the rise of networks and districts. In the last decade, the European Commission has started to promote studies devoted solely to supporting these types of clustering. Some countries have also launched programs to finance SME aggregations, defining agencies for pushing the establishment of new SMEs groups. This chapter offers an outline of a number of different national situations, concerning the rise and, sometimes, the fall of SME clusters and networks. Obviously, the scope of this chapter is not to give an exhaustive presentation of the European situation of SME aggregations: it aims to force the reader to recognize similarities, weakness and strength aspects, and to apply these to an analysis of the SME aggregations performance. RESEARCH CETIM, Pôle Productique Rhône-Alpes, Centre du Design Rhône-Alpes, INGRID, Pôle Optique Rhône-Alpes</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a360cd7c2116ec9337e2f8609054031a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51816573,"asset_id":31459583,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51816573/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="31459583"><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="31459583"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31459583; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31459583]").text(description); $(".js-view-count[data-work-id=31459583]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31459583; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31459583']"); 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: "a360cd7c2116ec9337e2f8609054031a" } } $('.js-work-strip[data-work-id=31459583]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31459583,"title":"A View of SME Clusters and Networks in Europe: Science Parks in Greece","internal_url":"https://www.academia.edu/31459583/A_View_of_SME_Clusters_and_Networks_in_Europe_Science_Parks_in_Greece","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":51816573,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/51816573/thumbnails/1.jpg","file_name":"Science_Parks_in_Greece.pdf","download_url":"https://www.academia.edu/attachments/51816573/download_file","bulk_download_file_name":"A_View_of_SME_Clusters_and_Networks_in_E.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/51816573/Science_Parks_in_Greece-libre.pdf?1487232300=\u0026response-content-disposition=attachment%3B+filename%3DA_View_of_SME_Clusters_and_Networks_in_E.pdf\u0026Expires=1739739883\u0026Signature=TmrtvajulaYCDuJ83Hg692mN~FecfXZ9IIvU7mtVV0T4yBrDsjPk68MEDoOaSTsNQTAkk-IS1FhkhaUGTSRJw~PnC8Y~2pfdqM5wquCxZJ5O138OXtyduNjgL83lEPRuW~StDBYai9T22ZIEAkLQSVPewOepRWRQrAjVkigeViOU7cxgo~RFOywo5pY-xzBYuIsgsQ-DEVqh0kS1LJjbOJEPxvyNg6TaBQJPjRsPsegRcVNeY5PVuwTXVFirCfxWsYyqDVfodg~CVDs6aLx-toMpFqTFwyc5Lnq3mIwBtOC0VrVrVaKzvv~kxcT348XGbO2oCLJDlx4IfavV~0T5RQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); 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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="31459580"><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/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems"><img alt="Research paper thumbnail of Fuzzy Cognitive Maps Structure for Medical Decision Support Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/51816568/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/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems">Fuzzy Cognitive Maps Structure for Medical Decision Support Systems</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fuzzy Cognitive Maps (FCMs) are a soft computing technique that follows an approach similar to hu...</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">Fuzzy Cognitive Maps (FCMs) are a soft computing technique that follows an approach similar to human reasoning and human decision-making process, considering them a valuable modeling and simulation methodology. FCMs can successfully represent knowledge and experience, introducing concepts for the essential elements and through the use of cause and effect relationships among the concepts Medical Decision Systems are complex systems consisting of irrelevant and relevant subsystems and elements, taking into consideration many factors that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall diagnosis with a different degree. Thus, FCMs are suitable to model Medical Decision Support Systems and the appropriate FCM structures are developed as well as corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5a014d25be63bfb58f7504b35f848b2e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51816568,"asset_id":31459580,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51816568/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="31459580"><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="31459580"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31459580; 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Even though they have been successfully used in many dif ferent areas, there are situations where incomplete and vague input information may present difficulty in reaching a decision. In this chapter the idea of using the Case Based Reasoning technique to augment FCMs is presented leading to the de velopment of an Advanced Medical Decision Support System. 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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="15168161"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/15168161/A_WebGIS_platform_to_monitor_environmental_conditions_in_ports_and_their_surroundings_in_South_Eastern_Europe"><img alt="Research paper thumbnail of A WebGIS platform to monitor environmental conditions in ports and their surroundings in South Eastern Europe" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/15168161/A_WebGIS_platform_to_monitor_environmental_conditions_in_ports_and_their_surroundings_in_South_Eastern_Europe">A WebGIS platform to monitor environmental conditions in ports and their surroundings in South Eastern Europe</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The scope of this work is to describe the design and development of a web-based Geographic Inform...</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 scope of this work is to describe the design and development of a web-based Geographic Information System (GIS) application and highlight its usefulness regarding monitoring and evaluating environmental conditions in several ports and their surroundings in the greater South East Europe (SEE). The system receives inputs and handles two kinds of data that are processed and illustrated through maps and graphs at various temporal and spatial scales in this informational platform. The aforementioned data consists of point measurements from stations operating in the area of SEE ports as well as satellite date sets derived monthly for a period of 10 to 12 years, in terms of sea surface temperature, chlorophyll a, and colored dissolved organic matter (CDOM). The WebGIS platform is based on the client–server model and uses Google Maps API services for data plotting. Advanced designing and development tools and methodologies are used. The available valuable data render the application into a trustful and accurate provider of visual environmental interest information regarding the main ports of southeastern Europe and their surroundings that would operate as a guide for an environmentally sustainable future of ports and sea corridors in SEE.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="15168161"><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="15168161"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 15168161; 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</script> <div class="js-work-strip profile--work_container" data-work-id="4767681"><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/4767681/Bearing_fault_detection_based_on_hybrid_ensemble_detector_and_Empirical_Mode_Decomposition"><img alt="Research paper thumbnail of Bearing fault detection based on hybrid ensemble detector and Empirical Mode Decomposition" class="work-thumbnail" src="https://attachments.academia-assets.com/32073980/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/4767681/Bearing_fault_detection_based_on_hybrid_ensemble_detector_and_Empirical_Mode_Decomposition">Bearing fault detection based on hybrid ensemble detector and Empirical Mode Decomposition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">and sharing with colleagues.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f7b1fda2fdbcce7ba05e5b44f070c774" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":32073980,"asset_id":4767681,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/32073980/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="4767681"><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="4767681"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4767681; 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$(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="4195248"><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/4195248/Principal_Component_Analysis_for_the_start_up_transient_and_Hidden_Markov_Modeling_for_broken_rotor_bar_fault_diagnosis_in_asynchronous_machines"><img alt="Research paper thumbnail of Principal Component Analysis for the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines" class="work-thumbnail" src="https://attachments.academia-assets.com/31691477/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/4195248/Principal_Component_Analysis_for_the_start_up_transient_and_Hidden_Markov_Modeling_for_broken_rotor_bar_fault_diagnosis_in_asynchronous_machines">Principal Component Analysis for the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/GeorgeNikolakopoulos">George Nikolakopoulos</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This article presents a novel computational method for the diagnosis of broken rotor bars in thre...</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 article presents a novel computational method for the diagnosis of broken rotor bars in three phase <br />asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is <br />applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient <br />because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s <br />current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially <br />utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken <br />bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two <br />schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed <br />schemes is evaluated by multiple experimental test cases. The results obtained indicate that the sug- <br />gested approaches based on the combination of PCA and HMMs, can be successfully utilized not only <br />for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) <br />of the fault.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="387b9a4b3ce5c5eefbe8e83f76729027" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31691477,"asset_id":4195248,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31691477/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="4195248"><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="4195248"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4195248; 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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="4195236"><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/4195236/_Seismic_mass_density_based_algorithm_for_spatio_temporal_clustering"><img alt="Research paper thumbnail of "Seismic-mass" density-based algorithm for spatio-temporal clustering" class="work-thumbnail" src="https://attachments.academia-assets.com/31691468/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/4195236/_Seismic_mass_density_based_algorithm_for_spatio_temporal_clustering">"Seismic-mass" density-based algorithm for spatio-temporal clustering</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this research work a new hybrid approach to spatio-temporal seismic clustering is proposed. Th...</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 research work a new hybrid approach to spatio-temporal seismic clustering is proposed. The <br />method builds upon a novel density based clustering scheme that explicitly takes into account earthquake’s <br />magnitude during the density estimation. The new density based clustering algorithm considers <br />both time and spatial information during cluster formation. Therefore clusters lie in a spatio-temporal <br />space. A hierarchical agglomerative clustering algorithm acts upon the identified clusters after dropping <br />the time information in order to come up only with the spatial description of seismic events. The <br />approach is demonstrated using data from the vicinity of the Hellenic seismic arc in order to enable its <br />comparison with some of the state-of-the-art distinct seismic region identification methodologies. The <br />presented results indicate that the combination of the two clustering stages could be potentially used <br />for an automatic definition of major seismic sources.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c2b595ad1d8f243ec0d3e99e196d4a93" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31691468,"asset_id":4195236,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31691468/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="4195236"><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="4195236"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4195236; 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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="4195226"><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/4195226/Bearing_fault_detection_based_on_hybrid_ensemble_detector_and_Empirical_Mode_Decomposition"><img alt="Research paper thumbnail of Bearing fault detection based on hybrid ensemble detector and Empirical Mode Decomposition" class="work-thumbnail" src="https://attachments.academia-assets.com/31691462/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/4195226/Bearing_fault_detection_based_on_hybrid_ensemble_detector_and_Empirical_Mode_Decomposition">Bearing fault detection based on hybrid ensemble detector and Empirical Mode Decomposition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly detec...</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">Aiming at more efficient fault diagnosis, this research work presents an integrated<br />anomaly detection approach for seeded bearing faults.Vibration signals from normal<br />bearings and bearings with three different faultl ocations, as well as different fault sizes and loading conditions are examined.The Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set.Then, a hybrid ensemble detector is trained using data coming only from the normal bearings and it is successfully applied for the detection of any deviation from the normal condition.The results prove the potential use of the proposed scheme as a first stage of an alarm signalling system for the detection of bearing faults irrespective of their loading condition.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="445ca45d4d7f595af89e13c87ff52b38" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31691462,"asset_id":4195226,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31691462/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="4195226"><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="4195226"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4195226; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=4195226]").text(description); $(".js-view-count[data-work-id=4195226]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 4195226; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='4195226']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="4195138"><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/4195138/Identification_of_land_cover_land_use_changes_in_the_greater_area_of_the_Preveza_peninsula_in_Greece_using_Landsat_satellite_data"><img alt="Research paper thumbnail of Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data" class="work-thumbnail" src="https://attachments.academia-assets.com/31691362/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/4195138/Identification_of_land_cover_land_use_changes_in_the_greater_area_of_the_Preveza_peninsula_in_Greece_using_Landsat_satellite_data">Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This study investigates the Land Use & Land Cover (LULC) changes in a coastal area of the southwe...</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 investigates the Land Use & Land Cover (LULC) changes in a coastal area of the southwest part <br />of Epirus region, called Preveza, situated in North-western Greece. Remote sensing imagery coming from <br />the Enhanced Thematic Mapper (ETMþ) sensor on board at the Landsat 7 satellite platform is used for <br />this purpose. More specifically, we identified LULC changes in this environmentally sensitive coastal area, <br />using Landsat image scenes for the dates of June 19th, 2000 and July 22nd, 2009. During this period, <br />there was an increasing tourist activity and a high growth in the construction sector of the study area. <br />The land-use changes were identified, examining several vegetation indices and band combinations, <br />along with the implementation of different well-known classification techniques. The Normalized Difference <br />Vegetation Index (NDVI) and the Brightness Index (BI) have proved to be the most suitable <br />indices to successfully identify discrete land surface classes for this study area. Regarding the classifiers, a <br />series of traditional and modern algorithms were tested. The Artificial Neural Networks (ANNs) and the <br />Support Vector Machines (SVMs) gave improved results in comparison to other more traditional classification <br />techniques. The best overall accuracy for the study area was achieved with the SVM classifier <br />and reached 96.25% and 97.15% on the dates of June 19th, 2000 and July 22nd, 2009 respectively. The <br />classification results depicted notable urbanization, small deforestation and important LULC changes in <br />the agriculture sector, indicating a rapid coastal environment change in the region of interest</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f6500d328ef744741c2e86e3361222b7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31691362,"asset_id":4195138,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31691362/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="4195138"><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="4195138"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4195138; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31466466]").text(description); $(".js-view-count[data-work-id=31466466]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31466466; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31466466']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=31466466]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31466466,"title":"A Software Architecture for Integrated Logistic Management System","internal_url":"https://www.academia.edu/31466466/A_Software_Architecture_for_Integrated_Logistic_Management_System","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[]}, 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="31785675"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/31785675/Coastal_Marine_Environment_Monitoring_Using_Satellite_Data_Derived_from_MODIS_Instrument"><img alt="Research paper thumbnail of Coastal Marine Environment Monitoring Using Satellite Data Derived from MODIS Instrument" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/31785675/Coastal_Marine_Environment_Monitoring_Using_Satellite_Data_Derived_from_MODIS_Instrument">Coastal Marine Environment Monitoring Using Satellite Data Derived from MODIS Instrument</a></div><div class="wp-workCard_item"><span>Sustainable Development of Sea-Corridors and Coastal Waters</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The quality of marine environment has a vital importance for the sustainable future of E...</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">ABSTRACT The quality of marine environment has a vital importance for the sustainable future of Earth’s planet. On the other hand, the human activities, the sea commerce and transportation affect significantly the marine environment especially in coastal areas, port areas and the sea-corridors. These induced activities impose contiguous and accurate methods for marine environment monitoring. Nowadays, modern satellite instruments gather data and the derived from them relative products can be used as an alternative, robust and accurate way to monitor many and basic marine parameters such as Chlorophyll, Sea Surface Temperature, Euphotic Depth, Dissolved Organic matter and examine their long-term (climatic) tendencies. This study comprises an effort to assess the accuracy of satellite products, comparing them with relative ground based measurements and it also focuses on provision of satellite-based mean variations in monthly basis regarding two important marine parameters (Chlorophyll-a and Sea Surface Temperature). In this study, available measurements of two different ports are used, i.e. port of Bar in Montenegro and port of Burgas in Bulgaria, which are partners of TEN ECOPORT (Transnational ENhancement of ECOPORT8 network) project.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="31785675"><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="31785675"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31785675; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31785675]").text(description); $(".js-view-count[data-work-id=31785675]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31785675; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31785675']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=31785675]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31785675,"title":"Coastal Marine Environment Monitoring Using Satellite Data Derived from MODIS Instrument","internal_url":"https://www.academia.edu/31785675/Coastal_Marine_Environment_Monitoring_Using_Satellite_Data_Derived_from_MODIS_Instrument","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="31466688"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/31466688/Fuzzy_Cognitive_Map_to_Model_Project_Management_Problems"><img alt="Research paper thumbnail of Fuzzy Cognitive Map to Model Project Management Problems" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/31466688/Fuzzy_Cognitive_Map_to_Model_Project_Management_Problems">Fuzzy Cognitive Map to Model Project Management Problems</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://nwmissouri.academia.edu/DeniseCase">Denise Case</a></span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="31466688"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="31466688"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31466688; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31466688]").text(description); $(".js-view-count[data-work-id=31466688]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31466688; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31466688']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=31466688]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31466688,"title":"Fuzzy Cognitive Map to Model Project Management Problems","internal_url":"https://www.academia.edu/31466688/Fuzzy_Cognitive_Map_to_Model_Project_Management_Problems","owner_id":2162586,"coauthors_can_edit":true,"owner":{"id":2162586,"first_name":"Denise","middle_initials":null,"last_name":"Case","page_name":"DeniseCase","domain_name":"nwmissouri","created_at":"2012-07-21T00:26:19.890-07:00","display_name":"Denise Case","url":"https://nwmissouri.academia.edu/DeniseCase"},"attachments":[]}, 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="31785677"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/31785677/An_automated_thermographic_image_segmentation_method_for_induction_motor_fault_diagnosis"><img alt="Research paper thumbnail of An automated thermographic image segmentation method for induction motor fault diagnosis" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/31785677/An_automated_thermographic_image_segmentation_method_for_induction_motor_fault_diagnosis">An automated thermographic image segmentation method for induction motor fault diagnosis</a></div><div class="wp-workCard_item"><span>IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Eventual failures in induction machines may lead to catastrophic consequences in terms o...</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">ABSTRACT Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="31785677"><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="31785677"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31785677; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31785677]").text(description); $(".js-view-count[data-work-id=31785677]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31785677; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31785677']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=31785677]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31785677,"title":"An automated thermographic image segmentation method for induction motor fault diagnosis","internal_url":"https://www.academia.edu/31785677/An_automated_thermographic_image_segmentation_method_for_induction_motor_fault_diagnosis","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[]}, 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="31785680"><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/31785680/Symbolic_Aggregate_ApproXimation_SAX_under_Interval_Uncertainty"><img alt="Research paper thumbnail of Symbolic Aggregate ApproXimation (SAX) under Interval Uncertainty" class="work-thumbnail" src="https://attachments.academia-assets.com/52087109/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/31785680/Symbolic_Aggregate_ApproXimation_SAX_under_Interval_Uncertainty">Symbolic Aggregate ApproXimation (SAX) under Interval Uncertainty</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In many practical situations, we monitor a system by continuously measuring the corresponding qua...</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 practical situations, we monitor a system by continuously measuring the corresponding quantities, to make sure that an abnormal deviation is detected as early as possible. Often, we do not have ready algorithms to detect abnormality, so we need to use machine learning techniques. For these techniques to be efficient, we first need to compress the data. One of the most successful methods of data compression is the technique of Symbolic Aggregate approXimation (SAX). While this technique is motivated by measurement uncertainty, it does not explicitly take this uncertainty into account. In this paper, we show that we can further improve upon this techniques if we explicitly take measurement uncertainty into account.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d4ebb4a97e80a3eb8afdf0b705eefba3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":52087109,"asset_id":31785680,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/52087109/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="31785680"><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="31785680"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31785680; 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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="31785678"><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/31785678/Symbolic_Representation_of_Human_Electromyograms_for_Automated_Detection_of_Phasic_Activity_During_Sleep"><img alt="Research paper thumbnail of Symbolic Representation of Human Electromyograms for Automated Detection of Phasic Activity During Sleep" class="work-thumbnail" src="https://attachments.academia-assets.com/52087117/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/31785678/Symbolic_Representation_of_Human_Electromyograms_for_Automated_Detection_of_Phasic_Activity_During_Sleep">Symbolic Representation of Human Electromyograms for Automated Detection of Phasic Activity During Sleep</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this study we investigate the feasibility of applying Symbolic Aggregate approximation (SAX) 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">In this study we investigate the feasibility of applying Symbolic Aggregate approximation (SAX) to automatically classify phasic eletromyographic (EMG) activity in human polysomnograms (PSGs). SAX offers potential benefits for time series analysis of PSGs that include: 1) dimensionality and storage space reduction and 2) access to robust symbolic based data mining algorithms, such as intelligent icons. To evaluate the proposed symbolic classification scheme we compare, expert visual scoring of phasic EMG activity, a reliable quantitative metric to assist in discriminating neurodegenerative disorder populations and age-matched controls, to a k-Nearest Neighbor intelligent icon based SAX scheme. Detection of non-phasic EMG activity exceeded 90% and detection of phasic EMG activity ranged between 53 to 90 %, for six subjects.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ad1f82bee460cba296e02ad1d72d3898" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":52087117,"asset_id":31785678,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/52087117/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="31785678"><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="31785678"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 31785678; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=31785681]").text(description); $(".js-view-count[data-work-id=31785681]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 31785681; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='31785681']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=31785681]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":31785681,"title":"Metaheuristic Approaches for Scheduling the Trieste-Fernetti Pickup and Delivery Service","internal_url":"https://www.academia.edu/31785681/Metaheuristic_Approaches_for_Scheduling_the_Trieste_Fernetti_Pickup_and_Delivery_Service","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[]}, 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="8917500"><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/8917500/_Time_Dependent_Fuzzy_Cognitive_Maps_for_Medical_Diagnosis_"><img alt="Research paper thumbnail of “Time Dependent Fuzzy Cognitive Maps for Medical Diagnosis”" class="work-thumbnail" src="https://attachments.academia-assets.com/35243158/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/8917500/_Time_Dependent_Fuzzy_Cognitive_Maps_for_Medical_Diagnosis_">“Time Dependent Fuzzy Cognitive Maps for Medical Diagnosis”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract. Time dependence in medical diagnosis is important since, frequently, symptoms evolve o...</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">Abstract. <br />Time dependence in medical diagnosis is important since, frequently, symptoms evolve over time, thus, changing with the progression of an illness. Taking into consideration that medical information may be vague, missing and/or conflicting during the diagnostic procedure, a new type of Fuzzy Cognitive Maps (FCMs), the soft computing technique that can handle uncertainty to infer a result, have been developed for Medical Diagnosis. Here, a method to enhance the FCM behaviour is proposed introducing time units that can follow disease progression. An example from the pulmonary field is described. <br />Keywords: Fuzzy Cognitive Map, time evolution, medical diagnosis.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e75e128e15a529f1b0fdd3b06340f0b5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":35243158,"asset_id":8917500,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/35243158/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="8917500"><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="8917500"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8917500; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8917500]").text(description); $(".js-view-count[data-work-id=8917500]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8917500; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='8917500']"); 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: "e75e128e15a529f1b0fdd3b06340f0b5" } } $('.js-work-strip[data-work-id=8917500]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":8917500,"title":"“Time Dependent Fuzzy Cognitive Maps for Medical Diagnosis”","internal_url":"https://www.academia.edu/8917500/_Time_Dependent_Fuzzy_Cognitive_Maps_for_Medical_Diagnosis_","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":35243158,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/35243158/thumbnails/1.jpg","file_name":"Time_Dependent_FCMs_for_Medical_Diagnosis.pdf","download_url":"https://www.academia.edu/attachments/35243158/download_file","bulk_download_file_name":"Time_Dependent_Fuzzy_Cognitive_Maps_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/35243158/Time_Dependent_FCMs_for_Medical_Diagnosis-libre.pdf?1414032035=\u0026response-content-disposition=attachment%3B+filename%3DTime_Dependent_Fuzzy_Cognitive_Maps_for.pdf\u0026Expires=1739827424\u0026Signature=MVQNdmP0J4gOP~UlbFKiCCMD0DPcvHqp8LFstSBmmQmBMEKldTHanPT9dPM0ZbZLhj77cEZSS6X1k-gpqX37vKcGlGpm6FH~VMtmjOW6Bek40ILn6msfWUJ7WqiHl5Hq9Ha2g-tNMO99YT1noB18xOSaP84Kfgmaf1DUNR7voXyCS3ik3iNDW3QIEXRhmWBQqUCR1W0drT~jxLEeXzqMtnEr9-D8ecZ9-VitE45kmN~DjYubX4nKskydCUpydW0lhYjR9fy2JJBt-lLP3S3DUykKKx1a8eTfyqlqgF9k0P84Rn-qu8y5HvHLMHxCjEL1nwwVz-io3vgS6CHElSSD1w__\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="8917447"><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/8917447/_Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity_"><img alt="Research paper thumbnail of “Semi-Automated Annotation of Phasic Electromyographic Activity”" class="work-thumbnail" src="https://attachments.academia-assets.com/35242984/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/8917447/_Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity_">“Semi-Automated Annotation of Phasic Electromyographic Activity”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract. Recent research on manual/visual identification of phasic muscle activity utilizing th...</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">Abstract. <br />Recent research on manual/visual identification of phasic muscle activity utilizing the phasic electromyographic metric (PEM) in human polysomnograms (PSGs) cites evidence that PEM is a potentially reliable quantitative metric to assist in distinguishing between neurodegenerative disorder populations and age-matched controls. However, visual scoring of PEM activity is time consuming-preventing feasible implementation within a clinical setting. Therefore, here we propose an assistive/semi-supervised software platform designed and tested to automatically identify and characterize PEM events in a clinical setting that will be extremely useful for sleep physicians and technicians. The proposed semi-automated approach consists of four levels: A) Signal Parsing, B) Calculation of quantitative features on candidate PEM events, C) Classification of PEM and non-PEM events using a linear classifier, and D) Post-processing/Expert feedback to correct/remove automated misclassifications of PEM and Non-PEM events. Performance evaluation of the designed software compared to manual labeling is provided for electromyographic (EMG) activity from the PSG of a control subject. Results indicate that the semi-automated approach provides an excellent benchmark that could be embedded into a clinical decision support system to detect PEM events that would be used in neurological disorder identification and treatment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="671b79197b6fc1a7d5cea88a682c6094" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":35242984,"asset_id":8917447,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/35242984/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="8917447"><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="8917447"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8917447; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8917447]").text(description); $(".js-view-count[data-work-id=8917447]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8917447; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='8917447']"); 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: "671b79197b6fc1a7d5cea88a682c6094" } } $('.js-work-strip[data-work-id=8917447]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":8917447,"title":"“Semi-Automated Annotation of Phasic Electromyographic Activity”","internal_url":"https://www.academia.edu/8917447/_Semi_Automated_Annotation_of_Phasic_Electromyographic_Activity_","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":35242984,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/35242984/thumbnails/1.jpg","file_name":"Semi-Automated_Annotation_of_Phasic_Electromyographic_Activity.pdf","download_url":"https://www.academia.edu/attachments/35242984/download_file","bulk_download_file_name":"Semi_Automated_Annotation_of_Phasic_Ele.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/35242984/Semi-Automated_Annotation_of_Phasic_Electromyographic_Activity-libre.pdf?1414032114=\u0026response-content-disposition=attachment%3B+filename%3DSemi_Automated_Annotation_of_Phasic_Ele.pdf\u0026Expires=1739827424\u0026Signature=FqED2~EqI6uh-uzJOMRvFeuXxW1pkwDIQ5K1oGHG~bjaAEssKHns5B4qxfTgmYJd1b8a8FP5UpHLrmVeYvtywmJhj60V91jFNtsqfcE4Hq2tP0xZLaj-zy5eM8slFdoC6~5IojyGVnc7AE2lPguk~4CPcGUEzKdSBOuX6iG7RQypVcxt~SVXveOlUB1f9HOP6ITPNul3GZTbDvenJWqNsIBdmkYeU1b-tTW-j-lLJbPojV6dsoIemh33f3zF2OboxJtdQrbqcojVdGrtd~aUf8TVLlhudIpkTcQaV3l6lNPETQM4fO4VAU1E~j1hGZUVh-y~rUPvg3ARvYmqclKSOA__\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="8917165"><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/8917165/_Discriminating_Normal_from_Abnormal_Pregnancy_Cases_Using_an_Automated_FHR_Evaluation_Method_"><img alt="Research paper thumbnail of “Discriminating Normal from “Abnormal” Pregnancy Cases Using an Automated FHR Evaluation Method”" class="work-thumbnail" src="https://attachments.academia-assets.com/35242885/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/8917165/_Discriminating_Normal_from_Abnormal_Pregnancy_Cases_Using_an_Automated_FHR_Evaluation_Method_">“Discriminating Normal from “Abnormal” Pregnancy Cases Using an Automated FHR Evaluation Method”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract. Electronic fetal monitoring has become the gold standard for fetal assessment both duri...</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">Abstract. Electronic fetal monitoring has become the gold standard for fetal assessment both during pregnancy as well as during delivery. Even though electronic fetal monitoring has been introduced to clinical practice more than forty years ago, there is still controversy in its usefulness especially due to the high inter- and intra-observer variability. Therefore the need for a more reliable and consistent interpretation has prompted the research community to investigate and propose various automated methodologies. In this work we propose the use of an automated method for the evaluation of fetal heart rate, the main monitored signal, which is based on a data set, whose labels/annotations are determined using a mixture model of clinical annotations. The successful results of the method suggest that it could be integrated into an assistive technology during delivery. <br />Keywords: Electronic fetal monitoring, Fetal Heart Rate, Random Forests, Classification.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="79a1a66d644cf124112a964443502be4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":35242885,"asset_id":8917165,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/35242885/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="8917165"><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="8917165"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8917165; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8917165]").text(description); $(".js-view-count[data-work-id=8917165]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8917165; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='8917165']"); 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: "79a1a66d644cf124112a964443502be4" } } $('.js-work-strip[data-work-id=8917165]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":8917165,"title":"“Discriminating Normal from “Abnormal” Pregnancy Cases Using an Automated FHR Evaluation Method”","internal_url":"https://www.academia.edu/8917165/_Discriminating_Normal_from_Abnormal_Pregnancy_Cases_Using_an_Automated_FHR_Evaluation_Method_","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":35242885,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/35242885/thumbnails/1.jpg","file_name":"Discriminating_Normal_from_Abnormal_Pregnancy_Cases_Using_an_Automated_FHR_Evaluation_Method.pdf","download_url":"https://www.academia.edu/attachments/35242885/download_file","bulk_download_file_name":"Discriminating_Normal_from_Abnormal_Pre.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/35242885/Discriminating_Normal_from_Abnormal_Pregnancy_Cases_Using_an_Automated_FHR_Evaluation_Method-libre.pdf?1414032054=\u0026response-content-disposition=attachment%3B+filename%3DDiscriminating_Normal_from_Abnormal_Pre.pdf\u0026Expires=1739827424\u0026Signature=NEBS5vl5LIvlOYvdNWu-JWpwsc87KTQRh6TRyCuK3yb3lItaeOp-ZMVvehbBlFGuALKG-tFr6Age13U31W9xjBE~HY1A8FwQ1bsBLRRX5m4b~LivBu0Q2ltlL2c3TmM5CIFmYfapeEtXvCnZeL9-5ucSwTbXIRyU8-6H4UAFicKGjP7k~rpNs-rV~kD-CNqHXP-tfMUDI2yOub56lVjrjPZz27Snxn5FTg9KNc7P12g-RsTkxQ8Qkc5-7BBzLBIpkqYWmHNdfUNEo1Pbi6Fjd2vq9uCRSypK9tfVO8~Wkc3AJHpBxYLAyp5elepAQUqxxTUdkFCwuyEEqocLunEh0Q__\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="4394775"><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/4394775/_Fuzzy_Cognitive_Maps_for_Medical_Diagnosis_Support_A_paradigm_from_Obstetrics_"><img alt="Research paper thumbnail of “Fuzzy Cognitive Maps for Medical Diagnosis Support - A paradigm from Obstetrics”" class="work-thumbnail" src="https://attachments.academia-assets.com/31825154/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/4394775/_Fuzzy_Cognitive_Maps_for_Medical_Diagnosis_Support_A_paradigm_from_Obstetrics_">“Fuzzy Cognitive Maps for Medical Diagnosis Support - A paradigm from Obstetrics”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Medical Decision Support Systems can provide assistance in crucial clinical judgm ents, particu...</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">Medical Decision Support Systems can provide <br />assistance in crucial clinical judgm <br />ents, particularly for <br />inexperienced medical professionals. Fuzzy Cognitive Maps <br />(FCMs) is a soft computing technique for mo <br />d <br />eling complex <br />systems follow <br />ing <br />an approach similar to human reasoning and <br />decision <br />- <br />making. FCMs successfully represent knowledge a <br />nd <br />human experience, introducing co <br />n <br />cepts to represent the <br />essential elements and the cause and effect relationships among <br />the concepts to model the behavior of any system. Medical <br />Decision Systems are complex syst <br />ems that can be decomposed <br />to <br />su <br />b <br />systems a <br />nd elements, where many factors have to be <br />taken into consideration that may be complementary, <br />contr <br />a <br />dictory, and competitive; these factors influence each <br />other and determine the overall clinical decision with <br />varying <br />degree <br />s <br />. <br />Here a <br />Medical Decision Sup <br />port System <br />based on an <br />appropriate FCM arch <br />i <br />tecture <br />is <br />proposed and developed <br />, <br />as <br />well as <br />a <br />corresponding <br />paradigm <br />from obstetrics is d <br />e <br />scribed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cc42127beacb4ae28637f34436e79518" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31825154,"asset_id":4394775,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31825154/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="4394775"><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="4394775"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394775; 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A...</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">Instrumental evaluation of the fetal well-being during delivery is more than hundred years old. Auscultation -sensing of the fetal heart rate (fHR) using a fetal stethoscope -introduced by Pinard in 1876 -was replaced in 1960's by electronic fetal monitoring(EFM) with cardiotocography (CTG -recording of fetal heart rate and force/pressure of contractions) as the most important representant.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="659b23c891b961d1ee83c807773d037f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31825147,"asset_id":4394766,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31825147/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="4394766"><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="4394766"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394766; 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$(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="4394718"><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/4394718/_Is_it_possible_to_distinguish_different_types_of_ECG_holder_beats_based_solely_on_features_obtained_from_windwed_QRS_complex_"><img alt="Research paper thumbnail of “Is it possible to distinguish different types of ECG-holder beats based solely on features obtained from windwed QRS complex?”" class="work-thumbnail" src="https://attachments.academia-assets.com/31825110/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/4394718/_Is_it_possible_to_distinguish_different_types_of_ECG_holder_beats_based_solely_on_features_obtained_from_windwed_QRS_complex_">“Is it possible to distinguish different types of ECG-holder beats based solely on features obtained from windwed QRS complex?”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The main focus of this paper is to investigate the possibility to distinguish among different cl...</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 main focus of this paper is to investigate <br />the possibility to distinguish among different classes of <br />beats, as provided by ANSI/AAMI EC57:1998 standard, <br />from the ECG holter recordings. <br />We compare the perform- <br />ance of an ensemble classifier <br />based on three classifiers on <br />distinguishing ECG beats from holter <br />recordings character- <br />ized by two distinct <br />sets of features. <br />The first feature set is one relying upon the "classical" <br />time interval measurements of QRS complex and T-wave. <br />The second one tries to de <br />scribe the beat using means as <br />simple as possible resulting in a description of the QRS <br />complex in terms of "easy-to-compute" statistical moments; <br />hermite coefficients and Karhunen <br />Loeve coefficients. <br />The results of the ensemble classifier consisting of three <br />different classifiers – namely a k-NN classifier, a Back <br />propagation Neural Network and a Support Vector classi- <br />fier- are as general as possible by using global train- <br />ing/testing approach that uses one half of the recordings <br />from the MIT-BIH database for training and the other half <br />for testing. Results of the cl <br />assifier are computed using <br />sensitivity (Se) and specificity (Sp) for both feature sets. The <br />best results achieved during <br />the experiments <br />were those <br />using the "classical" feature set and the ensemble classifier. <br />The specificity for detection of normal beats was 74.26% <br />and sensitivities were 68.19%, 45.73%, 35.19%, 48.70% for <br />ventricular, bundle branch <br />blocks, supraventricular, and <br />fusion beats respectively. The <br />results achieved <br />on the "easy- <br />to-compute" approach are comparable to those from "clas- <br />sical" approach when dealing <br />with the detection of ventricu- <br />lar beats with specificity 74.73% and sensitivity 59.97% – <br />but they have performed much <br />worse when trying to detect <br />the other classes such as supr <br />aventricular, fusion or bundle <br />branch block beats.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ad10d2e2ea2d0208f5b964418f8a16d9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31825110,"asset_id":4394718,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31825110/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="4394718"><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="4394718"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394718; 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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="4394687"><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/4394687/_Diagnosis_Support_Using_Fuzzy_Cognitive_Maps_combined_with_Genetic_Algorithms_"><img alt="Research paper thumbnail of “Diagnosis Support Using Fuzzy Cognitive Maps combined with Genetic Algorithms”" class="work-thumbnail" src="https://attachments.academia-assets.com/31825100/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/4394687/_Diagnosis_Support_Using_Fuzzy_Cognitive_Maps_combined_with_Genetic_Algorithms_">“Diagnosis Support Using Fuzzy Cognitive Maps combined with Genetic Algorithms”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new hybrid modeling methodology to support medical diagnosis decisions is developed here. It e...</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">A new hybrid modeling methodology to support <br />medical diagnosis decisions is developed here. It extends <br />previous work on Competitive Fuzzy Cognitive Maps for <br />Medical Diagnosis Support Systems by complementing them <br />with Genetic Algorithms Methods for concept interaction. The <br />synergy of these methodologies is accomplished by a new <br />proposed algorithm that leads to more dependable Advanced <br />Medical Diagnosis Support Systems that are suitable to handle <br />situations where the decisions are not clearly distinct. The <br />technique developed here is applied successfully to model and <br />test a differential diagnosis problem from the speech pathology <br />area for the diagnosis of language impairments.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9f666b3df633c8040055bf77d57fd7af" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31825100,"asset_id":4394687,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31825100/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="4394687"><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="4394687"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394687; 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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="4394654"><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/4394654/_Investigating_Articulating_Disorders_Using_Empirical_Mode_Decomposition_"><img alt="Research paper thumbnail of “Investigating Articulating Disorders Using Empirical Mode Decomposition”" class="work-thumbnail" src="https://attachments.academia-assets.com/31825081/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/4394654/_Investigating_Articulating_Disorders_Using_Empirical_Mode_Decomposition_">“Investigating Articulating Disorders Using Empirical Mode Decomposition”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a preliminary study of the applicability of a novel signal processing techn...</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 paper presents a preliminary study of <br />the applicability of a novel signal processing <br />technique as a means to exact valuable information <br />so that to diagnose the possible existence of a speech <br />articulation disorder in a speaker. Articulation, in <br />effect, is the specific and characteristic way that an <br />individual produces the speech sounds. Emprirical <br />Mode Decomposition and the Hilbert Huang <br />transform is applied in an attempt to identify <br />potential features to be used in an articulator <br />disorder detector.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1040dc5770418861f24076e6c41c4969" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31825081,"asset_id":4394654,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31825081/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="4394654"><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="4394654"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394654; 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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="4394630"><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/4394630/_SVM_classification_of_holter_ECG_beats_using_wavelet_features_"><img alt="Research paper thumbnail of “SVM classification of holter ECG beats using wavelet features”" class="work-thumbnail" src="https://attachments.academia-assets.com/31825069/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/4394630/_SVM_classification_of_holter_ECG_beats_using_wavelet_features_">“SVM classification of holter ECG beats using wavelet features”</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8d78121fff1845f8b4f946d540da0f29" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31825069,"asset_id":4394630,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31825069/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="4394630"><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="4394630"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394630; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=4394630]").text(description); $(".js-view-count[data-work-id=4394630]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 4394630; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='4394630']"); 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: "8d78121fff1845f8b4f946d540da0f29" } } $('.js-work-strip[data-work-id=4394630]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":4394630,"title":"“SVM classification of holter ECG beats using wavelet features”","internal_url":"https://www.academia.edu/4394630/_SVM_classification_of_holter_ECG_beats_using_wavelet_features_","owner_id":713166,"coauthors_can_edit":true,"owner":{"id":713166,"first_name":"Chrysostomos","middle_initials":"","last_name":"Stylios","page_name":"Chrysostomosstylios","domain_name":"teiep","created_at":"2011-09-06T18:17:14.553-07:00","display_name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},"attachments":[{"id":31825069,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/31825069/thumbnails/1.jpg","file_name":"SVM_Classification_OF_HOLTER_ECG_BEATS.pdf","download_url":"https://www.academia.edu/attachments/31825069/download_file","bulk_download_file_name":"SVM_classification_of_holter_ECG_beats.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/31825069/SVM_Classification_OF_HOLTER_ECG_BEATS-libre.pdf?1392419057=\u0026response-content-disposition=attachment%3B+filename%3DSVM_classification_of_holter_ECG_beats.pdf\u0026Expires=1739739884\u0026Signature=bhTjRdtgVQslmKIszJBXl8TK1OtiS~ShwMj9H-i3z1g-hUBS1GsQbPDx7n4zK5zZpAuHpKyTaW8WiBMcYgGOxF0Ckk4ZtANOgbAJM9lnakJmUvP5y8RdLn2B5oc~iDw6mJpMYR6bx71uGr2LiUUgLOvJlKbz-43bg7HSjMhoJLOTqaLMUsZ~Dhl76Uo~YU8RR0Xh2A-taalPHpfKR-Xe-ujkGYgpx6RCmg8xC5Ylli5uEAQ-NOrcpYJYypQBwpZzsbbE5k~O9ShCYBTfM9eyD5ozkf5ApsXwU~fvPQ3eiiTAMu9A5DRHQl8rt5G0RRE7d7WmlTM-BLIjJ65zaXFB~A__\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="4394477"><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/4394477/_Comparison_of_seven_approaches_for_holter_ECG_clustering_and_classification_"><img alt="Research paper thumbnail of “Comparison of seven approaches for holter ECG clustering and classification”" class="work-thumbnail" src="https://attachments.academia-assets.com/31824962/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/4394477/_Comparison_of_seven_approaches_for_holter_ECG_clustering_and_classification_">“Comparison of seven approaches for holter ECG clustering and classification”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this work we present a comparative study, testing selected methods for clustering and classif...</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 work we present a comparative study, <br />testing selected methods for clustering and classification of <br />holter electrocardiogram (ECG). More specifically we focus on <br />the task of discriminating between normal ‘N’ beats and <br />premature ventricular ‘V’ beats <br />Some of the tested methods represent the state of the art in <br />pattern analysis, while others are novel algorithms developed <br />by us. All the algorithms were tested on the same datasets, <br />namely the MIT-BIH and the AHA databases. <br />The results for all the employed methods are compared and <br />evaluated using the measures of sensitivity and specificity</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="254ec690dd460e5688aeedf4a4538d7f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31824962,"asset_id":4394477,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31824962/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="4394477"><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="4394477"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394477; 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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="4394443"><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/4394443/_Novel_Architecture_for_supporting_medical_decision_making_of_different_data_types_based_on_Fuzzy_Cognitive_Map_Framework_"><img alt="Research paper thumbnail of “Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework”" class="work-thumbnail" src="https://attachments.academia-assets.com/31824927/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/4394443/_Novel_Architecture_for_supporting_medical_decision_making_of_different_data_types_based_on_Fuzzy_Cognitive_Map_Framework_">“Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Medical problems involve different types of variables and data, which have to be processed, anal...</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">Medical problems involve different types of <br />variables and data, which have to be processed, analyzed and <br />synthesized in order to reach a decision and/or conclude to a <br />diagnosis. Usually, information and data set are both symbolic <br />and numeric but most of the well-known data analysis methods <br />deal with only one kind of data. Even when fuzzy approaches <br />are considered, which are not depended on the scales of <br />variables, usually only numeric data is considered. The medical <br />decision support methods usually are accessed in only one type <br />of available data. Thus, sophisticated methods have been <br />proposed such as integrated hybrid learning approaches to <br />process symbolic and numeric data for the decision support <br />tasks. Fuzzy Cognitive Maps (FCM) is an efficient modelling <br />method, which is based on human knowledge and experience <br />and it can handle with uncertainty and it is constructed by <br />extracted knowledge in the form of fuzzy rules. The FCM <br />model can be enhanced if a fuzzy rule base (IF-THEN rules) is <br />available. This rule base could be derived by a number of <br />machine learning and knowledge extraction methods. Here it is <br />introduced a hybrid attempt to handle situations with different <br />types of available medical and /or clinical data and with <br />difficulty to handle them for decision support tasks using soft <br />computing techniques.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a578302025a422083a4c59ea26c81abc" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31824927,"asset_id":4394443,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31824927/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="4394443"><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="4394443"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394443; 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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="4394427"><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/4394427/_Comparison_of_Methods_for_Premature_Ventricular_Beat_Detection_"><img alt="Research paper thumbnail of “Comparison of Methods for Premature Ventricular Beat Detection”" class="work-thumbnail" src="https://attachments.academia-assets.com/31824919/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/4394427/_Comparison_of_Methods_for_Premature_Ventricular_Beat_Detection_">“Comparison of Methods for Premature Ventricular Beat Detection”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Holter ECG mon i torin g is u s ed for lon g -term monitoring of patients w i th he...</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">Holter ECG mon <br />i <br />torin <br />g <br />is <br />u <br />s <br />ed <br />for lon <br />g <br />-term <br />monitoring of patients w <br />i <br />th <br />heart problems for diagnosis <br />reas <br />o <br />n <br />s <br />. A lot of res <br />earch <br />w <br />o <br />rk <br />h <br />a <br />s <br />b <br />een <br />d <br />o <br />n <br />in <br />th <br />is <br />field <br />an <br />d <br />many <br />me <br />thods and pr <br />oc <br />e <br />dur <br />e <br />s <br />h <br />ave been investigated. This <br />p <br />a <br />p <br />er d <br />i <br />s <br />c <br />u <br />s <br />s <br />e <br />s <br />an <br />d <br />comp <br />ares <br />a n <br />u <br />m <br />b <br />er of d <br />i <br />fferen <br />t ap <br />p <br />r <br />oach <br />e <br />s <br />of c <br />l <br />uste <br />r <br />i <br />ng algor <br />ithms foc <br />u <br />sing <br />on distinguishing pr <br />e <br />m <br />atur <br />e <br />ventricular complexes (V) from the normal (N) <br />beats. <br />Algorith <br />m <br />s <br />w <br />ere tes <br />t <br />ed <br />on <br />MI <br />T-BI <br />H <br />d <br />a <br />tab <br />a <br />s <br />e <br />an <br />d <br />res <br />u <br />l <br />ts <br />are <br />comp <br />u <br />t <br />ed <br />for local an <br />d <br />glob <br />al <br />tr <br />aining sets. Template matching <br />tech <br />n <br />i <br />q <br />u <br />e <br />u <br />s <br />in <br />g ru <br />le-b <br />as <br />ed <br />d <br />ecis <br />i <br />on <br />tree clu <br />s <br />terin <br />g <br />algorith <br />m <br />for <br />data r <br />e <br />duc <br />tion pe <br />r <br />f <br />or <br />me <br />d <br />be <br />st w <br />i <br />th specificity <br />of 96.63 % <br />and sensitivity <br />of 92.64 %.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d720cda3d9f401c28ba6c1cbd1ee6a8f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31824919,"asset_id":4394427,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31824919/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="4394427"><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="4394427"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394427; 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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="4394404"><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/4394404/_Hybrid_model_based_on_Decision_Trees_and_Fuzzy_Cognitive_Maps_for_Medical_Decision_Support_System_"><img alt="Research paper thumbnail of “Hybrid model based on Decision Trees and Fuzzy Cognitive Maps for Medical Decision Support System” " class="work-thumbnail" src="https://attachments.academia-assets.com/31824908/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/4394404/_Hybrid_model_based_on_Decision_Trees_and_Fuzzy_Cognitive_Maps_for_Medical_Decision_Support_System_">“Hybrid model based on Decision Trees and Fuzzy Cognitive Maps for Medical Decision Support System” </a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">For medical decision making processes (diag- nosing, classification, etc.) all decisions must b...</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">For medical decision <br />making processes (diag- <br />nosing, classification, etc.) all decisions must be made effec- <br />tively and reliably. Conceptual decision making models with <br />the potential of learning capabilities are more appropriate and <br />suitable for performing such hard <br />tasks. Decision trees are a <br />well known technique, which has been applied in many medi- <br />cal systems to support decisions based on a set of instances. On <br />the other hand, the soft computing technique of Fuzzy Cogni- <br />tive Maps (FCMs) is an effective decision making technique, <br />which provides high performance with a conceptual represen- <br />tation of gathered knowledge a <br />nd existing experience. FCMs <br />have been used for medical decision making with emphasis in <br />radiotherapy and classification <br />tasks for bladder tumour grad- <br />ing. This paper proposes and <br />presents an hybrid model de- <br />rived from the combination and <br />the synergistic application of <br />the above mentioned techniques. <br />The proposed Decision Tree- <br />Fuzzy Cognitive Map model has <br />enhanced operation and effec- <br />tiveness based on both methods <br />giving better accuracy results <br />in medical decision tasks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="37ed2cca7684f2a5858d14248ab59bfa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31824908,"asset_id":4394404,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31824908/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="4394404"><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="4394404"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4394404; 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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="8904477" id="ontologyandmultiagentsystems"><div class="js-work-strip profile--work_container" data-work-id="729203"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/729203/Communication_Interfaces_inside_the_PSIM_Environment"><img alt="Research paper thumbnail of Communication Interfaces inside the PSIM Environment" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.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" rel="nofollow" href="https://www.academia.edu/729203/Communication_Interfaces_inside_the_PSIM_Environment">Communication Interfaces inside the PSIM Environment</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/JanGoossenaerts">Jan Goossenaerts</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT This chapter deals with the communication interfaces existing within the PSIM enviromnen...</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">ABSTRACT This chapter deals with the communication interfaces existing within the PSIM enviromnent. A general overview is given of the term mapping techniques that have been applied in the interfaces. The definition, description and development of term mapping between the components of the PSIM infrastructure are analyzed and some examples are also presented. This chapter concludes with a description of the communication layer of the PSIM environment.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><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="729203"><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="729203"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 729203; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=729203]").text(description); 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