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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 Fahrettin Horasan</h3></div><div class="js-work-strip profile--work_container" data-work-id="123428043"><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/123428043/Secure_Encryption_of_Biomedical_Images_Based_on_Arneodo_Chaotic_System_with_the_Lowest_Fractional_Order_Value"><img alt="Research paper thumbnail of Secure Encryption of Biomedical Images Based on Arneodo Chaotic System with the Lowest Fractional-Order Value" class="work-thumbnail" src="https://attachments.academia-assets.com/117861254/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/123428043/Secure_Encryption_of_Biomedical_Images_Based_on_Arneodo_Chaotic_System_with_the_Lowest_Fractional_Order_Value">Secure Encryption of Biomedical Images Based on Arneodo Chaotic System with the Lowest Fractional-Order Value</a></div><div class="wp-workCard_item"><span>Electronics</span><span>, May 29, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared ...</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">Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical characteristics of the fractional-order Arneodo (FOAR) system with cubic nonlinearity is conducted. This investigation involves the analysis of phase planes, bifurcation diagrams, Lyapunov exponential spectra, and spectral entropy. Numerical studies show that the Arneodo chaotic system exhibits chaotic behavior when the lowest fractional-order (FO) value is set to 0.55. In this context, the aim is to securely encrypt biomedical images based on the Arneodo chaotic system with the lowest FO value using the Nvidia Jetson Nano development board. However, though the lowest FO system offers enhanced security in biomedical image encryption due to its richer dynamic behaviors, it necessitates careful consideration of the trade-off between high memory requirements and increasing complexity in encryption algorithms. Within the scope of the study, a novel random number generator (RNG) is designed using the FOAR chaotic system. The randomness of the random numbers is proven by using internationally accepted NIST 800-22 and ENT test suites. A biomedical image encryption application is developed using pseudo-random numbers. The images obtained as a result of the application are evaluated with tests such as histogram, correlation, differential attack, and entropy analyses. As a result of the study, it has been shown that encryption and decryption of biomedical images can be successfully performed on a mobile Nvidia Jetson Nano development card in a secure and fast manner.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="84fd5b8230cc84b65ac6a0f166d9e7d3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117861254,"asset_id":123428043,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117861254/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="123428043"><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="123428043"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123428043; 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$(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="111257529"><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/111257529/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1"><img alt="Research paper thumbnail of Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması" class="work-thumbnail" src="https://attachments.academia-assets.com/108842854/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/111257529/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1">Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması</a></div><div class="wp-workCard_item"><span>Uluslararası mühendislik araştırma ve geliştirme dergisi</span><span>, Jul 31, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Classification of network traffic not only contributes to improving the quality of network servic...</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">Classification of network traffic not only contributes to improving the quality of network services of institutions, but also helps to protect important data. Machine learning algorithms are frequently used in the classification of network traffic, since port-based and load-based classification processes are insufficient in encrypted networks. In this study, VPN and Tor network traffic combined in the Darknet category was classified with the Gradient Boosting Algorithm. 70% of the dataset is reserved for training and 30% for testing. 10 fold cross validation was applied in the training set. Network Flows in 8 different categories: Audio-Streaming, Browsing, Chat, E-mail, P2P, File Transfer, Video-Streaming and VOIP were classified with 99.8% accuracy. The proposed method automated the process of network analysis from the Darknet. It enabled organizations to protect the important data with high accuracy in a short time.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6982a7d450e8fea8d9c161fd9c59f15" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108842854,"asset_id":111257529,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108842854/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="111257529"><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="111257529"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111257529; 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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="111257528"><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/111257528/Keyword_Extraction_for_Search_Engine_Optimization_Using_Latent_Semantic_Analysis"><img alt="Research paper thumbnail of Keyword Extraction for Search Engine Optimization Using Latent Semantic Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/108842815/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/111257528/Keyword_Extraction_for_Search_Engine_Optimization_Using_Latent_Semantic_Analysis">Keyword Extraction for Search Engine Optimization Using Latent Semantic Analysis</a></div><div class="wp-workCard_item"><span>Politeknik dergisi</span><span>, Jun 1, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Bu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Horasan F., "Keyword ext...</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">Bu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Horasan F., "Keyword extraction for search engine optimization using latent semantic analysis",</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="082debdb4501c95b5df1d034d7dd14a5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108842815,"asset_id":111257528,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108842815/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="111257528"><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="111257528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111257528; 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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="111257527"><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/111257527/Latent_semantic_analysis_via_truncated_ULV_decomposition"><img alt="Research paper thumbnail of Latent semantic analysis via truncated ULV decomposition" 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/111257527/Latent_semantic_analysis_via_truncated_ULV_decomposition">Latent semantic analysis via truncated ULV decomposition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Latent semantic analysis (LSA) usually uses the singular value decomposition (SVD) of the term-do...</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">Latent semantic analysis (LSA) usually uses the singular value decomposition (SVD) of the term-document matrix for discovering the latent relationships within the document collection. With the SVD, by disregarding the smaller singular values of the term-document matrix a vector space cleaned from noises that distort the meaning is obtained. The latent semantic structure of the terms and documents is obtained by examining the relationship of representative vectors in the vector space. However, the computational time of re-computing or updating the SVD of the term-document is high when adding new terms and/or documents to pre-existing document collection. Thus, the need a method not only has low computational complexity but also creates the correct semantic structure when updating the latent semantic structure is arisen. This study shows that the truncated ULV decomposition is a good alternative to the SVD in LSA modelling about cost and producing the correct semantic structure.</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="111257527"><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="111257527"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111257527; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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Bu araştırmada çoğu damgalama tekniğinde tercih edilen Tekil Değer Ayrışımı (TDA) yerine, boyut indirgeme tabanlı Kesik-TDA tekniği kullanılmıştır. Önerilen bu teknik Ayrık Dalgacık Dönüşümü (ADD) ile birlikte kullanılmıştır. Temel TDA-ADD tabanlı yönteme göre önerilen yöntemin histogram eşitleme dışında tüm olası saldırılara karşı algılanamazlık ve dayanıklılık performanslarında ilerleme kaydettiği gözlenmiştir. 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</script> <div class="js-work-strip profile--work_container" data-work-id="91135800"><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/91135800/Dimension_Reduction_Based_Robust_Digital_Image_Watermarking_Using_Truncated_Singular_Value_Decomposition_and_Discrete_Wavelet_Transform"><img alt="Research paper thumbnail of Dimension Reduction Based Robust Digital Image Watermarking Using Truncated Singular Value Decomposition and Discrete Wavelet Transform" 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/91135800/Dimension_Reduction_Based_Robust_Digital_Image_Watermarking_Using_Truncated_Singular_Value_Decomposition_and_Discrete_Wavelet_Transform">Dimension Reduction Based Robust Digital Image Watermarking Using Truncated Singular Value Decomposition and Discrete Wavelet Transform</a></div><div class="wp-workCard_item"><span>Afyon Kocatepe University Journal of Sciences and Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Telif hakkı koruma, kimlik doğrulama, parmak izi, içerik etiketleme gibi alanlarda kullanılan dam...</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">Telif hakkı koruma, kimlik doğrulama, parmak izi, içerik etiketleme gibi alanlarda kullanılan damgalama tekniklerinde genel olarak sinyal işleme dönüşümleri ve matematiksel teknikler kullanılır. Bu araştırmada çoğu damgalama tekniğinde tercih edilen Tekil Değer Ayrışımı (TDA) yerine, boyut indirgeme tabanlı Kesik-TDA tekniği kullanılmıştır. Önerilen bu teknik Ayrık Dalgacık Dönüşümü (ADD) ile birlikte kullanılmıştır. Temel TDA-ADD tabanlı yönteme göre önerilen yöntemin histogram eşitleme dışında tüm olası saldırılara karşı algılanamazlık ve dayanıklılık performanslarında ilerleme kaydettiği gözlenmiştir. Önerilen şemanın farklı matris ayrışımı ve sinyal işleme dönüşümlerinin kullanıldığı alternatif damgalama şemalarına yön vereceği tahmin edilmektedir.</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="91135800"><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="91135800"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91135800; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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} }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91135797"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/91135797/Alternatif_d%C3%BC%C5%9F%C3%BCk_rankl%C4%B1_matris_ayr%C4%B1%C5%9F%C4%B1m%C4%B1_ile_gizli_anlamsal_dizinleme"><img alt="Research paper thumbnail of Alternatif düşük ranklı matris ayrışımı ile gizli anlamsal dizinleme" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/91135797/Alternatif_d%C3%BC%C5%9F%C3%BCk_rankl%C4%B1_matris_ayr%C4%B1%C5%9F%C4%B1m%C4%B1_ile_gizli_anlamsal_dizinleme">Alternatif düşük ranklı matris ayrışımı ile gizli anlamsal dizinleme</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tez (Doktora) -- Kırıkkale Üniversitesiref. no : 10199884119916</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91135797"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91135797"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91135797; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91135797]").text(description); $(".js-view-count[data-work-id=91135797]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91135797; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91135797']"); 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); 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</script> <div class="js-work-strip profile--work_container" data-work-id="91135791"><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/91135791/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1"><img alt="Research paper thumbnail of Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması" class="work-thumbnail" src="https://attachments.academia-assets.com/94508713/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/91135791/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1">Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması</a></div><div class="wp-workCard_item"><span>Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ağ trafiğinin sınıflandırılması kurumların ağ hizmetlerinin kalitesinin artırılmasına katkı sağla...</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">Ağ trafiğinin sınıflandırılması kurumların ağ hizmetlerinin kalitesinin artırılmasına katkı sağlamasının yanında önemli verilerinin korunmasına da yardımcı olmaktadır. Ağ trafiğinin sınıflandırmada, port tabanlı ve yük tabanlı sınıflandırma işlemlerinin şifreli ağlarda yetersiz kalması nedeniyle makine öğrenmesi algoritmaları sıklıkla kullanılmaktadır. Bu çalışmada, Darknet kategorisinde birleştirilen VPN ve Tor ağ trafiği Gradyan Artırma Algoritması ile sınıflandırılmıştır. Veri setinin %70’i eğitim, %30’u test için ayrılmıştır. Eğitim setinde 10 kat çapraz doğrulama uygulanmıştır. 8 farklı kategoride ağ akışları: Ses Akışı, Tarama, Sohbet, E-posta, P2P, Dosya Aktarımı, Video Akışı ve VOIP %99,8 doğrulukla sınıflandırıldı. Önerilen yöntem, karanlık ağdan ağ analizi sürecini otomatikleştirmiştir. Kuruluşların önemli verilerini kısa sürede yüksek doğrulukla korumasını sağlamaktadır.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="313df1f335604997ee7b1da85c21b0d0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94508713,"asset_id":91135791,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94508713/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="91135791"><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="91135791"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91135791; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91135791]").text(description); $(".js-view-count[data-work-id=91135791]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91135791; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91135791']"); 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: "313df1f335604997ee7b1da85c21b0d0" } } $('.js-work-strip[data-work-id=91135791]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91135791,"title":"Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması","internal_url":"https://www.academia.edu/91135791/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1","owner_id":12024395,"coauthors_can_edit":true,"owner":{"id":12024395,"first_name":"Fahrettin","middle_initials":null,"last_name":"Horasan","page_name":"FahrettinHorasan","domain_name":"kirikkaleturkey","created_at":"2014-05-13T23:05:25.230-07:00","display_name":"Fahrettin Horasan","url":"https://kirikkaleturkey.academia.edu/FahrettinHorasan"},"attachments":[{"id":94508713,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/94508713/thumbnails/1.jpg","file_name":"2431954.pdf","download_url":"https://www.academia.edu/attachments/94508713/download_file","bulk_download_file_name":"Gradyan_Artirma_Algoritmasi_ile_Karanlik.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/94508713/2431954-libre.pdf?1668866155=\u0026response-content-disposition=attachment%3B+filename%3DGradyan_Artirma_Algoritmasi_ile_Karanlik.pdf\u0026Expires=1741114107\u0026Signature=gAQbjtVawo6R5LugL~Dq71PpKHmZ9TjfCic1r4tBbPgrgx8en8QUdQClONUiCM3-tVQ3U00xb6wg0Iv-dWeNHWPZbk72Tt9P~Z0g7K-lj95J4zt7dVuVQc7jE7mvivL9Sku7bDN5F8o~VGFUVaNpXZTE~AMdOBuHTmRg~8HlG6HA63Vq9hX0KCTOOQ9WOPnobOI4gyx916bpTeh6FnLKXA6a~9ij3eAFAEvTEG7vw7y7CsN2VzgEotOQiMRt~ZjnuwdVN233IdtEOsPEyUTTbWBZAPKijD6NPBS2FQ83sdIGDdA5~xckyJ7XQWarDEZDKGF72nmM3QZf2dHUtGEASw__\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="91135772"><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/91135772/DWT_SVD_Based_Watermarking_for_High_Resolution_Medical_Holographic_Images"><img alt="Research paper thumbnail of DWT-SVD Based Watermarking for High-Resolution Medical Holographic Images" class="work-thumbnail" src="https://attachments.academia-assets.com/94508667/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/91135772/DWT_SVD_Based_Watermarking_for_High_Resolution_Medical_Holographic_Images">DWT-SVD Based Watermarking for High-Resolution Medical Holographic Images</a></div><div class="wp-workCard_item"><span>Complexity</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Watermarking is one of the most common techniques used to protect data’s authenticity, integrity,...</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">Watermarking is one of the most common techniques used to protect data’s authenticity, integrity, and security. The obfuscation in the frequency domain used in the watermarking method makes the watermarking stronger than the obfuscation in the spatial domain. It occupies an important place in watermarking works in imperceptibility, capacity, and robustness. Finding the optimal location to hide the watermarking is one of the most challenging tasks in these methods and affects the method’s performance. In this article, sample identification information is processed with the method of watermaking on the hiding environment created by using a chaos-based random number generator on biomedical data to provide solutions to problems such as visual attack, identity theft, and information confusion. In order to obtain biomedical data, a lensless digital in-line holographic microscopy (DIHM) setup was designed, and holographic data of human blood and cancer cell lines, which are widely used in ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d5b6118708a8c8b7523f90362455b967" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94508667,"asset_id":91135772,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94508667/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="91135772"><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="91135772"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91135772; 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İnternetin sunduğu başlıca hizmetlerden biri olan web sitelernde amac hedef kitlelere ulaşmaktır. Kişisel ya da kurumsal web sitelerinin bu hedefi gerçekleştirmesinde arama motorlarının payı büyüktür. Arama motorlarnın etkin bir şekilde yardımcı olabilmesi, hazırlanan web siteleri için &quot;arama motoru optimizasyonu&quot; işleminin en uygun bir şekilde yapılmasına bağlıdır. Bu işlemin gerçekleştirilmesi için optimizasyon işlemini yapacak personelin veya destek alınan firmanın personelinin kullanılan web tekonolojilerinin yanında hazırlanan içerik i...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cf1f387e8c639aac11e57f956a2815be" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90472593,"asset_id":85907355,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90472593/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="85907355"><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="85907355"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85907355; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=74513844]").text(description); $(".js-view-count[data-work-id=74513844]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 74513844; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='74513844']"); 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" } } $('.js-work-strip[data-work-id=74513844]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":74513844,"title":"Latent Semantic Indexing-Based Hybrid Collaborative Filtering for Recommender Systems","internal_url":"https://www.academia.edu/74513844/Latent_Semantic_Indexing_Based_Hybrid_Collaborative_Filtering_for_Recommender_Systems","owner_id":12024395,"coauthors_can_edit":true,"owner":{"id":12024395,"first_name":"Fahrettin","middle_initials":null,"last_name":"Horasan","page_name":"FahrettinHorasan","domain_name":"kirikkaleturkey","created_at":"2014-05-13T23:05:25.230-07:00","display_name":"Fahrettin Horasan","url":"https://kirikkaleturkey.academia.edu/FahrettinHorasan"},"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="64407776"><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/64407776/Decision_Trees_in_Large_Data_Sets"><img alt="Research paper thumbnail of Decision Trees in Large Data Sets" 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/64407776/Decision_Trees_in_Large_Data_Sets">Decision Trees in Large Data Sets</a></div><div class="wp-workCard_item"><span>Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Data mining is the process of obtaining information, which is used to identify and define the rel...</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">Data mining is the process of obtaining information, which is used to identify and define the relationships between data of different qualities. One of the important problems encountered in this process is the classification process in large data sets. Extensive research has been done to find solutions to this classification problem and different solution methods have been introduced. Some decision tree algorithms are among the structures that can be used effectively in this field. In this article, various decision tree structures and algorithms used for classification process in large data sets are discussed. Along with the definitions of the algorithms, the similarities and existing differences between them were determined, their advantages and disadvantages were investigated.</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="64407776"><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="64407776"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64407776; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64407776]").text(description); $(".js-view-count[data-work-id=64407776]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64407776; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64407776']"); 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" } } $('.js-work-strip[data-work-id=64407776]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64407776,"title":"Decision Trees in Large Data Sets","internal_url":"https://www.academia.edu/64407776/Decision_Trees_in_Large_Data_Sets","owner_id":12024395,"coauthors_can_edit":true,"owner":{"id":12024395,"first_name":"Fahrettin","middle_initials":null,"last_name":"Horasan","page_name":"FahrettinHorasan","domain_name":"kirikkaleturkey","created_at":"2014-05-13T23:05:25.230-07:00","display_name":"Fahrettin Horasan","url":"https://kirikkaleturkey.academia.edu/FahrettinHorasan"},"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="64407775"><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/64407775/LSTM_Network_based_Sentiment_Analysis_for_Customer_Reviews"><img alt="Research paper thumbnail of LSTM Network based Sentiment Analysis for Customer Reviews" class="work-thumbnail" src="https://attachments.academia-assets.com/76459622/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/64407775/LSTM_Network_based_Sentiment_Analysis_for_Customer_Reviews">LSTM Network based Sentiment Analysis for Customer Reviews</a></div><div class="wp-workCard_item"><span>Journal of Polytechnic</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Continuously increasing data bring new problems and problems usually reveal new research areas. 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">Continuously increasing data bring new problems and problems usually reveal new research areas. One of the new areas is Sentiment Analysis. This field has some difficulties. The fact that people have complex sentiments is the main cause of the difficulty, but this has not prevented the progress of the studies in this field. Sentiment analysis is generally used to obtain information about persons by collecting their texts or expressions. Sentiment analysis can sometimes bring serious benefits. In this study, with singular tag-plural class approach, a binary classification was performed. An LSTM network and several machine learning models were tested. The dataset collected in Turkish, and Stanford Large Movie Reviews datasets were used in this study. Due to the noise in the dataset, the Zemberek NLP Library for Turkic Languages and Regular Expression techniques were used to normalize and clean texts, later, the data were transformed into vector sequences. The preprocessing process mad...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="11a3c4151313cd123daa609d052a4134" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":76459622,"asset_id":64407775,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/76459622/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="64407775"><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="64407775"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64407775; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64407775]").text(description); $(".js-view-count[data-work-id=64407775]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64407775; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64407775']"); 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: "11a3c4151313cd123daa609d052a4134" } } $('.js-work-strip[data-work-id=64407775]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64407775,"title":"LSTM Network based Sentiment Analysis for Customer Reviews","internal_url":"https://www.academia.edu/64407775/LSTM_Network_based_Sentiment_Analysis_for_Customer_Reviews","owner_id":12024395,"coauthors_can_edit":true,"owner":{"id":12024395,"first_name":"Fahrettin","middle_initials":null,"last_name":"Horasan","page_name":"FahrettinHorasan","domain_name":"kirikkaleturkey","created_at":"2014-05-13T23:05:25.230-07:00","display_name":"Fahrettin Horasan","url":"https://kirikkaleturkey.academia.edu/FahrettinHorasan"},"attachments":[{"id":76459622,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/76459622/thumbnails/1.jpg","file_name":"1459231.pdf","download_url":"https://www.academia.edu/attachments/76459622/download_file","bulk_download_file_name":"LSTM_Network_based_Sentiment_Analysis_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/76459622/1459231-libre.pdf?1639635023=\u0026response-content-disposition=attachment%3B+filename%3DLSTM_Network_based_Sentiment_Analysis_fo.pdf\u0026Expires=1741114107\u0026Signature=ZXcxThNfPZsQvz81T4JGSMH-IIPXBm1MOrVpYCZH2bS07G2ji0lv9Fk-k9ddQ4oxAhbdpqAFaetWFYTrepybTLKSmgH3jwvB33rD3gfRcyADzVjXCzdl6BbSwiG2f5kj~IDslqjTlbrpxVLEBZSsTED1FBss1wSE-NtelQtEgdO7x9u5pGptOyCToTHjq1i7CSgNvpzHDCLuzFc4QKPAzblS1PSVp9l9Lo6RPU5RcZKDsYBH8lwnIDkzwPv-d3R1OEPAvTWqe-wFBWXg8CNSypaAS2K5NIugQ102Yogk0lJ8kwHEMPXSYBzgPAwlyteDtp7-b8N0SJVEEz0OUXCH~Q__\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="11205055" id="papers"><div class="js-work-strip profile--work_container" data-work-id="123428043"><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/123428043/Secure_Encryption_of_Biomedical_Images_Based_on_Arneodo_Chaotic_System_with_the_Lowest_Fractional_Order_Value"><img alt="Research paper thumbnail of Secure Encryption of Biomedical Images Based on Arneodo Chaotic System with the Lowest Fractional-Order Value" class="work-thumbnail" src="https://attachments.academia-assets.com/117861254/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/123428043/Secure_Encryption_of_Biomedical_Images_Based_on_Arneodo_Chaotic_System_with_the_Lowest_Fractional_Order_Value">Secure Encryption of Biomedical Images Based on Arneodo Chaotic System with the Lowest Fractional-Order Value</a></div><div class="wp-workCard_item"><span>Electronics</span><span>, May 29, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared ...</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">Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical characteristics of the fractional-order Arneodo (FOAR) system with cubic nonlinearity is conducted. This investigation involves the analysis of phase planes, bifurcation diagrams, Lyapunov exponential spectra, and spectral entropy. Numerical studies show that the Arneodo chaotic system exhibits chaotic behavior when the lowest fractional-order (FO) value is set to 0.55. In this context, the aim is to securely encrypt biomedical images based on the Arneodo chaotic system with the lowest FO value using the Nvidia Jetson Nano development board. However, though the lowest FO system offers enhanced security in biomedical image encryption due to its richer dynamic behaviors, it necessitates careful consideration of the trade-off between high memory requirements and increasing complexity in encryption algorithms. Within the scope of the study, a novel random number generator (RNG) is designed using the FOAR chaotic system. The randomness of the random numbers is proven by using internationally accepted NIST 800-22 and ENT test suites. A biomedical image encryption application is developed using pseudo-random numbers. The images obtained as a result of the application are evaluated with tests such as histogram, correlation, differential attack, and entropy analyses. As a result of the study, it has been shown that encryption and decryption of biomedical images can be successfully performed on a mobile Nvidia Jetson Nano development card in a secure and fast manner.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="84fd5b8230cc84b65ac6a0f166d9e7d3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":117861254,"asset_id":123428043,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/117861254/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="123428043"><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="123428043"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123428043; 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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="111257529"><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/111257529/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1"><img alt="Research paper thumbnail of Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması" class="work-thumbnail" src="https://attachments.academia-assets.com/108842854/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/111257529/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1">Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması</a></div><div class="wp-workCard_item"><span>Uluslararası mühendislik araştırma ve geliştirme dergisi</span><span>, Jul 31, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Classification of network traffic not only contributes to improving the quality of network servic...</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">Classification of network traffic not only contributes to improving the quality of network services of institutions, but also helps to protect important data. Machine learning algorithms are frequently used in the classification of network traffic, since port-based and load-based classification processes are insufficient in encrypted networks. In this study, VPN and Tor network traffic combined in the Darknet category was classified with the Gradient Boosting Algorithm. 70% of the dataset is reserved for training and 30% for testing. 10 fold cross validation was applied in the training set. Network Flows in 8 different categories: Audio-Streaming, Browsing, Chat, E-mail, P2P, File Transfer, Video-Streaming and VOIP were classified with 99.8% accuracy. The proposed method automated the process of network analysis from the Darknet. It enabled organizations to protect the important data with high accuracy in a short time.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d6982a7d450e8fea8d9c161fd9c59f15" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108842854,"asset_id":111257529,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108842854/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="111257529"><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="111257529"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111257529; 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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="111257528"><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/111257528/Keyword_Extraction_for_Search_Engine_Optimization_Using_Latent_Semantic_Analysis"><img alt="Research paper thumbnail of Keyword Extraction for Search Engine Optimization Using Latent Semantic Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/108842815/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/111257528/Keyword_Extraction_for_Search_Engine_Optimization_Using_Latent_Semantic_Analysis">Keyword Extraction for Search Engine Optimization Using Latent Semantic Analysis</a></div><div class="wp-workCard_item"><span>Politeknik dergisi</span><span>, Jun 1, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Bu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Horasan F., "Keyword ext...</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">Bu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Horasan F., "Keyword extraction for search engine optimization using latent semantic analysis",</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="082debdb4501c95b5df1d034d7dd14a5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":108842815,"asset_id":111257528,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/108842815/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="111257528"><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="111257528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111257528; 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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="111257527"><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/111257527/Latent_semantic_analysis_via_truncated_ULV_decomposition"><img alt="Research paper thumbnail of Latent semantic analysis via truncated ULV decomposition" 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/111257527/Latent_semantic_analysis_via_truncated_ULV_decomposition">Latent semantic analysis via truncated ULV decomposition</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Latent semantic analysis (LSA) usually uses the singular value decomposition (SVD) of the term-do...</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">Latent semantic analysis (LSA) usually uses the singular value decomposition (SVD) of the term-document matrix for discovering the latent relationships within the document collection. With the SVD, by disregarding the smaller singular values of the term-document matrix a vector space cleaned from noises that distort the meaning is obtained. The latent semantic structure of the terms and documents is obtained by examining the relationship of representative vectors in the vector space. However, the computational time of re-computing or updating the SVD of the term-document is high when adding new terms and/or documents to pre-existing document collection. Thus, the need a method not only has low computational complexity but also creates the correct semantic structure when updating the latent semantic structure is arisen. This study shows that the truncated ULV decomposition is a good alternative to the SVD in LSA modelling about cost and producing the correct semantic structure.</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="111257527"><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="111257527"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 111257527; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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Bu araştırmada çoğu damgalama tekniğinde tercih edilen Tekil Değer Ayrışımı (TDA) yerine, boyut indirgeme tabanlı Kesik-TDA tekniği kullanılmıştır. Önerilen bu teknik Ayrık Dalgacık Dönüşümü (ADD) ile birlikte kullanılmıştır. Temel TDA-ADD tabanlı yönteme göre önerilen yöntemin histogram eşitleme dışında tüm olası saldırılara karşı algılanamazlık ve dayanıklılık performanslarında ilerleme kaydettiği gözlenmiştir. 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</script> <div class="js-work-strip profile--work_container" data-work-id="91135800"><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/91135800/Dimension_Reduction_Based_Robust_Digital_Image_Watermarking_Using_Truncated_Singular_Value_Decomposition_and_Discrete_Wavelet_Transform"><img alt="Research paper thumbnail of Dimension Reduction Based Robust Digital Image Watermarking Using Truncated Singular Value Decomposition and Discrete Wavelet Transform" 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/91135800/Dimension_Reduction_Based_Robust_Digital_Image_Watermarking_Using_Truncated_Singular_Value_Decomposition_and_Discrete_Wavelet_Transform">Dimension Reduction Based Robust Digital Image Watermarking Using Truncated Singular Value Decomposition and Discrete Wavelet Transform</a></div><div class="wp-workCard_item"><span>Afyon Kocatepe University Journal of Sciences and Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Telif hakkı koruma, kimlik doğrulama, parmak izi, içerik etiketleme gibi alanlarda kullanılan dam...</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">Telif hakkı koruma, kimlik doğrulama, parmak izi, içerik etiketleme gibi alanlarda kullanılan damgalama tekniklerinde genel olarak sinyal işleme dönüşümleri ve matematiksel teknikler kullanılır. Bu araştırmada çoğu damgalama tekniğinde tercih edilen Tekil Değer Ayrışımı (TDA) yerine, boyut indirgeme tabanlı Kesik-TDA tekniği kullanılmıştır. Önerilen bu teknik Ayrık Dalgacık Dönüşümü (ADD) ile birlikte kullanılmıştır. Temel TDA-ADD tabanlı yönteme göre önerilen yöntemin histogram eşitleme dışında tüm olası saldırılara karşı algılanamazlık ve dayanıklılık performanslarında ilerleme kaydettiği gözlenmiştir. Önerilen şemanın farklı matris ayrışımı ve sinyal işleme dönüşümlerinin kullanıldığı alternatif damgalama şemalarına yön vereceği tahmin edilmektedir.</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="91135800"><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="91135800"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91135800; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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} }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91135797"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/91135797/Alternatif_d%C3%BC%C5%9F%C3%BCk_rankl%C4%B1_matris_ayr%C4%B1%C5%9F%C4%B1m%C4%B1_ile_gizli_anlamsal_dizinleme"><img alt="Research paper thumbnail of Alternatif düşük ranklı matris ayrışımı ile gizli anlamsal dizinleme" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/91135797/Alternatif_d%C3%BC%C5%9F%C3%BCk_rankl%C4%B1_matris_ayr%C4%B1%C5%9F%C4%B1m%C4%B1_ile_gizli_anlamsal_dizinleme">Alternatif düşük ranklı matris ayrışımı ile gizli anlamsal dizinleme</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tez (Doktora) -- Kırıkkale Üniversitesiref. no : 10199884119916</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91135797"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91135797"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91135797; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91135797]").text(description); $(".js-view-count[data-work-id=91135797]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91135797; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91135797']"); 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); 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</script> <div class="js-work-strip profile--work_container" data-work-id="91135791"><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/91135791/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1"><img alt="Research paper thumbnail of Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması" class="work-thumbnail" src="https://attachments.academia-assets.com/94508713/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/91135791/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1">Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması</a></div><div class="wp-workCard_item"><span>Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ağ trafiğinin sınıflandırılması kurumların ağ hizmetlerinin kalitesinin artırılmasına katkı sağla...</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">Ağ trafiğinin sınıflandırılması kurumların ağ hizmetlerinin kalitesinin artırılmasına katkı sağlamasının yanında önemli verilerinin korunmasına da yardımcı olmaktadır. Ağ trafiğinin sınıflandırmada, port tabanlı ve yük tabanlı sınıflandırma işlemlerinin şifreli ağlarda yetersiz kalması nedeniyle makine öğrenmesi algoritmaları sıklıkla kullanılmaktadır. Bu çalışmada, Darknet kategorisinde birleştirilen VPN ve Tor ağ trafiği Gradyan Artırma Algoritması ile sınıflandırılmıştır. Veri setinin %70’i eğitim, %30’u test için ayrılmıştır. Eğitim setinde 10 kat çapraz doğrulama uygulanmıştır. 8 farklı kategoride ağ akışları: Ses Akışı, Tarama, Sohbet, E-posta, P2P, Dosya Aktarımı, Video Akışı ve VOIP %99,8 doğrulukla sınıflandırıldı. Önerilen yöntem, karanlık ağdan ağ analizi sürecini otomatikleştirmiştir. Kuruluşların önemli verilerini kısa sürede yüksek doğrulukla korumasını sağlamaktadır.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="313df1f335604997ee7b1da85c21b0d0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94508713,"asset_id":91135791,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94508713/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="91135791"><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="91135791"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91135791; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91135791]").text(description); $(".js-view-count[data-work-id=91135791]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91135791; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91135791']"); 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: "313df1f335604997ee7b1da85c21b0d0" } } $('.js-work-strip[data-work-id=91135791]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91135791,"title":"Gradyan Artırma Algoritması ile Karanlık Ağ Web Trafiği Sınıflandırması","internal_url":"https://www.academia.edu/91135791/Gradyan_Art%C4%B1rma_Algoritmas%C4%B1_ile_Karanl%C4%B1k_A%C4%9F_Web_Trafi%C4%9Fi_S%C4%B1n%C4%B1fland%C4%B1rmas%C4%B1","owner_id":12024395,"coauthors_can_edit":true,"owner":{"id":12024395,"first_name":"Fahrettin","middle_initials":null,"last_name":"Horasan","page_name":"FahrettinHorasan","domain_name":"kirikkaleturkey","created_at":"2014-05-13T23:05:25.230-07:00","display_name":"Fahrettin Horasan","url":"https://kirikkaleturkey.academia.edu/FahrettinHorasan"},"attachments":[{"id":94508713,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/94508713/thumbnails/1.jpg","file_name":"2431954.pdf","download_url":"https://www.academia.edu/attachments/94508713/download_file","bulk_download_file_name":"Gradyan_Artirma_Algoritmasi_ile_Karanlik.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/94508713/2431954-libre.pdf?1668866155=\u0026response-content-disposition=attachment%3B+filename%3DGradyan_Artirma_Algoritmasi_ile_Karanlik.pdf\u0026Expires=1741114107\u0026Signature=gAQbjtVawo6R5LugL~Dq71PpKHmZ9TjfCic1r4tBbPgrgx8en8QUdQClONUiCM3-tVQ3U00xb6wg0Iv-dWeNHWPZbk72Tt9P~Z0g7K-lj95J4zt7dVuVQc7jE7mvivL9Sku7bDN5F8o~VGFUVaNpXZTE~AMdOBuHTmRg~8HlG6HA63Vq9hX0KCTOOQ9WOPnobOI4gyx916bpTeh6FnLKXA6a~9ij3eAFAEvTEG7vw7y7CsN2VzgEotOQiMRt~ZjnuwdVN233IdtEOsPEyUTTbWBZAPKijD6NPBS2FQ83sdIGDdA5~xckyJ7XQWarDEZDKGF72nmM3QZf2dHUtGEASw__\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="91135772"><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/91135772/DWT_SVD_Based_Watermarking_for_High_Resolution_Medical_Holographic_Images"><img alt="Research paper thumbnail of DWT-SVD Based Watermarking for High-Resolution Medical Holographic Images" class="work-thumbnail" src="https://attachments.academia-assets.com/94508667/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/91135772/DWT_SVD_Based_Watermarking_for_High_Resolution_Medical_Holographic_Images">DWT-SVD Based Watermarking for High-Resolution Medical Holographic Images</a></div><div class="wp-workCard_item"><span>Complexity</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Watermarking is one of the most common techniques used to protect data’s authenticity, integrity,...</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">Watermarking is one of the most common techniques used to protect data’s authenticity, integrity, and security. The obfuscation in the frequency domain used in the watermarking method makes the watermarking stronger than the obfuscation in the spatial domain. It occupies an important place in watermarking works in imperceptibility, capacity, and robustness. Finding the optimal location to hide the watermarking is one of the most challenging tasks in these methods and affects the method’s performance. In this article, sample identification information is processed with the method of watermaking on the hiding environment created by using a chaos-based random number generator on biomedical data to provide solutions to problems such as visual attack, identity theft, and information confusion. In order to obtain biomedical data, a lensless digital in-line holographic microscopy (DIHM) setup was designed, and holographic data of human blood and cancer cell lines, which are widely used in ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d5b6118708a8c8b7523f90362455b967" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94508667,"asset_id":91135772,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94508667/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="91135772"><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="91135772"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91135772; 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İnternetin sunduğu başlıca hizmetlerden biri olan web sitelernde amac hedef kitlelere ulaşmaktır. Kişisel ya da kurumsal web sitelerinin bu hedefi gerçekleştirmesinde arama motorlarının payı büyüktür. Arama motorlarnın etkin bir şekilde yardımcı olabilmesi, hazırlanan web siteleri için &quot;arama motoru optimizasyonu&quot; işleminin en uygun bir şekilde yapılmasına bağlıdır. Bu işlemin gerçekleştirilmesi için optimizasyon işlemini yapacak personelin veya destek alınan firmanın personelinin kullanılan web tekonolojilerinin yanında hazırlanan içerik i...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cf1f387e8c639aac11e57f956a2815be" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":90472593,"asset_id":85907355,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/90472593/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="85907355"><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="85907355"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 85907355; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=74513844]").text(description); $(".js-view-count[data-work-id=74513844]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 74513844; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='74513844']"); 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" } } $('.js-work-strip[data-work-id=74513844]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":74513844,"title":"Latent Semantic Indexing-Based Hybrid Collaborative Filtering for Recommender Systems","internal_url":"https://www.academia.edu/74513844/Latent_Semantic_Indexing_Based_Hybrid_Collaborative_Filtering_for_Recommender_Systems","owner_id":12024395,"coauthors_can_edit":true,"owner":{"id":12024395,"first_name":"Fahrettin","middle_initials":null,"last_name":"Horasan","page_name":"FahrettinHorasan","domain_name":"kirikkaleturkey","created_at":"2014-05-13T23:05:25.230-07:00","display_name":"Fahrettin Horasan","url":"https://kirikkaleturkey.academia.edu/FahrettinHorasan"},"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="64407776"><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/64407776/Decision_Trees_in_Large_Data_Sets"><img alt="Research paper thumbnail of Decision Trees in Large Data Sets" 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/64407776/Decision_Trees_in_Large_Data_Sets">Decision Trees in Large Data Sets</a></div><div class="wp-workCard_item"><span>Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Data mining is the process of obtaining information, which is used to identify and define the rel...</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">Data mining is the process of obtaining information, which is used to identify and define the relationships between data of different qualities. One of the important problems encountered in this process is the classification process in large data sets. Extensive research has been done to find solutions to this classification problem and different solution methods have been introduced. Some decision tree algorithms are among the structures that can be used effectively in this field. In this article, various decision tree structures and algorithms used for classification process in large data sets are discussed. Along with the definitions of the algorithms, the similarities and existing differences between them were determined, their advantages and disadvantages were investigated.</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="64407776"><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="64407776"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64407776; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64407776]").text(description); $(".js-view-count[data-work-id=64407776]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64407776; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='64407776']"); 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" } } $('.js-work-strip[data-work-id=64407776]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":64407776,"title":"Decision Trees in Large Data Sets","internal_url":"https://www.academia.edu/64407776/Decision_Trees_in_Large_Data_Sets","owner_id":12024395,"coauthors_can_edit":true,"owner":{"id":12024395,"first_name":"Fahrettin","middle_initials":null,"last_name":"Horasan","page_name":"FahrettinHorasan","domain_name":"kirikkaleturkey","created_at":"2014-05-13T23:05:25.230-07:00","display_name":"Fahrettin Horasan","url":"https://kirikkaleturkey.academia.edu/FahrettinHorasan"},"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="64407775"><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/64407775/LSTM_Network_based_Sentiment_Analysis_for_Customer_Reviews"><img alt="Research paper thumbnail of LSTM Network based Sentiment Analysis for Customer Reviews" class="work-thumbnail" src="https://attachments.academia-assets.com/76459622/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/64407775/LSTM_Network_based_Sentiment_Analysis_for_Customer_Reviews">LSTM Network based Sentiment Analysis for Customer Reviews</a></div><div class="wp-workCard_item"><span>Journal of Polytechnic</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Continuously increasing data bring new problems and problems usually reveal new research areas. 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">Continuously increasing data bring new problems and problems usually reveal new research areas. One of the new areas is Sentiment Analysis. This field has some difficulties. The fact that people have complex sentiments is the main cause of the difficulty, but this has not prevented the progress of the studies in this field. Sentiment analysis is generally used to obtain information about persons by collecting their texts or expressions. Sentiment analysis can sometimes bring serious benefits. In this study, with singular tag-plural class approach, a binary classification was performed. An LSTM network and several machine learning models were tested. The dataset collected in Turkish, and Stanford Large Movie Reviews datasets were used in this study. Due to the noise in the dataset, the Zemberek NLP Library for Turkic Languages and Regular Expression techniques were used to normalize and clean texts, later, the data were transformed into vector sequences. The preprocessing process mad...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="11a3c4151313cd123daa609d052a4134" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":76459622,"asset_id":64407775,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/76459622/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="64407775"><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="64407775"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64407775; 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