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<span class="author-name" >Xie Shiwen </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Xie Yongfang </span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;36313532" target="_blank">Evaluation of Zinc Roughing Process Based on Weighted Density Clustering</a></div> <div class="InBook"><div class="chapter-in">In:</div> <div class="authors"> </div> <div class="booktitle"><a href="/gui2/?mode=browse&params=publication;36313533" target="_blank">2025 37th Chinese Control and Decision Conference (CCDC) </a></div> <div class="conference"> Konferencia helye, ideje: <span class="location">Xiamen, Kína <span class="conference-date">2025.05.16. - 2025.05.19.</span> </div> <span class="publishedAt">Piscataway (NJ): <span class="publishers">IEEE</span>, <span class="page"> pp 1592-1597 </span> <span class="year">(2025)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="10.1109/CCDC65474.2025.11090896" target="_blank" href="https://doi.org/10.1109/CCDC65474.2025.11090896"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="001556889801158" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/001556889801158"> WoS </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="105013964775" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-105013964775"> Scopus </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:black" title="https://ieeexplore.ieee.org/document/11090896/" target="_blank" href="https://ieeexplore.ieee.org/document/11090896/"> Egyéb URL </a> </span> </span> <span class="bookchapter-ids">Befoglaló link(ek):</span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <span class="isbnOrIssn"> ISBN: </span> <a style="color:black" title="9798331510565" target="_blank" href="https://www.worldcat.org/search?q=isbn%3A9798331510565"> 9798331510565 </a> </span> </span> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;36313532&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 2 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 36313532</span> | <span class="status-data status-VALIDATED"> Egyeztetett </span> <span class="long-book-mtid">Befoglaló: 36313533</span> Idéző | <span class="type-subtype">Könyvrészlet ( Konferenciaközlemény ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">WoS-XML</span> </div> <div class="lastModified">Utolsó módosítás: 2026.05.19. 18:26 Szuper Admin (admin) </div> </div> </div> JournalArticle 33447095 APPROVED true 0 false 2025-12-19T12:10:15.235+0000 2025-12-19T12:08:50.170+0000 2022-12-23T06:56:40.090+0000 true 521 Szuper Admin /api/admin/521 Admin true 2025-12-19T12:08:50.435+0000 2025-12-19T12:08:50.435+0000 false false true 24 24 /api/publicationtype/24 PublicationType 1 true 24 JournalArticle true 10000059 Article true 24 24 /api/publicationtype/24 PublicationType 1 true 24 JournalArticle /api/subtype/10000059 Szakcikk SubType 101 true 10000059 true 1 /api/category/1 Category true 1 Azzouzi, Souad An improved image clustering algorithm based on Kernel method and Tchebychev orthogonal moments true true /api/journal/10055182 REVIEWED true false 10055182 false 10055182 true 1864-5909 1864-5917 Journal FOREIGN 16 1237 1258 1237 22 2023 In this paper, we introduce a new clustering algorithm called Improved Kernel Possibilistic Fuzzy C-Means algorithm (ImKPFCM), based on the kernel method and possibilistic approach. The proposed ImKPFCM algorithm corrects several FCM, PFCM and GPFCM algorithms shortcomings, reliably detects clustering centers and allows in addition to use Euclidean distance, the employment of other more powerful additional norms able to handle various complex situations. In this study, we applied ImKPFCM algorithm as a new image clustering method on the basis of Tchebychev orthogonal moments to extract feature vectors and then compared it with FCM, PFCM and GPFCM algorithms to evaluate its performance. The comparative study results applied to several image dataset, revealed that the ImKPFCM clustering algorithm improves the clustering accuracy over the FCM, PFCM and GPFCM methods. Therefore, we conclude that the ImKPFCM algorithm is more efficient and produces satisfactory image clustering results. true 2022 true true true false false false NONE 2025-12-19 false 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Q2 false false false false 2026-03-19T12:08:50.079+0000 3 12 0 4 0 true Language 10002 /api/language/10002 Angol English true 2 true PersonAuthorship 105637128 /api/authorship/105637128 1 0.25 true false true Azzouzi Souad true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105637129 /api/authorship/105637129 2 0.25 false false false Hjouji Amal true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105637130 /api/authorship/105637130 3 0.25 false false false EL-Mekkaoui Jaouad true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PersonAuthorship 105637131 /api/authorship/105637131 4 0.25 false true false EL Khalfi Ahmed true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PublicationIdentifier 22601899 /api/publicationidentifier/22601899 PlainSource 6 /api/publicationsource/6 PublicationSourceType 10001 /api/publicationsourcetype/10001 true true true DOI DOI https://doi.org/@@@ true true 6 true 10.1007/s12065-022-00734-x https://doi.org/10.1007/s12065-022-00734-x false true PublicationIdentifier 22601898 /api/publicationidentifier/22601898 PlainSource 1 /api/publicationsource/1 PublicationSourceType 10003 /api/publicationsourcetype/10003 false true true WoS WoS https://www.webofscience.com/wos/woscc/full-record/@@@ true true 1 true IDENTICAL 000805743500001 https://www.webofscience.com/wos/woscc/full-record/000805743500001 false true Classification 10034 /api/classification/10034 true true Keyword 1011157 /api/keyword/1011157 true 1011157 true Keyword 1678533 /api/keyword/1678533 true true Keyword 1909125 /api/keyword/1909125 true true Keyword 2891181 /api/keyword/2891181 true true Keyword 2891182 /api/keyword/2891182 true true Keyword 2891183 /api/keyword/2891183 true true SjrRating 11366659 /api/sjrrating/11366659 39 0.49 journal RatingType 10002 /api/ratingtype/10002 sjr true true ClassificationExternal 1707 /api/classificationexternal/1707 true 1707 true Q2 DIRECT true true /api/publication/33447095 <div class="JournalArticle Publication long-list"> <div class="authors"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" >Azzouzi Souad ✉ </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Hjouji Amal </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >EL-Mekkaoui Jaouad </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >EL Khalfi Ahmed </span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;33447095" target="_blank">An improved image clustering algorithm based on Kernel method and Tchebychev orthogonal moments</a></div> <div> <span class="journal-title">EVOLUTIONARY INTELLIGENCE</span> <span class="journal-issn">(<a target="_blank" href="https://portal.issn.org/resource/ISSN/1864-5909">1864-5909</a> <a target="_blank" href="https://portal.issn.org/resource/ISSN/1864-5917">1864-5917</a>)</span>: <span class="journal-volume">16</span> <span class="page"> pp 1237-1258 </span> <span class="year">(2023)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:black" title="10.1007/s12065-022-00734-x" target="_blank" href="https://doi.org/10.1007/s12065-022-00734-x"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="000805743500001" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/000805743500001"> WoS </a> </span> </span> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;33447095&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 1 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 33447095</span> | <span class="status-data status-APPROVED"> Nyilvános </span> Idéző | <span class="type-subtype">Folyóiratcikk ( Szakcikk ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">WoS-XML</span> </div> <div class="lastModified">Utolsó módosítás: 2025.12.19. 13:08 Szatmári Erika (BME admin4) </div> </div></div> BookChapter 33589051 VALIDATED true Doctoral School of Applied Mathematics and Applied Informatics, O´buda University, Budapest, Hungary Comput. 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<span class="author-name" mtid="10001992"><a href="/gui2/?type=authors&mode=browse&sel=10001992" target="_blank">Levente Kovács (<span class="authorship-author-name">Kovács Levente</span> <span class="authorAux-mtmt"> Irányítástechnika</span>) </a> </span> <span class="author-affil"><span title="Óbudai Egyetem">ÓE</span>/<span title="Egyetemi Kutató és Innovációs Központ">EKIK</span>/Élettani Szabályozások Kutatóközpont; <span title="Óbudai Egyetem">ÓE</span>/<span title="Neumann János Informatikai Kar">NIK</span>/Biomatika és Alkalmazott Mesterséges Intelligencia Intézet</span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" mtid="10013789"><a href="/gui2/?type=authors&mode=browse&sel=10013789" target="_blank">László Szilágyi (<span class="authorship-author-name">Szilágyi László</span> <span class="authorAux-mtmt"> Villamosmérnöki tudományok</span>) </a> </span> <span class="author-affil"><span title="Óbudai Egyetem">ÓE</span>/<span title="Neumann János Informatikai Kar">NIK</span>/Biomatika és Alkalmazott Mesterséges Intelligencia Intézet; <span title="Sapientia Erdélyi Magyar Tudományegyetem">EMTE</span>/<span title="Marosvásárhelyi Kar">MvhKar</span>/Villamosmérnöki Tanszék</span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;33589051" target="_blank">A review on advanced c-means clustering models based on fuzzy logic</a></div> <div class="InBook"><div class="chapter-in">In:</div> <div class="authors"> <div class="autype autype-1"> <span class="author-affil">IEEE</span> (szerk.) </div> </div> <div class="booktitle"><a href="/gui2/?mode=browse&params=publication;33553134" target="_blank">IEEE 21st World Symposium on Applied Machine Intelligence and Informatics SAMI (2023) : Proceedings </a></div> <div class="conference"> Konferencia helye, ideje: <span class="location">Herlany, Szlovákia <span class="conference-date">2023.01.19. - 2023.01.21.</span> (<span class="conference-organizer">IEEE Hungary Section) </div> <span class="publishedAt">Herlany: <span class="publishers">Institute of Electrical and Electronics Engineers (IEEE)</span>, <span class="page"> pp 293-298 </span> <span class="year">(2023)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="10.1109/SAMI58000.2023.10044530" target="_blank" href="https://doi.org/10.1109/SAMI58000.2023.10044530"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="000972960800050" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/000972960800050"> WoS </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="85149788025" target="_blank" href="http://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85149788025"> Scopus </a> </span> </span> <span class="bookchapter-ids">Befoglaló link(ek):</span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="10.1109/SAMI58000.2023" target="_blank" href="https://doi.org/10.1109/SAMI58000.2023"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <span class="isbnOrIssn"> ISBN: </span> <a style="color:black" title="9798350319859" target="_blank" href="https://www.worldcat.org/search?q=isbn%3A9798350319859"> 9798350319859 </a> </span> </span> <div class="publication-citation" style="margin-left: 0.5cm;"> <span title="Nyilvános idézőközlemények összesen, említések nélkül" class="citingPub-count">Nyilvános idéző összesen: 9</span> | Független: 9 | Függő: 0 | Nem jelölt: 0 | WoS jelölt: 4 | Scopus jelölt:&nbsp;8 | WoS/Scopus jelölt:&nbsp;8 | DOI jelölt:&nbsp;9 </div> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;33589051&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 4 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 33589051</span> | <span class="status-data status-VALIDATED"> Egyeztetett </span> <span class="long-book-mtid">Befoglaló: 33553134</span> Forrás Idéző | <span class="type-subtype">Könyvrészlet ( Konferenciaközlemény ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">kézi felvitel</span> </div> <div class="lastModified">Utolsó módosítás: 2026.01.09. 11:18 Kuszálik Eszter (Sapientia EMTE Marosvásárhely admin4) </div> <pre class="comment" style="margin-top: 0; margin-bottom: 0;"><u>Megjegyzés</u>: Doctoral School of Applied Mathematics and Applied Informatics, O´buda University, Budapest, Hungary Comput. Intell. Research Group, Sapientia Univ., Tg. Mures, Romania Physiological Controls Research Center, O´buda University, Budapest, Hungary Computational Intelligence Research Group Sapientia University, Tg. Mures, Roma...</pre> </div> </div> JournalArticle 33447094 VALIDATED true 0 false 2025-11-04T03:12:18.895+0000 2022-12-23T06:56:39.010+0000 2022-12-23T06:56:37.330+0000 true 521 Szuper Admin /api/admin/521 Admin true 2022-12-23T06:56:37.171+0000 2022-12-23T06:56:37.171+0000 2022-12-23T06:56:37.557+0000 true 521 Szuper Admin /api/admin/521 Admin true false false true 24 24 /api/publicationtype/24 PublicationType 1 true 24 JournalArticle true 10000059 Article true 24 24 /api/publicationtype/24 PublicationType 1 true 24 JournalArticle /api/subtype/10000059 Szakcikk SubType 101 true 10000059 true 1 /api/category/1 Category true 1 Mahfouz, Mohamed A. SPCM: Efficient semi-possibilistic c-means clustering algorithm true true /api/journal/13406 REVIEWED true false 13406 false 13406 true 1064-1246 1875-8967 Journal FOREIGN 43 6 7227 7241 7227 15 2022 The required division and exponentiation operations needed per iteration for the possibilistic c-means (PCM) clustering algorithm complicate its implementation, especially on homomorphically-encrypted data. This paper presents a novel efficient soft clustering algorithm based on the possibilistic paradigm, termed SPCM. It aims at easing future applications of PCM to encrypted data. It reduces the required exponentiation and division operations at each iteration by restricting the membership values to an ordered set of discrete values in [0,1], resulting in a better performance in terms of runtime and several other performance indices. At each iteration, distances to the new clusters' centers are determined, then the distances are compared to the initially computed and dynamically updated range of values, that divide the entire range of distances associated with each cluster center into intervals (bins), to assign appropriate soft memberships to objects. The required number of comparisons is O(log the number of discretization levels). Thus, the computation of centers and memberships is greatly simplified during execution. Also, the use of discrete values for memberships allows soft modification (increment or decrement) of the soft memberships of identified outliers and core objects instead of rough modification (setting to zero or one) in related algorithms. Experimental results on synthetic and standard test data sets verified the efficiency and effectiveness of the proposed algorithm. The average percent of the achieved reduction in runtime is 35% and the average percent of the achieved increase in v-measure, adjusted mutual information, and adjusted rand index is 6% on five datasets compared to PCM. The larger the dataset, the higher the reduction in runtime. Also, SPCM achieved a comparable performance with less computational complexity compared to variants of related algorithms. 2022 true true true false false NONE 2025-11-04 false 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Q2 false false false false 3 12 0 1 0 true Language 10002 /api/language/10002 Angol English true 2 true PersonAuthorship 105637127 /api/authorship/105637127 1 1.0 true false true Mahfouz Mohamed A. true false false AuthorshipType 1 /api/authorshiptype/1 0 true 0 true false true PublicationIdentifier 22601897 /api/publicationidentifier/22601897 PlainSource 6 /api/publicationsource/6 PublicationSourceType 10001 /api/publicationsourcetype/10001 true true true DOI DOI https://doi.org/@@@ true true 6 true 10.3233/JIFS-213172 https://doi.org/10.3233/JIFS-213172 false true PublicationIdentifier 22601896 /api/publicationidentifier/22601896 PlainSource 1 /api/publicationsource/1 PublicationSourceType 10003 /api/publicationsourcetype/10003 false true true WoS WoS https://www.webofscience.com/wos/woscc/full-record/@@@ true true 1 true IDENTICAL 000886972200026 https://www.webofscience.com/wos/woscc/full-record/000886972200026 false true Classification 10034 /api/classification/10034 true true Keyword 1011157 /api/keyword/1011157 true 1011157 true Keyword 1016245 /api/keyword/1016245 true 1016245 true Keyword 1581239 /api/keyword/1581239 true true Keyword 2714442 /api/keyword/2714442 true true Keyword 2891180 /api/keyword/2891180 true true SjrRating 11297590 /api/sjrrating/11297590 174 0.49 journal RatingType 10002 /api/ratingtype/10002 sjr true true ClassificationExternal 2201 /api/classificationexternal/2201 true 2201 true Q2 DIRECT true true /api/publication/33447094 <div class="JournalArticle Publication long-list"> <div class="authors"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" >Mahfouz Mohamed A. ✉ </span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;33447094" target="_blank">SPCM: Efficient semi-possibilistic c-means clustering algorithm</a></div> <div> <span class="journal-title">JOURNAL OF INTELLIGENT & FUZZY SYSTEMS</span> <span class="journal-issn">(<a target="_blank" href="https://portal.issn.org/resource/ISSN/1064-1246">1064-1246</a> <a target="_blank" href="https://portal.issn.org/resource/ISSN/1875-8967">1875-8967</a>)</span>: <span class="journal-volume">43</span> <span class="journal-issue">6</span> <span class="page"> pp 7227-7241 </span> <span class="year">(2022)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:black" title="10.3233/JIFS-213172" target="_blank" href="https://doi.org/10.3233/JIFS-213172"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="000886972200026" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/000886972200026"> WoS </a> </span> </span> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;33447094&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 1 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 33447094</span> | <span class="status-data status-VALIDATED"> Egyeztetett </span> Idéző | <span class="type-subtype">Folyóiratcikk ( Szakcikk ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">WoS-XML</span> </div> <div class="lastModified">Utolsó módosítás: 2022.12.23. 07:56 Szuper Admin (admin) </div> </div></div>