Pub Date : 2023-08-01DOI: 10.1142/s0218488523970048
Vladik Kreinovich
International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsVol. 31, No. 04, pp. 711-712 (2023) No AccessInterval Methods in Knowledge RepresentationVladik KreinovichVladik KreinovichDepartment of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USAhttps://doi.org/10.1142/S0218488523970048Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles on Fuzzy Logic & Z-Numbers With a wide range of areas, you're bound to find something you like. FiguresReferencesRelatedDetails Recommended Vol. 31, No. 04 Metrics History PDF download
国际不确定性，模糊性和基于知识的系统杂志vol . 3。31, No. 04, pp. 711-712 (2023) No AccessInterval Methods in Knowledge表示法弗拉迪克·克雷诺维奇弗拉迪克·克雷诺维奇德克萨斯大学埃尔帕索分校计算机科学系，埃尔帕索，得克萨斯州79968，美国埃尔帕索市https://doi.org/10.1142/S0218488523970048Cited by:0 Previous AboutSectionsPDF/EPUB tools添加到收藏夹下载CitationsTrack citations推荐到图书馆分享分享在facebook上推特链接在redditemail记得查看被引用最多的文章!看看我们的标题模糊逻辑和z -数字与广泛的领域，你一定会找到你喜欢的东西。FiguresReferencesRelatedDetails推荐卷31,No. 04指标历史PDF下载
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Pub Date : 2023-08-01DOI: 10.1142/s0218488523500320
T. Arumuga Maria Devi, P. Darwin
The quality of food and the safety of consumer is one of the major essential things in our day-to-day life. To ensure the quality of foods through their various attributes, different types of methods have been introduced. In this proposed method, three underlying blocks namely Hyperspectral Food Image Context Extractor (HFICE), Hyperspectral Context Fuzzy Classifier (HCFC) and CNN for Food Quality Analyzer (CFQA). Hyperspectral Food Image Context Extractor module is used as the preprocess to get food attributes such as texture, color, size, shape and molecular particulars. Hyperspectral Context Fuzzy Classifier module identifies a particular part of the food (zone entity) is whether carbohydrate, fat, protein, water or unusable core. CNN for Food Quality Analyzer module uses a Tuned Convolutional layer, Heuristic Activation Operation, Parallel Element Merge Layer and a regular Fully Connected Layer. Indian Pines, Salinas and Pavia are the benchmark dataset to evaluate hyperspectral image-based machine learning procedures. These datasets are used along with a dedicated Chicken meat HSI dataset is used in the training and testing process. Results are obtained that about 7.86% of average values in various essential evaluation metrics such as performance metrics such as accuracy, precision, sensitivity and specificity have improved when compared to existing state of the art results.
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Pub Date : 2023-08-01DOI: 10.1142/s0218488523500265
Paul Alain Kaldjob Kaldjob, Brice Mayag, Denis Bouyssou
In the context of Multiple Criteria Decision Making, this paper studies the robustness of the sign of nonadditivity index for subset of criteria in a Choquet integral model. In the case where the set of alternatives is discrete, the use of the nonadditivity index proposed in the literature often leads to interpretations which are not always robust. Indeed, the sign of this nonadditivity index can depend on the arbitrary choice of a numerical representation in the set of all numerical representations compatible with the ordinal preferential information given by the Decision Maker. We characterize the ordinal preferential information for which the problem appears. We also propose a linear program allowing to test the non robustness of the sign of nonadditivity index for subset of criteria.
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Pub Date : 2023-06-01DOI: 10.1142/s0218488523970036
International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsVol. 31, No. 03, pp. 531-532 (2023) No AccessInterval Methods in Knowledge Representationhttps://doi.org/10.1142/S0218488523970036Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles on Fuzzy Logic & Z-Numbers With a wide range of areas, you're bound to find something you like. FiguresReferencesRelatedDetails Recommended Vol. 31, No. 03 Metrics History PDF download
国际不确定性，模糊性和基于知识的系统杂志vol . 3。31, No. 03, pp. 531-532 (2023) No AccessInterval Methods in Knowledge Representationhttps://doi.org/10.1142/S0218488523970036Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack citationsrecommended to Library ShareShare onFacebookTwitterLinked InRedditEmail记得查看被引用最多的文章!看看我们的标题模糊逻辑和z -数字与广泛的领域，你一定会找到你喜欢的东西。FiguresReferencesRelatedDetails推荐卷31,No. 03指标历史PDF下载
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Pub Date : 2023-04-01DOI: 10.1142/s0218488523970024
Vladik Kreinovich
International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsVol. 31, No. 02, pp. 351-352 (2023) No AccessInterval Methods in Knowledge RepresentationVladik KreinovichVladik KreinovichDepartment of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USAhttps://doi.org/10.1142/S0218488523970024Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles on Fuzzy Logic & Z-Numbers With a wide range of areas, you're bound to find something you like. FiguresReferencesRelatedDetails Recommended Vol. 31, No. 02 Metrics History PDF download
国际不确定性，模糊性和基于知识的系统杂志vol . 3。31, No. 02, pp. 351-352 (2023) No AccessInterval Methods in Knowledge表示弗拉迪克·克雷诺维奇弗拉迪克·克雷诺维奇德克萨斯大学埃尔帕索分校计算机科学系，埃尔帕索，得克萨斯州79968，美国埃尔帕索市https://doi.org/10.1142/S0218488523970024Cited by:0 Previous AboutSectionsPDF/EPUB tools添加到收藏夹下载CitationsTrack citations推荐到图书馆分享分享在facebook上推特链接在redditemail记得查看被引用最多的文章!看看我们的标题模糊逻辑和z -数字与广泛的领域，你一定会找到你喜欢的东西。FiguresReferencesRelatedDetails推荐卷31,No. 02指标历史PDF下载
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Pub Date : 2023-02-01DOI: 10.1142/s0218488523970012
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Pub Date : 2022-12-28DOI: 10.1142/s0218488522970066
Vladik Kreinovich
Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA…
请将您的摘要(或您希望在这里看到的论文副本)发送到[email protected]，或通过普通邮件发送到德克萨斯州埃尔帕索大学计算机科学系Vladik Kreinovich, El Paso, TX 79968, USA…
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Pub Date : 2022-12-01DOI: 10.1142/s021848852299001x
{"title":"Author Index Volume 30 (2022)","authors":"","doi":"10.1142/s021848852299001x","DOIUrl":"https://doi.org/10.1142/s021848852299001x","url":null,"abstract":"","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"1 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77663914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1142/s0218488522970054
Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA…
请将您的摘要(或您希望在这里看到的论文副本)发送到[email protected]，或通过普通邮件发送到德克萨斯州埃尔帕索大学计算机科学系Vladik Kreinovich, El Paso, TX 79968, USA…
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Pub Date : 2022-09-09DOI: 10.1142/s0218488522500143
Bruno Almeida Pimentel, Rafael de Amorim Silva, Jadson Crislan Santos Costa
Fuzzy C-means (FCM) clustering algorithm is an important and popular clustering algorithm which is utilized in various application domains such as pattern recognition, machine learning, and data mining. Although this algorithm has shown acceptable performance in diverse problems, the current literature does not have studies about how they can improve the clustering quality of partitions with overlapping classes. The better the clustering quality of a partition, the better is the interpretation of the data, which is essential to understand real problems. This work proposes two robust FCM algorithms to prevent ambiguous membership into clusters. For this, we compute two types of weights: an weight to avoid the problem of overlapping clusters; and other weight to enable the algorithm to identify clusters of different shapes. We perform a study with synthetic datasets, where each one contains classes of different shapes and different degrees of overlapping. Moreover, the study considered real application datasets. Our results indicate such weights are effective to reduce the ambiguity of membership assignments thus generating a better data interpretation.
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