International Journal of Uncertainty Fuzziness and Knowledge-Based Systems最新文献_第4页

Interval Methods in Knowledge Representation 知识表示中的区间方法

4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2023-08-01 DOI: 10.1142/s0218488523970048

Vladik Kreinovich

引用次数: 0

Performance Metrics on Hyperspectral Images in Fuzzy Contextual Convolutional Neural Network for Food Quality Analyzer 基于模糊上下文卷积神经网络的食品质量分析仪高光谱图像性能评价

4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2023-08-01 DOI: 10.1142/s0218488523500320

T. Arumuga Maria Devi, P. Darwin

{"title":"Performance Metrics on Hyperspectral Images in Fuzzy Contextual Convolutional Neural Network for Food Quality Analyzer","authors":"T. Arumuga Maria Devi, P. Darwin","doi":"10.1142/s0218488523500320","DOIUrl":"https://doi.org/10.1142/s0218488523500320","url":null,"abstract":"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.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136375253","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}

引用次数: 2

On the Robustness of the Sign of Nonadditivity Index in a Choquet Integral Model Choquet积分模型中不可加性指标符号的鲁棒性

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2023-08-01 DOI: 10.1142/s0218488523500265

Paul Alain Kaldjob Kaldjob, Brice Mayag, Denis Bouyssou

引用次数: 0

Interval Methods in Knowledge Representation 知识表示中的区间方法

4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2023-06-01 DOI: 10.1142/s0218488523970036

引用次数: 0

Interval Methods in Knowledge Representation 知识表示中的区间方法

4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2023-04-01 DOI: 10.1142/s0218488523970024

Vladik Kreinovich

引用次数: 0

Acknowledgements to the Referees (2022) 对审稿人的感谢(2022)

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2023-02-01 DOI: 10.1142/s0218488523970012

引用次数: 0

Interval Methods in Knowledge Representation 知识表示中的区间方法

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2022-12-28 DOI: 10.1142/s0218488522970066

Vladik Kreinovich

引用次数: 0

Author Index Volume 30 (2022) 作者索引第30卷(2022年)

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2022-12-01 DOI: 10.1142/s021848852299001x

引用次数: 0

Interval Methods in Knowledge Representation 知识表示中的区间方法

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2022-11-18 DOI: 10.1142/s0218488522970054

引用次数: 0

Fuzzy C-Means Clustering Algorithms with Weighted Membership and Distance 加权隶属度和距离模糊c均值聚类算法

IF 1.5 4区计算机科学

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Pub Date : 2022-09-09 DOI: 10.1142/s0218488522500143

Bruno Almeida Pimentel, Rafael de Amorim Silva, Jadson Crislan Santos Costa

{"title":"Fuzzy C-Means Clustering Algorithms with Weighted Membership and Distance","authors":"Bruno Almeida Pimentel, Rafael de Amorim Silva, Jadson Crislan Santos Costa","doi":"10.1142/s0218488522500143","DOIUrl":"https://doi.org/10.1142/s0218488522500143","url":null,"abstract":"<p>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.</p>","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"34 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138529084","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}

引用次数: 3