IEEE Transactions on Fuzzy Systems最新文献

{"title":"Multivariate Long-Term Forecasting Using Multi-Linear Trend Fuzzy Information Granules for Traffic Time Series","authors":"Xianfeng Huang, Zhiyuan Huang, Jianming Zhan","doi":"10.1109/tfuzz.2024.3497974","DOIUrl":"https://doi.org/10.1109/tfuzz.2024.3497974","url":null,"abstract":"","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"21 1","pages":""},"PeriodicalIF":11.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Linguistic Description Approach for Time Series and Its Application to Bed Restlessness Monitoring for Eldercare","authors":"Carmen Martinez-Cruz;Antonio J. Rueda;Mihail Popescu;James M. Keller","doi":"10.1109/TFUZZ.2021.3052107","DOIUrl":"10.1109/TFUZZ.2021.3052107","url":null,"abstract":"Time-series analysis has been an active area of research for years, with important applications in forecasting or discovery of hidden information such as patterns or anomalies in observed data. In recent years, the use of time-series analysis techniques for the generation of descriptions and summaries in natural language of any variable, such as temperature, heart rate, or CO\u0000₂\u0000 emission has received increasing attention. Natural language has been recognized as more effective than traditional graphical representations of numerical data in many cases, in particular, in situations where a large amount of data needs to be inspected or when the user lacks the necessary background and skills to interpret it. In this article, we describe a novel mechanism to generate linguistic descriptions of time series using natural language and fuzzy logic techniques. The proposed method generates quality summaries capturing the time-series features that are relevant for a user in a particular application, and can be easily customized for different domains. This approach has been successfully applied to the generation of linguistic descriptions of bed restlessness data from residents at TigerPlace (Columbia, MO, USA), which is used as a case study to illustrate the modeling process and show the quality of the descriptions obtained.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"30 4","pages":"1048-1059"},"PeriodicalIF":11.9,"publicationDate":"2021-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TFUZZ.2021.3052107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9226883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring","authors":"Cheng Kang;Xiang Yu;Shui-Hua Wang;David S. Guttery;Hari Mohan Pandey;Yingli Tian;Yu-Dong Zhang","doi":"10.1109/TFUZZ.2020.2966163","DOIUrl":"10.1109/TFUZZ.2020.2966163","url":null,"abstract":"Traditional deep learning methods are suboptimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal-more likely normal-probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this article proposes a dominant fuzzy fully connected layer (FFCL) for breast imaging reporting and data system (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzifier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean distance to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the curated breast imaging subset of digital database of screening mammography dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"29 1","pages":"34-45"},"PeriodicalIF":11.9,"publicationDate":"2020-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TFUZZ.2020.2966163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39125074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}