Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.
{"title":"Unification of imprecise data - translation of fuzzy to multi-valued knowledge over Y-axis","authors":"","doi":"10.4018/ijfsa.292459","DOIUrl":"https://doi.org/10.4018/ijfsa.292459","url":null,"abstract":"Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43161823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Concept selection in design is an important aspect of design process that must be done properly with the right tools. The identification of optimal design is presented in this article by integrating three Multicriteria decision making models which are fuzzified pairwise comparison matrices, fuzzified weighted decision matrix and fuzzy VIKOR. Rather than depending solely on design expert’s view to determine weights of the design features, the pairwise comparison matrices determines the weights of design features and sub features in the decision process. The weighted decision matrix aggregates scores for the alternative designs considering the availability of sub features in them. The aggregated scores form the elements of the main decision matrix together with the weights of the design features and the Fuzzy VIKOR model determines the performance index of the design concepts. The hybridized model was validated using four conceptual designs of liquid spraying machines and the results obtained shows that the model provides computational integrity in decision making process.
{"title":"IMPROVING THE COMPUTATIONAL PROCESS FOR IDENTIFYING OPTIMAL DESIGN USING FUZZIFIED DECISION MODELS","authors":"","doi":"10.4018/ijfsa.303562","DOIUrl":"https://doi.org/10.4018/ijfsa.303562","url":null,"abstract":"Concept selection in design is an important aspect of design process that must be done properly with the right tools. The identification of optimal design is presented in this article by integrating three Multicriteria decision making models which are fuzzified pairwise comparison matrices, fuzzified weighted decision matrix and fuzzy VIKOR. Rather than depending solely on design expert’s view to determine weights of the design features, the pairwise comparison matrices determines the weights of design features and sub features in the decision process. The weighted decision matrix aggregates scores for the alternative designs considering the availability of sub features in them. The aggregated scores form the elements of the main decision matrix together with the weights of the design features and the Fuzzy VIKOR model determines the performance index of the design concepts. The hybridized model was validated using four conceptual designs of liquid spraying machines and the results obtained shows that the model provides computational integrity in decision making process.","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43036957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the present article an effort has been made to design and develop a diet recommendation system for Metabolic Disorders patients. The key feature of this system is to recommend a menu for dinner to maintain nutritional micros as per daily requirements.The proposed intelligent decision-making system is designed as per the following phases:Under the 1st Phase, we compute the requirement of calories as per the Patient's personal information (like sex, age, height, weight), physical activity, environmental situations, and food habits on a daily basis. Under the 2nd Phase, development of knowledge base as per Patient's foods habits information. 3rd Phase is based on designing the recommendation system for a dinner menu to maintain nutritional micros as per daily requirements. The results of the system have been validated by using the Degree of match algorithm and comments of Nutritional experts. This intelligent decision making system will help the ordinary people living in urban and rural areas, especially those not aware of the nutritional value concerned with their daily food items.
{"title":"Fuzzy Utility Matrix based Intelligent Decision-Making model and its application to Diet recommendation system for Metabolic Disorders patient","authors":"","doi":"10.4018/ijfsa.303563","DOIUrl":"https://doi.org/10.4018/ijfsa.303563","url":null,"abstract":"In the present article an effort has been made to design and develop a diet recommendation system for Metabolic Disorders patients. The key feature of this system is to recommend a menu for dinner to maintain nutritional micros as per daily requirements.The proposed intelligent decision-making system is designed as per the following phases:Under the 1st Phase, we compute the requirement of calories as per the Patient's personal information (like sex, age, height, weight), physical activity, environmental situations, and food habits on a daily basis. Under the 2nd Phase, development of knowledge base as per Patient's foods habits information. 3rd Phase is based on designing the recommendation system for a dinner menu to maintain nutritional micros as per daily requirements. The results of the system have been validated by using the Degree of match algorithm and comments of Nutritional experts. This intelligent decision making system will help the ordinary people living in urban and rural areas, especially those not aware of the nutritional value concerned with their daily food items.","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43493204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heart disease diagnosis depends on vague, imprecise, ambiguity and inconsistent combination of clinical and pathological data. Therefore, researches in these fields tend to the use of intelligent systems to overcome the uncertainty found in data. This paper suggests neutrosophic logic to obtain a better decision of heart diagnosis with the desire to reduce the number of tests required to be taken on a patient and solve the information uncertainty issue. This paper analyses the dataset to extract the five common features that affect heart disease in Egypt, which are blood pressure, blood sugar, cholesterol, chest pain, and maximum heart rate. Then; it presents a neutrosophic diagnosing system for heart disease depends on a dataset from Egyptian persons were used and independently verified by three experts using semi-structured questionnaire. Finally, the comparison results between human experts, and the presented neutrosophic diagnosing system shows an accuracy of 87% of the proposed system compared with 73% of the fuzzy system.
{"title":"A Neutrosophic Intelligent System for Heart Disease Diagnosis","authors":"","doi":"10.4018/ijfsa.302121","DOIUrl":"https://doi.org/10.4018/ijfsa.302121","url":null,"abstract":"Heart disease diagnosis depends on vague, imprecise, ambiguity and inconsistent combination of clinical and pathological data. Therefore, researches in these fields tend to the use of intelligent systems to overcome the uncertainty found in data. This paper suggests neutrosophic logic to obtain a better decision of heart diagnosis with the desire to reduce the number of tests required to be taken on a patient and solve the information uncertainty issue. This paper analyses the dataset to extract the five common features that affect heart disease in Egypt, which are blood pressure, blood sugar, cholesterol, chest pain, and maximum heart rate. Then; it presents a neutrosophic diagnosing system for heart disease depends on a dataset from Egyptian persons were used and independently verified by three experts using semi-structured questionnaire. Finally, the comparison results between human experts, and the presented neutrosophic diagnosing system shows an accuracy of 87% of the proposed system compared with 73% of the fuzzy system.","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44054974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hybrid Distributed Deep-GAN Intrusion Detection System in IoT with Autoencoder","authors":"S. Balaji, S. Sankaranarayanan","doi":"10.4018/ijfsa.312238","DOIUrl":"https://doi.org/10.4018/ijfsa.312238","url":null,"abstract":"","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70458042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A New Distance Measure to Rank Type-2 Intuitionistic Fuzzy Sets and Its Application to Multi-Criteria Group Decision Making","authors":"V. Anusha, V. Sireesha","doi":"10.4018/IJFSA.285982","DOIUrl":"https://doi.org/10.4018/IJFSA.285982","url":null,"abstract":"","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70458131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present paper contributes to a set of models capturing economic order quantity with learning effect and fuzzy environment for decaying defective quality items under the inflation condition and credit financing. In real-life situations, the demand is uncertain and is controlled with fuzzy numbers. When each item goes through inspection process, the screening rate is assumed to be more than the demand rate otherwise shortages may occur and this consideration also helps one to meet their demand parallel to the screening process, out of the items which are of perfect quality. Further, the defective items are sold immediately after the screening process as a single lot at a discounted price. Further, the fraction of defective items follows the S-shaped learning curve. An expression for the total fuzzy profit of the retailer has been de-fuzzified with the help of a signed distance method and maximizes the cycle length. Conclusively, sensitive analysis has been presented on the various effective parameters of the inventory model.
{"title":"Impact of Learning on the Inventory Model of Deteriorating Imperfect Quality Items With Inflation and Credit Financing Under Fuzzy Environment","authors":"","doi":"10.4018/ijfsa.302125","DOIUrl":"https://doi.org/10.4018/ijfsa.302125","url":null,"abstract":"The present paper contributes to a set of models capturing economic order quantity with learning effect and fuzzy environment for decaying defective quality items under the inflation condition and credit financing. In real-life situations, the demand is uncertain and is controlled with fuzzy numbers. When each item goes through inspection process, the screening rate is assumed to be more than the demand rate otherwise shortages may occur and this consideration also helps one to meet their demand parallel to the screening process, out of the items which are of perfect quality. Further, the defective items are sold immediately after the screening process as a single lot at a discounted price. Further, the fraction of defective items follows the S-shaped learning curve. An expression for the total fuzzy profit of the retailer has been de-fuzzified with the help of a signed distance method and maximizes the cycle length. Conclusively, sensitive analysis has been presented on the various effective parameters of the inventory model.","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46987087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Motivated by the structural aspect of the probabilistic entropy, the concept of fuzzy entropy enabled the researchers to investigate the uncertainty due to vague information. Fuzzy entropy measures the ambiguity/vagueness entailed in a fuzzy set. Hesitant fuzzy entropy and hesitant fuzzy linguistic term set based entropy presents a more comprehensive evaluation of vague information. In the vague situations of multiple-criteria decision-making, entropy measure is utilized to compute the objective weights of attributes. The weights obtained due to entropy measures are not reasonable in all the situations. To model such situation, a knowledge measure is very significant, which is a structural dual to entropy. A fuzzy knowledge measure determines the level of precision in a fuzzy set. This article introduces the concept of a knowledge measure for hesitant fuzzy linguistic term sets (HFLTS) and show how it may be derived from HFLTS distance measures. Authors also investigate its application in determining the weights of criteria in multi-criteria decision-making (MCDM).
{"title":"Distance-based Knowledge measure of Hesitant Fuzzy Linguistic Term Set with its application in Multi-criteria decision-making","authors":"","doi":"10.4018/ijfsa.292460","DOIUrl":"https://doi.org/10.4018/ijfsa.292460","url":null,"abstract":"Motivated by the structural aspect of the probabilistic entropy, the concept of fuzzy entropy enabled the researchers to investigate the uncertainty due to vague information. Fuzzy entropy measures the ambiguity/vagueness entailed in a fuzzy set. Hesitant fuzzy entropy and hesitant fuzzy linguistic term set based entropy presents a more comprehensive evaluation of vague information. In the vague situations of multiple-criteria decision-making, entropy measure is utilized to compute the objective weights of attributes. The weights obtained due to entropy measures are not reasonable in all the situations. To model such situation, a knowledge measure is very significant, which is a structural dual to entropy. A fuzzy knowledge measure determines the level of precision in a fuzzy set. This article introduces the concept of a knowledge measure for hesitant fuzzy linguistic term sets (HFLTS) and show how it may be derived from HFLTS distance measures. Authors also investigate its application in determining the weights of criteria in multi-criteria decision-making (MCDM).","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43932298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neuro-Fuzzy-Based Routing Mechanism for Effective Communication in 6LoWPAN-Based IoT Infrastructure","authors":"B. Revathi, K. Arulanandam","doi":"10.4018/ijfsa.306280","DOIUrl":"https://doi.org/10.4018/ijfsa.306280","url":null,"abstract":"","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70457834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Text summarization generates a concise summary of the available information by determining the most relevant and important sentences in the document. In this paper, an effective approach of document summarization is developed for generating summary of Hindi documents. The developed deep learning-based Hindi document summarization system comprises of a number of phases, such as input data acquisition, tokenization, feature extraction, score generation, and sentence extraction. Here, a deep recurrent neural network (Deep RNN) is employed for generating the scores of the sentences based on the significant features, wherein the weights and learning parameters of the deep RNN are updated by using the devised coot remora optimization (CRO) algorithm. Moreover, the developed CRO-Deep RNN is examined for its efficacy considering metrics, like recall-oriented understudy for gisting evaluation (ROUGE), recall, precision, and f-measure, and is found to have attained values of 80.896%, 95.700%, 95.051%, and 95.374%, respectively.
文本摘要通过确定文档中最相关和最重要的句子，生成可用信息的简明摘要。本文提出了一种生成印地语文档摘要的有效方法。所开发的基于深度学习的印地语文档摘要系统包括多个阶段，如输入数据获取、标记化、特征提取、分数生成和句子提取。这里，深度递归神经网络（deep RNN）用于基于显著特征生成句子的分数，其中通过使用所设计的coot-remora优化（CRO）算法来更新深度RNN的权重和学习参数。此外，考虑到面向召回的注册评估替代研究（ROUGE）、召回率、精确度和f-measure等指标，对所开发的CRO Deep RNN的功效进行了检验，发现其值分别为80.896%、95.700%、95.051%和95.374%。
{"title":"BERT Tokenization and Hybrid-Optimized Deep Recurrent Neural Network for Hindi Document Summarization","authors":"Sumalatha Bandari, Vishnu Vardhan Bulusu","doi":"10.4018/ijfsa.313601","DOIUrl":"https://doi.org/10.4018/ijfsa.313601","url":null,"abstract":"Text summarization generates a concise summary of the available information by determining the most relevant and important sentences in the document. In this paper, an effective approach of document summarization is developed for generating summary of Hindi documents. The developed deep learning-based Hindi document summarization system comprises of a number of phases, such as input data acquisition, tokenization, feature extraction, score generation, and sentence extraction. Here, a deep recurrent neural network (Deep RNN) is employed for generating the scores of the sentences based on the significant features, wherein the weights and learning parameters of the deep RNN are updated by using the devised coot remora optimization (CRO) algorithm. Moreover, the developed CRO-Deep RNN is examined for its efficacy considering metrics, like recall-oriented understudy for gisting evaluation (ROUGE), recall, precision, and f-measure, and is found to have attained values of 80.896%, 95.700%, 95.051%, and 95.374%, respectively.","PeriodicalId":38154,"journal":{"name":"International Journal of Fuzzy System Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46526494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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