Hesamian G, Johannssen A and Chukhrova N (2023). Fuzzy Nonlinear Regression Modeling With Radial Basis Function Networks, IEEE Transactions on Fuzzy Systems , 32 :4 , (1733-1742), Online publication date: 1-Apr-2024 .
Hesamian G, Chukhrova N and Johannssen A (2023). Statistical inference on quantiles of two independent populations under uncertainty, Soft Computing - A Fusion of Foundations, Methodologies and Applications , 27 :23 , (17573-17583), Online publication date: 1-Dec-2023 .
Hesamian G and Akbari M (2023). A fuzzy Bayesian regression model with Gaussian process prior based on exact predictors and fuzzy responses, Artificial Intelligence Review , 56 :11 , (13765-13785), Online publication date: 1-Nov-2023 .
Zumelzu N, Bedregal B, Mansilla E, Bustince H and Díaz R (2022). Admissible Orders on Fuzzy Numbers, IEEE Transactions on Fuzzy Systems , 30 :11 , (4788-4799), Online publication date: 1-Nov-2022 .
Akbari M and Hesamian G (2019). Neyman---Pearson lemma based on intuitionistic fuzzy parameters, Soft Computing - A Fusion of Foundations, Methodologies and Applications , 23 :14 , (5905-5911), Online publication date: 1-Jul-2019 .
(2019). Level 2 feature extraction for latent fingerprint enhancement and matching using type-2 intuitionistic fuzzy set, International Journal of Bioinformatics Research and Applications , 15 :1 , (33-50), Online publication date: 1-Jan-2019 .
Mohammed A, Gadallah A, Hefny H and Hazman M (2018). Fuzzy based approach for discovering crops plantation knowledge from huge agro-climatic data respecting climate changes, Computing , 100 :7 , (689-713), Online publication date: 1-Jul-2018 .
Dash J and Mukhopadhyay S (2018). Similarity learning for texture image retrieval using multiple classifier system, Multimedia Tools and Applications , 77 :1 , (459-483), Online publication date: 1-Jan-2018 .
Hesamian G and Shams M (2016). A Note on Fuzzy Probability of a Fuzzy Event, International Journal of Intelligent Systems , 32 :7 , (676-685), Online publication date: 2-May-2017 .
Akbari M and Chahkandi M (2017). Fuzzy record values, Soft Computing - A Fusion of Foundations, Methodologies and Applications , 21 :4 , (1013-1020), Online publication date: 1-Feb-2017 .
Das S, Guha D and Dutta B (2016). Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic, Applied Intelligence , 45 :3 , (850-867), Online publication date: 1-Oct-2016 .
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Jain A and Lobiyal D (2015). Fuzzy Hindi WordNet and Word Sense Disambiguation Using Fuzzy Graph Connectivity Measures, ACM Transactions on Asian and Low-Resource Language Information Processing , 15 :2 , (1-31), Online publication date: 1-Feb-2016 .
Hesamian G and Chachi J (2014). Fuzzy Sign test for imprecise quantities, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology , 27 :6 , (3159-3167), Online publication date: 1-Nov-2014 .
Finotto V, da Silva W, Štemberk P and Valášek M (2014). Sensitivity analysis of fuzzy-genetic approach applied to cabled-truss design, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology , 26 :4 , (1931-1942), Online publication date: 1-Jul-2014 .
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Ja'fari A and Moghadam R Integration of fuzzy systems and genetic algorithm in permeability prediction Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II, (287-299)
Valdés L, Ariza A, Allende S, Parada R and Joya G Study of alternative strategies to selection of peer in P2P wireless mesh networks Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I, (124-132)
Wu Z, Hsieh S and Li J (2013). Sensor deployment based on fuzzy graph considering heterogeneity and multiple-objectives to diagnose manufacturing system, Robotics and Computer-Integrated Manufacturing , 29 :1 , (192-208), Online publication date: 1-Feb-2013 .
Khaniyev T, Turksen I, Gokpinar F and Gever B (2013). Ergodic distribution for a fuzzy inventory model of type (s,S) with gamma distributed demands, Expert Systems with Applications: An International Journal , 40 :3 , (958-963), Online publication date: 1-Feb-2013 .
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Akyar E, Akyar H and Düzce S (2013). Fuzzy risk analysis based on a geometric ranking method for generalized trapezoidal fuzzy numbers, Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology , 25 :1 , (209-217), Online publication date: 1-Jan-2013 .
Das S, Roy Chowdhury S and Saha H (2012). Accuracy Enhancement in a Fuzzy Expert Decision Making System Through Appropriate Determination of Membership Functions and Its Application in a Medical Diagnostic Decision Making System, Journal of Medical Systems , 36 :3 , (1607-1620), Online publication date: 1-Jun-2012 .
Huang T, Huang C and Chiu K Reliability sequential sampling test based on exponential lifetime distributions under fuzzy environment Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III, (346-355)
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Maraj A, Shehu A and Mitrushi R Studying of different parameters that affect QoS in IPTV systems Proceedings of the 9th WSEAS international conference on Telecommunications and informatics, (107-112)
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Maraj A Analysis of routing metrics for providing better link utilization in WiMAX using soft computing Proceedings of the 8th WSEAS international conference on Electronics, hardware, wireless and optical communication, (161-166)
Maraj A, Shatri B and Rugova S Selection of defuzzification method for routing metrics in MPLS network to obtain better crisp values for link optimization Proceedings of the 7th WSEAS international conference on System science and simulation in engineering, (200-205)
Yoo G, Park J and Lee E Hybrid inference architecture and model for self-healing system Proceedings of the 9th Asia-Pacific international conference on Network Operations and Management: management of Convergence Networks and Services, (566-569)
Park I, Na D, Lee K and Lee D Fuzzy continuous petri net-based approach for modeling helper t cell differentiation Proceedings of the 4th international conference on Artificial Immune Systems, (331-338)
Park I, Na D, Lee D and Lee K Fuzzy continuous petri net-based approach for modeling immune systems Proceedings of the 16th Italian conference on Neural Nets, (278-285)
Reviewer: Arthur Gittleman
Fuzzy systems handle imprecise concepts. Using fuzzy sets and fuzzy logic, one can build expert systems and controllers that have the advantages of being easy to understand, and of requiring less processing power than comparable neural net applications. The author lectures on fuzzy theory, and his book has the flavor of lecture notes. It is easy to read, very suitable for self-study or an introductory course, and has no embedded references in the text. The first eight chapters develop fuzzy theory, while the last four briefly introduce applications. Each chapter ends with a summary and an exercise set. The exercises are mostly computational, to check one's understanding of the material, but a few are proofs. Chapter 1 defines a fuzzy set, in which a membership function assigns a number from 0 to 1 to every element of the universal set. A zero value indicates that the element is definitely not a member, while a value of one indicates that it definitely is. The values in between represent the fuzzy cases. The next five chapters cover fuzzy set operations, fuzzy relations, fuzzy graphs, fuzzy numbers, and fuzzy functions. Chapter 7 compares fuzzy theory with probability. The final theory chapter covers fuzzy logic. Chapter 9, the first of the application chapters, presents fuzzy inference, discussing four different inference methods. This chapter provides a good foundation for the remaining ones, which cover fuzzy control and fuzzy expert systems, the fusion of fuzzy systems and neural networks, and the fusion of fuzzy system and genetic algorithms. These later chapters are brief introductions, encompassing only 70 pages. Chapter 7 has three obvious misspellings in the first three pages. Other chapters have occasional errors in grammar. The book would have benefited from more proofreading. The bibliography has 155 references, but it would have been much more helpful to include annotations, group them into categories, and refer to them at appropriate points within the text. Overall, this text is a pleasant way to approach an interesting subject. Online Computing Reviews Service
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Type-2 fuzzy sets are used for modeling uncertainty and imprecision in a better way. These type-2 fuzzy sets were originally presented by Zadeh in 1975 and are essentially “fuzzy fuzzy” sets where the fuzzy degree of membership is a type-1 fuzzy set. .
The convergence theory not only is an significantly basic theory of fuzzy topology and fuzzy analysis but also has wide applications in fuzzy inference and some other aspects. In this paper, we introduce the concept of PSC-remote-neighborhood of fuzzy .
Abstract Type-2 fuzzy sets possess higher capability of capturing uncertainties than ordinary fuzzy sets due to the presence of secondary membership degree. As a consequence, type-2 fuzzy set has remarkably progressed as a promising tool for dealing .
