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Ravi L, Devarajan M, V V, Sangaiah AK, Wang L, A S, Subramaniyaswamy V. An intelligent location recommender system utilising multi-agent induced cognitive behavioural model. ENTERP INF SYST-UK 2020. [DOI: 10.1080/17517575.2020.1812003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Logesh Ravi
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | | | - Vijayakumar V
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | - Arun Kumar Sangaiah
- School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Lipo Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
| | - Sasikumar A
- School of Computing, SASTRA Deemed University, Thanjavur, India
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Nápoles G, Espinosa ML, Grau I, Vanhoof K. FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps. INT J ARTIF INTELL T 2018. [DOI: 10.1142/s0218213018600102] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many successful studies solved through the use of FCMs; however, when it comes to software implementations, where domain experts can design FCM-based systems, run simulations or perform more advanced experiments, not much is found or documented. The few existing implementations are not proficient in providing options for experimentation. Therefore, we believe that a gap exists, specifically between the theoretical advances and the development of accurate, transparent and sound FCM-based systems; and we advocate for the creation of more complete and exible software products. The goal of this paper is to introduce “FCM Expert”, a software tool for fuzzy cognitive modeling, where we focus on scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters that might define a model, optimize its network topology and improve the system convergence without losing information. Also, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.
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Affiliation(s)
- Gonzalo Nápoles
- Faculty of Business Economics, Universiteit Hasselt, Belgium
| | | | - Isel Grau
- Artificial Intelligence Laboratory, Vrije Universiteit Brussel, Belgium
| | - Koen Vanhoof
- Faculty of Business Economics, Universiteit Hasselt, Belgium
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Nápoles G, Papageorgiou E, Bello R, Vanhoof K. On the convergence of sigmoid Fuzzy Cognitive Maps. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.02.040] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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