Presentation
About the workshop
Hybrid Intelligent Systems (HIS) deal with real-world complexity with a multidisciplinary approach and a plurality of artificial intelligence techniques. Complex systems, including biology, medicine, logistics, management, engineering, humanities, industrial environments, and technological applications, have significant difficulty modeling and interacting with their processes using classical methods. This workshop aims to discuss research on progress working with hybrid intelligent systems applied to topics applying artificial intelligence techniques in this framework.
The HIS2024 is a workshop conference held by the Mexican Society of Artificial Intelligence (SMIA) in its central Mexican International Conference on Artificial Intelligence (MICAI).
HIS2024 covers and gathers research topics associated with Hybrid Intelligent Systems and their capabilities for modeling, negotiating a specific topic, demonstrating reputation using diverse models, and managing all these complex processes.
Keynote Speaker XVII HIS 2024
Prof. Patricia Melin, Ph.D., D.Sc.
Tijuana Institute of Technology-TecNM,
Tijuana, Mexico.
BIOGRAPHY:
Prof. Patricia Melin is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology-TecNM, Tijuana, Mexico, since 1998. In addition, she is serving as Director of Graduate Studies in Computer Science and is head of the research group on Hybrid Neural Intelligent Systems (2000-present). She belongs to the Mexican Research System with level III . She holds the Doctor in Science degree (Doctor Habilitatus D.Sc.) in Computer Science from the Polish Academy of Sciences. She has also been advisor of more than 100 graduate students in computer science at the Ph.D. and masters levels. Prof. Melin has published nearly 1100 publications in indexed journals, book chapters, and conference proceedings, as well as nearly 50 books, and as consequence of this she has achieved more than 23700 citations with an h index of 86 in Google Scholar, and h index of 76 in Scopus. In addition, she has been awarded the Highly Cited Researcher recognition in the area of Computer Science in 2017 and 2018 by Clarivate Analytics-Web of Science because she is in the top 1% cited author in this area. She has also been awarded with the IFSA 2021 Award on Outstanding Applications of Fuzzy Technology for the contribution of Development and Application of Fuzzy Models in Medical Diagnosis, the NAFIPS 2022 K. S. Fu Award for contributions to the North American Fuzzy Information Processing Society, the INFUS 2022 and 2024 Lotfi A. Zadeh Memorial Award for the outstanding contribution to fuzzy sets and systems field. She is past President of NAFIPS (North American Fuzzy Information Processing Society) 2019-2020. Prof. Melin is the founding Chair of the Mexican Chapter of the IEEE Computational Intelligence Society. She is member of the IEEE Neural Network Technical Committee (2007 to present), the IEEE Fuzzy System Technical Committee (2014 to present) and is the founding Chair of the Task Force on Hybrid Intelligent Systems (2007 to present) and she is currently Associate Editor of the Information Sciences Journal, IEEE Transactions on Fuzzy Systems and Journal of Complex and Intelligent Systems. She is Editor-in-Chief of the Advances in Fuzzy Systems journal (Wiley). She is member of NAFIPS, IFSA, and IEEE. Her research interests are in Modular Neural Networks, Type-2 Fuzzy Logic, Pattern Recognition, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches.
Hybrid Intelligent Models based on Neural Networks and Fuzzy Logic
ABSTRACT:
Hybrid intelligent systems are formed by prudent combinations of intelligent models, such as neural networks, fuzzy models and others, to achieve efficient solutions to real-world problems. The main idea is to take advantage of the main characteristics of the individual models. For example, neural networks are good for learning from training data, while fuzzy logic is good for representing expert knowledge and uncertainty management. In our work the proposed approach is to build powerful hybrid intelligent systems for achieving accurate results for different applications. The proposed hybrid architecture is based on modular neural networks for learning from large datasets. Then for combining the outputs of the modules an integration based on type-1 or type-2 fuzzy rules is performed for modeling the involved decision-making process, as well as the inherent uncertainty in making the decisions. In addition, some applications will be used to illustrate the good performance of general type-2 fuzzy logic, as well as a comparison with interval type-2 and type-1 fuzzy systems to verify the significant advantage obtained in using general type-2 fuzzy logic. We believe that the proposed hybrid intelligent approach can be used for solving many real problems.