Fuzzy Information and Engineering

Uncertainty is a broad and complicated area of research. However, there is a clear need to move towards more intelligent decision-making frameworks that can handle complex uncertainties and open-mindedness in the real world. One such framework that has gained popularity is the one adopted by fuzzy logic, where a set of membership functions represents uncertainty. Since their inception, fuzzy controllers have been applied to many practical problems, from robotics to financial markets. Runtime systems based on fuzzy inference have also been used for modeling uncertain information systems (UI) that humans produce during application development.

Fuzzy logic systems are methods of reasoning that allow users to define their knowledge representations. These approaches are powerful because they can be tailored to the needs of users with varying skill levels and different domains. Still, they also require a high level of system understanding and careful analysis to ensure reliable results. A good example of a situation where fuzzy logic and probabilistic modeling could be applied is by providing the option for selectable prioritization rules for crossmodal retrieval. As information technology has evolved in recent years and computers have become more powerful, economic forecasting software based on probabilistic models has become increasingly practical. Nevertheless, a thorough understanding of the principles of fuzzy modeling is required to apply this technique correctly.

The main motivation for research in fuzzy logic and probabilistic modeling at this time is the need for a new way of thinking to deal with the uncertainties in our environment. Fuzzy logic provides a framework for describing fate, making it easier to reason about uncertain information systems. For example, fuzzy logic will help highly important and complex industrial problems such as decision support systems and expert systems, which provide users with better solutions than traditional computer programs. In addition, it may be possible to combine fuzzy logic with other approaches like neural networks to create hybrid systems that take account of multiple sources of uncertainty more effectively than either technique alone. This can lead to better solutions to any given problem than using just one approach by itself.

List of potential topics of the special section include, but are not limited to the following:

• Probabilistic models, including Bayesian filtering, naive Bayesian classifiers, probability density functions
• Fuzzy logic and its application to various domains such as computer vision, robotics, and control systems.
• Fuzzy controller design using genetic algorithms and/or particle swarm optimization.
• Hybrid systems consist of fuzzy controllers, neural networks, and/or other approaches like evolutionary computation.
• Hybrid approaches that combine fuzzy logic with other AI techniques, such as neural networks
• Combining fuzziness with other approaches to create hybrid systems that take account of multiple sources of uncertainty more effectively than either technique alone.
• Fuzzy logic in the context of real-world applications, including expert systems and decision support systems.
• Fuzzy logic in action: the application of fuzzy logic to various problems and how it improves on existing approaches
• The development of fuzzy logic for different problem domains, including but not limited to control systems and expert systems
• Hybrid models that combine multiple methods, such as neural networks with fuzzy logic
• Application of fuzzy logic in real-world problems, including decision support systems and expert systems.
• Combining neural networks and other approaches like fuzzy logic to create hybrid systems that take account of multiple sources of uncertainty more effectively than either technique alone

Guest Editor Affiliation Details:

Dr. Thinagaran Perumal (MGE)
Associate Professor
Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
Email: thinagaran@upm.edu.my, gmthinachen@gmail.com 
Personal Site: https://www.tperumal.com/
Google Scholar: https://scholar.google.com.my/citations?user=KJo5T0sAAAAJ&hl=en

Dr. Ramanujam E (First Co-GE)
Assistant Professor
Department of Computer Science and Engineering, National Institute of Technology Silchar, Assam - 788010, India.
Email: ramanujam@cse.nits.ac.in
Google Scholar: https://scholar.google.co.in/citations?user=tI_C9kEAAAAJ&hl=en 
Official Site: http://cs.nits.ac.in/ramanujam/

Dr. Liang-Bi Chen (Second Co-GE)
Email: liangbichen@gms.npu.edu.tw
Google Scholar: https://scholar.google.com.tw/citations?user=EFvRCHYAAAAJ&hl=en
IEEE Site: https://ieeexplore.ieee.org/author/37086046066

Guest Editor Short Biography:

MGE Short Bio: Dr Thinagaran Perumal is the recipient of 2014 Early Career Award from IEEE Consumer Electronics Society for his pioneering contribution in the field of consumer electronics. He completed his PhD at Universiti Putra Malaysia, in the area of smart technology and robotics. He is currently an Associate Professor attached with Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia. He is also currently appointed as Chair of IEEE Consumer Electronics Society Malaysia Chapter. His research interests are towards interoperability aspects of smart homes and Internet of Things (IoT), activity recognition for ambient intelligence and cyber-physical systems. Some of the eminent works include proactive architecture for IoT systems; development of the cognitive and semantic IoT frameworks for smart homes and activity recognition in smart environments. He is an active member of IEEE Consumer Electronics Society and its Future Directions Committee on Internet of Things. Dr Thinagaran has been invited to give several keynote lectures and plenary talk on Internet of Things in various institutions and organizations internationally. He has received 4 best paper awards. Dr Thinagaran has delivered 10 keynotes and served on 5 panels at various International Conferences. His Scopus h-index is 13, with 460+ citations while Google Scholar h-index is 15 and i10-index is 19 with 727+ citations.