Special Issue "Soft Computing and Machine Learning in Industrial Systems"

Submission Deadline: 30 March 2022
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Guest Editors
Dr. Theyazn Aldhyani, King Faisal University, King Saudi Arabia.
Dr. M. Irfan Uddin, Institute of Computing Kohat University of Science and Technology, Pakistan.
Dr. Belal Alsubari, Miami College of Henan University, China.
Dr. Mohammed Yehya Alzahrani, Albaha University, Saudi Arabia.


Recent advances in control and soft computing systems have brought new levels of real-life applications in a wide range of areas, including control of industrial systems, internet of things (IoT), cyber-physical systems, smart grids, power and energy systems, biomedical engineering, and so on. The complexity and amount of data underlying the industrial processes have increased during the recent years, mainly with the advent of the industry 4.0 paradigm, thus requiring advanced strategies to cope with them. Soft computing techniques, as opposed to traditional computing, have been demonstrated to be a useful tool to translate the data and complexity of modern industrial systems into useful information, which can be, for example, used to help to process control and optimization, and process understanding.

Soft Computing can be understood as a set of methodologies and techniques such as fuzzy logic, expert systems, artificial neural networks, fuzzy neural networks, and genetic algorithms that—when working together and not in isolation—can help both the industry and policymakers to make the best decisions correctly. It is therefore a challenge to choose the best methodologies to accomplish this task. The aim of this Special Issue is to show recent and novel applications of Soft Computing in the field of sustainability assessment. This special issue aims to share and exchange innovative theories, practices, and approaches in soft computing paradigm to unveil the challenging issues associated in deploying the edge-driven smart computing applications.

List of potential topics include, but are not limited to:


• Soft computing and machine learning in industrial applications

• Soft computing and machine learning for complex industrial systems

• Mobile computing and sensing for real-time system simulation

• Soft computing and machine learning in real life applications

• Application of machine learning in cybersecurity

• Machin learning soft computing in big data analytics from numerical simulations

Machine learning, soft computing, IoT, artificial intelligence, smart computing applications, real applications in engineering.