Special Issues

Soft Computing and Machine Learning in Industrial Systems

Submission Deadline: 30 March 2022 (closed) View: 20

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.

Published Papers

  • Open Access


    A Mathematical Optimization Model for Maintenance Planning of School Buildings

    Mehdi Zandiyehvakili, Babak Aminnejad, Alireza Lork
    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 499-512, 2022, DOI:10.32604/iasc.2022.021461
    (This article belongs to the Special Issue: Soft Computing and Machine Learning in Industrial Systems)
    Abstract This article presents a methodology to optimize the maintenance planning model and minimize the total maintenance costs of a typical school building. It makes an effort to provide a maintenance schedule, focusing on maintenance costs. In the allocation of operations to the school equipment, the parameter of its age was also taken into account. A mathematical optimization model to minimize the school maintenance cost in a three-year period was provided in the GAMS software with CPLEX solver. Finally, the optimum architecture of the Perceptron multi-layer neural network was used to predict the schedule of equipment More >

  • Open Access


    Detecting and Analysing Fake Opinions Using Artificial Intelligence Algorithms

    Mosleh Hmoud Al-Adhaileh, Fawaz Waselallah Alsaade
    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 643-655, 2022, DOI:10.32604/iasc.2022.021225
    (This article belongs to the Special Issue: Soft Computing and Machine Learning in Industrial Systems)
    Abstract In e-commerce and on social media, identifying fake opinions has become a tremendous challenge. Such opinions are widely generated on the internet by fake viewers, also called fraudsters. They write deceptive reviews that purport to reflect actual user experience either to promote some products or to defame others. They also target the reputations of e-businesses. Their aim is to mislead customers to make a wrong purchase decision by selecting undesired products. Such reviewers are often paid by rival e-business companies to compose positive reviews of their products and/or negative reviews of other companies’ products. The… More >

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