Special lssues
Table of Content

Control Systems and Machine Learning for Intelligent Computing

Submission Deadline: 30 March 2024 (closed)

Guest Editors

Prof. Dragan Pamučar, University of Belgrade, Serbia
Dr. Muhammad Riaz, University of the Punjab, Pakistan

Summary

The integration of machine learning techniques with control systems has led to several advancements in various domains such as robotics, automation, and process control. The objective of this special issue is to bring together original research contributions that demonstrate the latest developments, innovations, and applications of control systems and machine learning. We invite submissions of research papers that cover the following topics:

· Intelligent Computing for Engineering and Management

· Control systems design using machine learning

· Reinforcement learning for control systems

· Machine learning-based fault detection and diagnosis in control systems

· Intelligent control systems for robotics and automation

· Adaptive and learning-based control methods

· Real-time control systems and machine learning

· Fuzzy control theory in decision analysis

· Fuzzy clustering

· Computational intelligence

· Algorithms for Machine learning and Neural Network

· Hybrid control systems

· Applications of control systems and machine learning in process control, power systems, transportation, and other fields


The special issue will feature high-quality research papers that provide new insights into the integration of control systems and machine learning, with a focus on real-world applications. We encourage submissions from both academics and industry professionals to showcase their latest research and practical implementations.


The objectives of this special issue are:

· To bring together original research contributions that highlight the latest developments and innovations in the integration of control systems and machine learning.

· To showcase the advancements and applications of control systems and machine learning in various domains, including robotics, automation, and process control.

· To explore the challenges and opportunities in the use of machine learning for control systems, and to propose solutions to overcome these challenges.

· To provide a platform for researchers and industry professionals to share their experiences and best practices in applying machine learning to control systems.


Overall, this special issue will provide a platform for researchers and industry professionals to showcase their latest research and practical implementations of machine learning in control systems. It will contribute to advancing the field by promoting the integration of machine learning with control systems and highlighting the potential benefits and challenges.



Published Papers


  • Open Access

    REVIEW

    Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques: A Comprehensive Review and Open Challenges

    Samina Amin, Muhammad Ali Zeb, Hani Alshahrani, Mohammed Hamdi, Mohammad Alsulami, Asadullah Shaikh
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1167-1202, 2024, DOI:10.32604/cmes.2023.043921
    (This article belongs to the Special Issue: Control Systems and Machine Learning for Intelligent Computing)
    Abstract Social media (SM) based surveillance systems, combined with machine learning (ML) and deep learning (DL) techniques, have shown potential for early detection of epidemic outbreaks. This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance. Since, every year, a large amount of data related to epidemic outbreaks, particularly Twitter data is generated by SM. This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM, along with the ML and DL techniques that… More >

  • Open Access

    ARTICLE

    Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

    Mani Parimala, Karthikeyan Prakash, Ashraf Al-Quran, Muhammad Riaz, Saeid Jafari
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1095-1118, 2024, DOI:10.32604/cmes.2023.031193
    (This article belongs to the Special Issue: Control Systems and Machine Learning for Intelligent Computing)
    Abstract The idea of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong model for machine learning and optimization under uncertainty. The activity times in the critical path method (CPM) representation procedures approach are initially static, but in the Project Evaluation and Review Technique (PERT) approach, they are probabilistic. This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy (LDF) environment. The LDF expected task time, LDF variance, LDF critical path, and LDF total expected time for determining the project network… More >

    Graphic Abstract

    Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

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