Guest Editors
Dr. MUHAMMAD ADNAN KHAN, Lahore Garrison University, Pakistan.
Dr. RIZWAN ALI NAQVI, Sejong University, Korea.
Dr. MOHAMMED A. ALGHAMDI, Umm Al-Qura University, Saudi Arabia.
Summary
Machine learning has been a subject of increasing concern to scholars, both from academia and business, over the past few years. Unlike conventional learning methods, machine learning methods suggest the potential to learn and develop very broad sets of data. Machine learning methods in computer vision, natural language analysis, robots, and other fields have gained considerable popularity in numerous activities. Recent years have seen a tremendous advancement of the principle of machine learning and numerous implementations in the general area of artificial intelligence, including neural network architecture, automation, statistical analysis and deep learning.
Though machine learning has been extensively explored in recent decades, the use of machine learning strategies in intelligent systems faces several complexities. Well first of all, machine learning methods need a vast and varied amount of data as input to frameworks and provide a wide range of training requirements. Secondly, the teaching of machine learning models is quick to slip into overfitting issues. Furthermore, because machine learning systems have uncertainty or backbox problems, it is challenging to consider how a given algorithm makes a judgment, which is essential in certain fields such as financial trading or medical diagnosis.
Suggested topics include, but are not limited to, the following:
• Agent and Multi-Agent Systems
• Artificial Intelligence Applications
• Artificial Neural Networks
• Autonomous and Ubiquitous Computing
• Biomedical systems
• Colour/Image Analysis
• Computational Intelligence
• Computer Vision
• Cybersecurity and AI
• Distributed AI Systems and Architectures
• eBusiness, eCommerce, eHealth, eLearning
• Finance and AI
• Extreme Machine Learning
• Forensic Science
• Grid-Based Computing
• Internet of Things (IoT), IoMT, AIoT & AIoMT
• Medical Informatics and Biomedical
• Natural Language Processing
• Object and Face Recognition
• Pattern Recognition
• Robotics and Virtual Reality
• Signal and Image Processing
• Signal Processing Techniques
• Knowledge Extraction
• Smart Grids
• Smart City
• Time Series and Forecasting
Keywords
• AIoT
• IoMT
• MUD
• Fuzzy
• Swarm Intelligence
• Evolutionary Algorithms
• Neural Networks
• Extreme machine learning
• Smart Health
• Smart Traffic
• Intelligent Bussiness
• Image Processing
Published Papers
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