Advancements in Artificial Intelligence, Machine Learning, and Deep Learning Across Multidisciplinary Domains: Innovations, Applications, and Challenges

TOPICAL COLLECTIONS

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Guest Editor

Anas Bilal

College of Information Science and Technology, Hainan Normal University

Dr. Anas Bilal, an esteemed scholar at the College of Information Science and Technology, Hainan Normal University in Haikou, China, is recognized among the Top 2% Scientists for 2024 by Stanford University and Elsevier. Dr. Bilal has authored over 50 technical articles in prestigious journals and conference proceedings. His research spans cutting-edge areas, including artificial intelligence, computer vision, image processing, medical imaging, remote sensing, and pattern recognition.A highly regarded senior member of IEEE, Dr. Bilal holds distinguished editorial and advisory positions and actively contributes to organizing international conferences. He has served in key leadership positions, such as the International Advisory Chair at BDAITM 2023 in China. He was also a keynote speaker at ICCSEI 2024 on advancements in computer science and education. Dr. Anas Bilal is an active member of various professional societies, including the IEEE Computer Society Technical Committees on Pattern Analysis and Machine Intelligence (PAMI-TC) and Computer Architecture. He is also a member of the British Machine Vision Association (BMVA) and IEEE Young Professionals. Committed to advancing computational methodologies, Dr. Bilal has developed pioneering techniques in medical and remote sensing imagery.

Collection Overview

Advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are transforming industries, ranging from healthcare and education to business and environmental science. However, the widespread adoption of these technologies is often hindered by challenges such as computational inefficiency, scalability issues, and ethical concerns.

This collection aims to address these barriers by presenting cutting-edge AI/ML/DL applications and algorithms that offer innovative solutions across diverse domains. With a focus on personalized healthcare, smart cities, AI-driven education, and sustainability, the collection seeks to bridge the gap between theoretical advancements and real-world applications. It seeks to provide tangible solutions to industry-specific challenges while tackling ethical, social, and operational considerations. This work will contribute to the ongoing progress of AI/ML/DL, supporting their effective and responsible integration into various fields.

Topics of interest include:

AI for global impact
Smart cities and AI integration
Sustainable AI for environmental solutions
Machine learning for healthcare innovation
Ethics in AI and data science
AI for societal transformation
Deep learning for cross-domain applications
AI-powered decision-making systems
AI in education and personalized learning
Interdisciplinary AI solutions for industries