Intelligent Computing: Models, Algorithms, and Applications

Dear Colleagues,

Intelligent computing has emerged as a core enabling technology driving breakthroughs across scientific research, industrial applications, and daily life, integrating advances in artificial intelligence, machine learning, optimization, data science, high-performance computing, and interdisciplinary applications. This Special Issue, “Intelligent Computing: Models, Algorithms, and Applications”, aims to collect state-of-the-art theoretical innovations, methodological improvements, and practical deployments that will advance the foundations and real-world utility of intelligent computing systems.

This Special Issue welcomes original research articles, comprehensive reviews, and technical communications covering a broad spectrum of topics. In terms of computational models, it will focus on novel neural network architectures, foundation models, probabilistic models, evolutionary computation frameworks, hybrid intelligent models, and lightweight or efficient model designs suitable for constrained environments. Regarding algorithms, contributions should showcase advanced optimization methods, deep learning training and inference algorithms, federated and distributed learning strategies, reinforcement learning algorithms, and explainable and robust algorithmic designs, as well as algorithmic improvements for large-scale data processing and high-dimensional computing.

In the domain of applications, the Special Issue encourages work that deploys intelligent computing in real-world scenarios, including, but not limited to, computer vision, natural language processing, bioinformatics, healthcare analytics, smart manufacturing, transportation systems, financial intelligence, environmental monitoring, edge computing, Internet of Things (IoT), and scientific computing. It will also emphasize interdisciplinary integration, such as intelligent computing combined with physics, chemistry, materials science, and engineering disciplines.

By gathering high-quality research addressing theoretical challenges, algorithmic efficiency, model interpretability, scalability, and practical deployment issues, this Special Issue seeks to promote a deeper understanding of intelligent computing paradigms, foster cross-field collaboration, and support the development of reliable, efficient, and adaptable intelligent systems. It will serve as a valuable reference for researchers, engineers, and practitioners working at the frontier of artificial intelligence, computational intelligence, and data-driven engineering applications.

Dr. Guoqing Li
Dr. Yirui Wang
Dr. Yu Xin
Dr. Qianlong Dang
Guest Editors

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