Fractional-Order Modeling in Data-Driven Intelligent Systems: From Optimization to AI/ML Applications

Dear Colleagues,

The explosive growth of data and the increasing complexity of intelligent systems have ushered in a new era where optimization, Big Data technologies, and AI/ML (Artificial Intelligence and Machine Learning) must be tightly integrated. From massive-scale data analytics to real-time decision-making in uncertain environments, modern intelligent systems demand solutions that are not only data-driven, but also theoretically sound, scalable, and robust.

This Special Issue serves as a platform to advance the theoretical foundations and practical methodologies that drive scalable and intelligent systems, with a special focus on the intersection of optimization algorithm, Big Data infrastructure, and AI/ML algorithm design. We particularly welcome contributions that explore novel algorithmic paradigms, efficient system architectures, and robust decision-making strategies tailored for large-scale, data-intensive environments. Approaches that incorporate memory-aware modeling, such as those based on fractional-order calculus or fractal structures, are of special interest, as they offer enhanced flexibility and precision in capturing the complex dynamics of modern intelligent systems.

Topics of interest include, but are not limited to, the following:

1. Fractional-order control and optimization algorithm for AI/ML
2. Control theoretic analysis and design of ML algorithms (e.g., stability, robustness, convergence)
3. Optimization-driven AI and ML frameworks for high-dimensional or streaming data
4. Integration of optimization, control, and ML in Big Data environments
5. Scalable and distributed optimization methods for large-scale ML models
6. Safe and explainable AI through control and optimization perspectives
7. Real-time ML and optimization for dynamic data streams in edge/cloud computing
8. Interdisciplinary methods bridging optimization theory, control, and data science

We welcome original research articles, comprehensive review papers, and experimental studies that explore novel methodologies or demonstrate real-world impact through the integration of optimization, Big Data, and AI/ML, while embracing the analytical rigor and system insight provided by control theory and fractional-order methodologies.

Prof. Dr. Yan Li
Dr. Ziquan Yu
Guest Editors

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