Data-Driven Approaches for Big Data Analysis of Intelligent Systems

Dear Colleagues,

The rapid evolution of intelligent systems has generated vast, heterogeneous datasets, demanding advanced mathematical frameworks to unlock actionable insights. This Special Issue, "Data-Driven Approaches for Big Data Analysis of Intelligent Systems", focuses on rigorous computational methodologies, including statistical learning models, optimization algorithms, and stochastic processes, to address scalability, efficiency, and robustness in intelligent systems. Key topics encompass machine learning architectures (e.g., deep neural networks and reinforcement learning), distributed computing paradigms (e.g., MapReduce and Spark), and mathematical techniques for high-dimensional data (e.g., tensor decomposition and sparse linear algebra). Computational analysis of time-series forecasting, nonlinear dynamics, and graph-based algorithms for network-structured data will be explored. Submissions should emphasize mathematical innovations, such as differential equations for predictive modeling, convex/non-convex optimization for parameter tuning, or probabilistic graphical models for uncertainty quantification. This issue aims to bridge mathematical theory with real-world implementations, fostering advancements in intelligent systems through data-driven computational science.

Dr. Qiuyang Huang
Guest Editor

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