Mathematical Modelling in Artificial Intelligence, Information Systems and Big Data

Dear Colleagues,

The rapid advancement of Artificial Intelligence (AI), Information Systems, and Big Data technologies has transformed the way organizations, industries, and societies process information, make decisions, and solve complex problems. Mathematical modelling plays a fundamental role in these advancements by providing rigorous analytical frameworks for understanding complex systems, optimizing decision-making processes, predicting future trends, and extracting valuable insights from massive and heterogeneous datasets.

Recent developments in AI, machine learning, deep learning, information systems, and data analytics have generated unprecedented opportunities for the application of mathematical models across diverse domains, including healthcare, finance, smart cities, cybersecurity, transportation, social networks, manufacturing, and digital transformation. Mathematical modelling enables researchers and practitioners to design intelligent algorithms, improve prediction accuracy, optimize system performance, and support evidence-based decision-making in increasingly data-driven environments.

Despite significant progress, several important challenges remain. These include the following: (1) developing scalable mathematical models capable of handling high-dimensional and large-scale datasets, (2) improving the interpretability and reliability of AI-driven models, (3) addressing uncertainty and dynamic behaviours in complex information systems, and (4) integrating advanced optimization, statistical, and computational techniques for real-world applications.

The Special Issue aims to provide a platform for researchers and practitioners to present recent advances, innovative methodologies, and practical applications of mathematical modelling in Artificial Intelligence, Information Systems, and Big Data. We welcome original research contributions that address theoretical developments, computational techniques, and interdisciplinary applications that leverage mathematical modelling to solve contemporary challenges in intelligent and data-centric systems.

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

• Mathematical modelling for Artificial Intelligence and Machine Learning;
• Mathematical foundations of Deep Learning and Neural Networks;
• Optimization algorithms and computational intelligence;
• Big Data analytics and predictive modelling;
• Statistical learning theory and data-driven decision-making;
• Mathematical models for information systems and digital transformation;
• Explainable AI and interpretable machine learning models;
• Data mining, knowledge discovery, and pattern recognition;
• Stochastic modelling and uncertainty quantification;
• Network analysis and graph-based mathematical models;
• Mathematical modelling for cybersecurity and privacy protection;
• Mathematical models for smart cities and intelligent transportation systems;
• Healthcare analytics and biomedical data modelling;
• Time-series forecasting and predictive analytics;
• Operations research and optimization in large-scale systems;
• Reinforcement learning and decision-theoretic models;
• Mathematical approaches for social network analysis;
• Computational methods for complex and dynamic systems;
• Hybrid AI models integrating statistical and mathematical techniques.

Dr. Sungjin Yu
Guest Editor

Manuscript Submission Information

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• mathematical modelling
• artificial intelligence
• big aata analytics
• information systems
• machine learning
• AI security