Recent Trends and Advances in Machine Learning and Data Mining

Dear Colleagues,

In recent years, machine learning and data mining have evolved from specialized technical fields to pivotal drivers of innovation across academia and industry. With the explosion of multi-modal big data, the increasing computing power of hardware, and the deepening of theoretical research, these technologies have witnessed breakthroughs in fundamental algorithms, scenario-specific applications, and interdisciplinary integration while opening up new frontiers in emerging domains. The latest advances in machine learning and data mining are revolutionizing research paradigms and practical landscapes across the world.

This Special Issue aims to provide a comprehensive overview of the latest theoretical advances, methodological innovations, and practical breakthroughs in machine learning and data mining. We welcome original research papers, reviews, and perspectives that address various aspects of theories, techniques, and applications of data mining and machine learning, including (but not limited to) the following:

• Advances in fundamental machine learning paradigms;
• Advanced data modeling, data mining, data fusion, and processing technologies and the enhancement of existing methods;
• Advanced technologies and applications of artificial intelligence;
• Ethics, privacy, and security in data mining, including privacy concerns, data governance, potential for bias in data-driven models, etc.;
• Artificial intelligence techniques for exploring applications, including a range of advanced methodologies such as machine learning, deep learning, optimization algorithms, etc.;
• Domain-specific applications and case studies demonstrating how state-of-the-art machine learning and data mining technologies solve practical problems in education, policymaking, health, transportation, commerce, social science, IOT, etc.;
• Advanced approaches for examining the use of data mining or machine learning in multiple application scenarios;
• Generative AI such as large language models (LLMs) for smart education;
• Data governance strategies and frameworks.

Dr. Jun Zhang
Dr. Shengbo Liu
Dr. Yuan Lin
Guest Editors

Manuscript Submission Information

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