New Applications of Big Data Technology: Integration of Data Mining and Artificial Intelligence

Dear Colleagues,

The landscape of data mining and machine learning is rapidly evolving, fuelled by advancements in algorithms, computational power, and the availability of vast datasets. This Topic will explore the latest trends and innovations shaping the future of these fields. Key areas of interest include, but are not limited to, deep learning architectures, reinforcement learning, unsupervised and semi-supervised learning techniques, federated learning, and the integration of machine learning with big data technologies. We invite contributions that address novel approaches and methodologies, including improvements in model interpretability, the development of more efficient algorithms, and the application of machine learning in diverse domains such as healthcare, finance, engineering, material science, and social networks. Special emphasis will be placed on emerging topics like generative AI, explainable AI (XAI), edge AI, and the ethical implications of AI deployment. In the realm of data mining, we are particularly interested in new techniques for anomaly detection, pattern recognition, and predictive analytics. Papers exploring the convergence of data mining with AI technologies, such as using deep learning for feature extraction or leveraging generative models for data augmentation, are highly encouraged. By bringing together cutting-edge research and practical applications, this Topic will provide a comprehensive overview of the current state and future directions of data mining and machine learning. We encourage submissions that offer theoretical insights, empirical studies, and case studies demonstrating the transformative impacts of these technologies. Join us in contributing to this exciting discourse and advancing our field through collaborative knowledge-sharing.

Prof. Dr. Xujuan Zhou
Prof. Dr. Yuefeng Li
Prof. Dr. Raj Gururajan
Prof. Dr. Ji Zhang
Prof. Dr. Revathi Venkataraman
Topic Editors