The 11th International Conference on Fuzzy Systems and Data Mining (FSDM 2025)

Abstract: Wavelets, renowned for their ability to perform multi-resolution analysis and capture localized time-frequency representations, have long been instrumental in fields like signal processing and image analysis. However, their integration into machine learning opens up exciting new possibilities for addressing critical challenges such as noise reduction, feature extraction, dimensionality reduction, and multi-scale analysis. This talk explores the untapped potential of wavelets within the context of machine learning, highlighting their applications across diverse tasks, including classification, regression, and generative modeling. By examining how wavelets can enhance neural network architectures, improve data preprocessing, and support efficient feature learning, this discussion aims to inspire innovative approaches at the intersection of wavelet theory and machine learning. Emphasis will also be placed on the challenges of designing wavelet-based frameworks, the trade-offs between interpretability and automation, and the prospects for wavelets in emerging ML paradigms such as transformers, reinforcement learning, and federated learning. This talk serves as a call to action for researchers and practitioners to harness the unique strengths of wavelets, fostering interdisciplinary innovation in artificial intelligence and data science.

Biography: Dr. Sayan Kaennakham, an Associate Professor at Suranaree University of Technology, Thailand, specializes in Computational Fluid Dynamics, a field he delved into during his Ph.D. at Coventry University, UK. He holds a Senior Fellowship with the UKPSF, underlining his academic prowess. His work is focused on areas like data science, neural networks, machine learning, and AI-driven innovation. He's authored notable books including ‘Mathematics in Daily Life’ and ‘An Introduction to a Collocation Meshless Method for DEs’. As a Graduate Program Coordinator, he leads modules in Applied Machine Learning and Scientific Data Analysis. Involved in various cutting-edge research projects, Dr. Kaennakham contributes significantly to the fields of medical imaging diagnosis and smart healthcare solutions through AI. His commitment to interdisciplinary research is evident in his roles in the Multidisciplinary Innovation Research Centre and the Applied and Computational Mathematics Research Group.