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This book comprises original and peer reviewed research papers presented at 2025 17th International Conference on Machine Learning and Computing that was held in Guangzhou, China, from February 14 to 17, 2025. The focus of the conference is to establish an effective platform for institutions and industries to share ideas and to present the works of scientists, engineers, educators and students from all over the world. Topics discussed in this volume include Machine Learning Theory and Algorithms, High-performance Computing Models and Data Processing, Large-scale Language Models and Natural Language Processing, Data-oriented Information System Optimization and Intelligent Computing, AI-based Intelligent Control Systems and System Security, etc. The book will become a valuable resource for academics, industry professionals, and engineers working in the related fields of machine learning and computing.
Dr. Lin Huang is a professor in the Department of Engineering and Engineering Technology (EAET) at Metropolitan State University of Denver, where she has been a faculty member since 2010. Dr. Huang received her Ph.D. in Electrical Engineering from Florida Atlantic University in USA. Her research interests include Biometrics, Pattern Recognition, Signal Processing, Computer Vision, Machine Learning, Embedded System Design and VLSI. Dr. Huang was recognized in the post "20 Professors in Engineering Technology to Know" on the website of OnlineEngineeringPrograms.com, an online resource for prospective students interested in the Engineering Field. The list is comprised of some of the outstanding professors and Universities in the field. Dr. Huang is a member of the International Association of Computer Science and Information Technology (IACSIT). In addition, Dr. Huang was a guest editor for EDP Sciences in 2016. In 2017, she joined the editorial board of International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE) and joined the team as an honorary member of Editorial Board of the journal, International Journal of Darshan Institute on Engineering Research and Emerging Technologies. She has been the editor-in-chief for International Journal of Machine Learning (IJML) since 2012. And she has been reviewing papers on regular basis for some conferences and journals since 2008.
Professor David Greenhalgh gained a PhD from the University of Cambridge in 1984 and worked at Imperial College, London from 1984 to 1986. He also has a first-class Honours degree in Mathematics and a distinction in Part III Mathematics. He is currently a member of the Population Modelling and Epidemiology Research Group at Strathclyde and has been a member of staff at Strathclyde in the Departments of Mathematics, Statistics and Modelling Science and Mathematics and Statistics since 1986. He is currently (since 2017) a full professor in the Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK, Postgraduate Director (Mathematics and Statistics) at Strathclyde and also Associate Editor of Journal of Biological Systems. He has published over 110 publications in international refereed journals, supervised over 20 MPhil and PhD research students and been on the editorial board of eighteen international journals. In 2015 he was awarded a two-year (2015-2017) Leverhulme Trust Research Fellowship grant (50K RF-2015-88) as PI to study mathematical modelling of vaccination against dengue. He has also been involved in collaboration with Malaysia to mathematically model a mosquito trap to control dengue and won a 187K grant from the Newton Fund to do this in 2016. His main research interests are in mathematical and statistical epidemiology, but he has also done some work in genetic algorithms and signal and image processing.
Image Feature Analysis and Processing Technology.- Intelligent Detection Models and Algorithms.- Multimodal Image Intelligent Recognition and Calculation.- Image Segmentation and Classification.- Signal Recognition and Key Technologies.- Information Security Detection and Analysis.- Text Analysis and Classification.
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