Soumya Banerjee - Böcker

Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques.Covers the hardware architecture implementation of machine learning algorithmsDiscusses the software implementation approach and the efficient hardware of machine learning application with FPGAPresents the major design challenges and research potential in machine learning techniques

This book constitutes the refereed post-proceedings of the 9th International Conference on Mobile, Secure, and Programmable Networking, MSPN 2023, held in Paris, France, during October 26–27, 2023. The 15 full papers included in this book were carefully reviewed and selected from 31 submissions.