Machine Learning Models and Architectures for Biomedical Signal Processing

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Machine Learning Models and Architectures for Biomedical Signal Processing is written by Suman Lata Tripathi and published by Elsevier. It's available with International Standard Book Number or ISBN identification 044322157X (ISBN 10) and 9780443221576 (ISBN 13).

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 algorithms - Discusses the software implementation approach and the efficient hardware of machine learning application with FPGA - Presents the major design challenges and research potential in machine learning techniques