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
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ISBN-13
978-0-443-22157-6 (9780443221576)
Schweitzer Klassifikation
Section 1: Introduction to bioinformatics1.1 Recent trends of bioinformatics1.2 Biomedical signal processing technique1.3 Transfer Learning based Arrhythmia classification using ElectrocardiogramSection 2: Machine learning models for biomedical signal processing2.1 Exploring Machine Learning Models for Biomedical Signal Processing: A Comprehensive Review2.2 Machine Learning for Audio Processing: From Feature Extraction to Model Selection2.3 Pre-processing of MRI images suitable for Artificial Intelligence-based Alzheimer's Disease classification2.4 Machine Learning Models for Text and Image Processing2.5 Assistive Technology for Neuro-rehabilitation Applications Using Machine Learning Techniques2.6 Deep Learning Architectures in Computer Vision based Medical Imaging Applications with Emerging Challenges2.7 Relevance of Artificial Intelligence, Machine Learning, and Biomedical Devices to Healthcare Quality and patient Outcomes2.8 AI-Based ECG Signal processing applications2.9 Deep Learning Approach for the Prediction of Skin DiseasesSection 3: Brain computer interfaces (BCI)3.1 Brain-Computer Interface3.2 Analysis on Types of Brain-Computer Interfaces for Disabled Person3.3 Brain Computer Interfaces for elderly and disabled personSection 4: Real time architecture design for biomedical signals4.1 Machine learning model implementation with FPGA'S 4.2 Smart Biomedical Devices for Smart Healthcare4.3 FPGA implementation for explainable machine learning and deep learning models to real time problemsSection 5: Software and Hardware-based Applications for biomedical Informatics5.1 Software Applications for Biometric Informatics5.2 Smart Medical Devices: Making Health Care More Intelligent5.3 Security modules for biomedical signal processing5.4 Artificial intelligence-based diagnostic tool for cardiovascular risk prediction5.5 Machine Learning Algorithm approach in risk prediction of Liver Cancer
