Machine Learning Models and Architectures for Biomedical Signal Processing
Herausgeber: Tripathi, Suman Lata; Banerjee, Soumya; Mahmud, Mufti; Balas, Valentina Emilia
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Machine Learning Models and Architectures for Biomedical Signal Processing
Herausgeber: Tripathi, Suman Lata; Banerjee, Soumya; Mahmud, Mufti; Balas, Valentina Emilia
- Broschiertes Buch
Produktdetails
- Verlag: Elsevier Science
- Seitenzahl: 400
- Erscheinungstermin: 1. November 2024
- Englisch
- ISBN-13: 9780443221583
- ISBN-10: 0443221588
- Artikelnr.: 70701757
Section 1: Introduction to bioinformatics 1.1 Recent trends of
bioinformatics 1.2 Biomedical signal processing technique 1.3 Transfer
Learning based Arrhythmia classification using Electrocardiogram Section 2:
Machine learning models for biomedical signal processing 2.1 Exploring
Machine Learning Models for Biomedical Signal Processing: A Comprehensive
Review 2.2 Machine Learning for Audio Processing: From Feature Extraction
to Model Selection 2.3 Pre-processing of MRI images suitable for Artificial
Intelligence-based Alzheimer’s Disease classification 2.4 Machine Learning
Models for Text and Image Processing 2.5 Assistive Technology for
Neuro-rehabilitation Applications Using Machine Learning Techniques 2.6
Deep Learning Architectures in Computer Vision based Medical Imaging
Applications with Emerging Challenges 2.7 Relevance of Artificial
Intelligence, Machine Learning, and Biomedical Devices to Healthcare
Quality and patient Outcomes 2.8 AI-Based ECG Signal processing
applications 2.9 Deep Learning Approach for the Prediction of Skin Diseases
Section 3: Brain computer interfaces (BCI) 3.1 Brain-Computer Interface 3.2
Analysis on Types of Brain-Computer Interfaces for Disabled Person 3.3
Brain Computer Interfaces for elderly and disabled person Section 4: Real
time architecture design for biomedical signals 4.1 Machine learning model
implementation with FPGA’S 4.2 Smart Biomedical Devices for Smart
Healthcare 4.3 FPGA implementation for explainable machine learning and
deep learning models to real time problems Section 5: Software and
Hardware-based Applications for biomedical Informatics 5.1 Software
Applications for Biometric Informatics 5.2 Smart Medical Devices: Making
Health Care More Intelligent 5.3 Security modules for biomedical signal
processing 5.4 Artificial intelligence-based diagnostic tool for
cardiovascular risk prediction 5.5 Machine Learning Algorithm approach in
risk prediction of Liver Cancer
bioinformatics 1.2 Biomedical signal processing technique 1.3 Transfer
Learning based Arrhythmia classification using Electrocardiogram Section 2:
Machine learning models for biomedical signal processing 2.1 Exploring
Machine Learning Models for Biomedical Signal Processing: A Comprehensive
Review 2.2 Machine Learning for Audio Processing: From Feature Extraction
to Model Selection 2.3 Pre-processing of MRI images suitable for Artificial
Intelligence-based Alzheimer’s Disease classification 2.4 Machine Learning
Models for Text and Image Processing 2.5 Assistive Technology for
Neuro-rehabilitation Applications Using Machine Learning Techniques 2.6
Deep Learning Architectures in Computer Vision based Medical Imaging
Applications with Emerging Challenges 2.7 Relevance of Artificial
Intelligence, Machine Learning, and Biomedical Devices to Healthcare
Quality and patient Outcomes 2.8 AI-Based ECG Signal processing
applications 2.9 Deep Learning Approach for the Prediction of Skin Diseases
Section 3: Brain computer interfaces (BCI) 3.1 Brain-Computer Interface 3.2
Analysis on Types of Brain-Computer Interfaces for Disabled Person 3.3
Brain Computer Interfaces for elderly and disabled person Section 4: Real
time architecture design for biomedical signals 4.1 Machine learning model
implementation with FPGA’S 4.2 Smart Biomedical Devices for Smart
Healthcare 4.3 FPGA implementation for explainable machine learning and
deep learning models to real time problems Section 5: Software and
Hardware-based Applications for biomedical Informatics 5.1 Software
Applications for Biometric Informatics 5.2 Smart Medical Devices: Making
Health Care More Intelligent 5.3 Security modules for biomedical signal
processing 5.4 Artificial intelligence-based diagnostic tool for
cardiovascular risk prediction 5.5 Machine Learning Algorithm approach in
risk prediction of Liver Cancer
Section 1: Introduction to bioinformatics 1.1 Recent trends of
bioinformatics 1.2 Biomedical signal processing technique 1.3 Transfer
Learning based Arrhythmia classification using Electrocardiogram Section 2:
Machine learning models for biomedical signal processing 2.1 Exploring
Machine Learning Models for Biomedical Signal Processing: A Comprehensive
Review 2.2 Machine Learning for Audio Processing: From Feature Extraction
to Model Selection 2.3 Pre-processing of MRI images suitable for Artificial
Intelligence-based Alzheimer’s Disease classification 2.4 Machine Learning
Models for Text and Image Processing 2.5 Assistive Technology for
Neuro-rehabilitation Applications Using Machine Learning Techniques 2.6
Deep Learning Architectures in Computer Vision based Medical Imaging
Applications with Emerging Challenges 2.7 Relevance of Artificial
Intelligence, Machine Learning, and Biomedical Devices to Healthcare
Quality and patient Outcomes 2.8 AI-Based ECG Signal processing
applications 2.9 Deep Learning Approach for the Prediction of Skin Diseases
Section 3: Brain computer interfaces (BCI) 3.1 Brain-Computer Interface 3.2
Analysis on Types of Brain-Computer Interfaces for Disabled Person 3.3
Brain Computer Interfaces for elderly and disabled person Section 4: Real
time architecture design for biomedical signals 4.1 Machine learning model
implementation with FPGA’S 4.2 Smart Biomedical Devices for Smart
Healthcare 4.3 FPGA implementation for explainable machine learning and
deep learning models to real time problems Section 5: Software and
Hardware-based Applications for biomedical Informatics 5.1 Software
Applications for Biometric Informatics 5.2 Smart Medical Devices: Making
Health Care More Intelligent 5.3 Security modules for biomedical signal
processing 5.4 Artificial intelligence-based diagnostic tool for
cardiovascular risk prediction 5.5 Machine Learning Algorithm approach in
risk prediction of Liver Cancer
bioinformatics 1.2 Biomedical signal processing technique 1.3 Transfer
Learning based Arrhythmia classification using Electrocardiogram Section 2:
Machine learning models for biomedical signal processing 2.1 Exploring
Machine Learning Models for Biomedical Signal Processing: A Comprehensive
Review 2.2 Machine Learning for Audio Processing: From Feature Extraction
to Model Selection 2.3 Pre-processing of MRI images suitable for Artificial
Intelligence-based Alzheimer’s Disease classification 2.4 Machine Learning
Models for Text and Image Processing 2.5 Assistive Technology for
Neuro-rehabilitation Applications Using Machine Learning Techniques 2.6
Deep Learning Architectures in Computer Vision based Medical Imaging
Applications with Emerging Challenges 2.7 Relevance of Artificial
Intelligence, Machine Learning, and Biomedical Devices to Healthcare
Quality and patient Outcomes 2.8 AI-Based ECG Signal processing
applications 2.9 Deep Learning Approach for the Prediction of Skin Diseases
Section 3: Brain computer interfaces (BCI) 3.1 Brain-Computer Interface 3.2
Analysis on Types of Brain-Computer Interfaces for Disabled Person 3.3
Brain Computer Interfaces for elderly and disabled person Section 4: Real
time architecture design for biomedical signals 4.1 Machine learning model
implementation with FPGA’S 4.2 Smart Biomedical Devices for Smart
Healthcare 4.3 FPGA implementation for explainable machine learning and
deep learning models to real time problems Section 5: Software and
Hardware-based Applications for biomedical Informatics 5.1 Software
Applications for Biometric Informatics 5.2 Smart Medical Devices: Making
Health Care More Intelligent 5.3 Security modules for biomedical signal
processing 5.4 Artificial intelligence-based diagnostic tool for
cardiovascular risk prediction 5.5 Machine Learning Algorithm approach in
risk prediction of Liver Cancer