(Ebook PDF) Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models 1st edition by Jorge Garza Ulloa 0128209348 9780128209349 full chapters

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ISBN-10 ‏ : ‎0128209348 

ISBN-13 ‏ : ‎ 9780128209349

Author :  Jorge Garza Ulloa 

Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body.

Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models 1st Table of contents:

Chapter 1. Biomedical engineering and the evolution of artificial intelligence
Abstract
1.1 Introduction
1.2 Biomedical engineering
1.3 Artificial intelligence
1.4 Machine learning
1.5 Deep learning
1.6 Cognitive science
1.7 Neuroscience, cognitive science, and AI models
References
Chapter 2. Introduction to Cognitive Science, Cognitive Computing, and Human Cognitive relation to help in the solution of Artificial Intelligence Biomedical Engineering problems
Abstract
2.1 Introduction
2.2 Brain, spinal cord, and nerves
2.3 Neurons and neural pathways in cognition
2.4 Cognitive science
2.5 Natural Language Processing
2.6 MATLAB® toolboxes solution for natural language processing
2.7 Cloud service and AI
2.8 IBM Cloud, IBM Watson, and Cognitive apps
2.9 The future of the relationship between cognitive science, cognitive computing, and human cognition
References
Further reading
Chapter 3. Artificial Intelligence Models Applied to Biomedical Engineering
Abstract
3.1 Introduction artificial intelligence and biomedical engineering
3.2 AI optimization in biomedical engineering
3.3 Evolutionary algorithms for AI optimization in BME
3.4 IBM Watson Studio for artificial intelligence
3.5 Examples of applications of evolutionary algorithms with other AI tools in biomedical engineering
References
Chapter 4. Machine Learning Models Applied to Biomedical Engineering
Abstract
4.1 Introduction
4.2 Choosing the best ML model
4.3 ML clusters, classification, and regression models
4.4 Naive Bayes family models for supervised learning
4.5 k-Nearest neighbor family models for supervised learning
4.6 Decision trees family models for supervised learning
4.7 Support vector machine family members
4.8 Artificial neural network family models
4.9 Discriminant analysis family models
4.10 Logistic regression classifier
4.11 Ensemble classifiers family models
4.12 IBM ML Solution: IBM Watson SPSS
References
Further reading
Chapter 5. Deep Learning Models Principles Applied to Biomedical Engineering
Abstract
5.1 Deep learning based on artificial neural networks
5.2 Feed forward neural networks types
5.3 Shallow neural network
5.4 Backpropagation neural networks types
5.5 Transfer learning from pretrained deep learning networks
References
Chapter 6. Deep Learning Models Evolution Applied to Biomedical Engineering
Abstract
6.1 Deep learning models evolution
6.2 Recurrent neural networks types
6.3 Memory augmented neural networks types
6.4 Modular Neural Networks types
6.5 Evolutionary Deep Neural Networks types
References
Further reading
Chapter 7. Cognitive learning and reasoning models applied to biomedical engineering
Abstract
7.1 Introduction
7.2 Artificial intelligence and Cognitive Computing Agents System (AI-CCAS)
7.3 Inference engine and research example
7.4 Action generation
7.5 Business intelligence in healthcare
7.6 Learning and reasoning relationship of biomedical engineering, cognitive science, and computer science through artificial intelligent models
7.7 Cognitive Learning and Reasoning research example applying AI-CCAS framework
7.8 Challenge research for “Applied Biomedical Engineering using Artificial Intelligence and Cognitive 

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