Handbook of Artificial Intelligence in Biomedical Engineering

Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, G.S. Mahalakshmi, Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, G.S. Mahalakshmi

• Verlag: Apple Academic Press
• Jahr: 2021
• ISBN:
  9781000067675
• Auflage: 1
• Thema: Technik & Maschinenbau
• Thema: Biomedizinwissenschaft

Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts.

Inhaltsverzeichnis

• Cover
• Half Title
• Title Page
• Copyright Page
• Series Page
• About the Editors
• Contents
• Contributors
• Abbreviations
• Preface
• 1. Design of Medical Expert Systems Using Machine Learning Techniques
• 2. From Design Issues to Validation: Machine Learning in Biomedical Engineering
• 3. Biomedical Engineering and Informatics Using Artificial Intelligence
• 4. Hybrid Genetic Algorithms for Biomedical Applications
• 5. Healthcare Applications of the Biomedical AI System
• 6. Applications of Artificial Intelligence in Biomedical Engineering
• 7. Biomedical Imaging Techniques Using AI Systems
• 8. Analysis of Heart Disease Prediction Using Machine Learning Techniques
• 9. A Review on Patient Monitoring and Diagnosis Assistance by Artificial Intelligence Tools
• 10. Semantic Annotation of Healthcare Data
• 11. Drug Side Effect Frequency Mining over a Large Twitter Dataset using Apache Spark
• 12. Deep Learning in Brain Segmentation
• 13. Security and Privacy Issues in Biomedical AI Systems and Potential Solutions
• 14. LiMoS—Live Patient Monitoring System
• 15. Real-Time Detection of Facial Expressions Using k-NN, SVM, Ensemble classifier and Convolution Neural Networks
• 16. Analysis and Interpretation of Uterine Contraction Signals Using Artificial Intelligence
• 17. Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques
• 18. Deployment of Supervised Machine Learning and Deep Learning Algorithms in Biomedical Text Classification
• 19. Energy Efficient Optimum Cluster Head Estimation for Body Area Networks
• 20. Segmentation and Classification of Tumour Regions from Brain Magnetic Resonance Images by Neural Network-Based Technique
• 21. A Hypothetical Study in Biomedical Based Artificial Intelligence Systems using Machine Language (ML) Rudiments
• 22. Neural Source Connectivity Estimation using particle filter and Granger causality methods
• 23. Exploration of Lymph Node-Negative Breast Cancers by Support Vector Machines, Naïve Bayes, and Decision Trees: A Comparative Study
• Index