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Full Description
The rapid technological advancements in the healthcare industry over recent decades have been transformative. These innovations have not only enhanced our understanding of the morphology and physiology of various organs but have also significantly improved the early diagnosis and treatment of numerous diseases across different medical specialties. This progress has been largely driven by advancements in artificial intelligence (AI) and computer vision (CV). AI and CV enable the real-time collection, processing, interpretation, and analysis of vast amounts of static and dynamic medical data, revolutionizing disease characterization and patient selection. Early detection is crucial in treating life-threatening illnesses such as COVID-19, pneumonia, and cancer. Computer-based medical imaging techniques, including CT scans and X-rays, play a vital role in diagnosing these conditions. Similarly, biological signals like electroencephalography (EEG) and electrocardiography (ECG) help anticipate brain anomalies and heart diseases. Machine learning further enhances the accuracy of disease prediction, assisting clinicians in making precise diagnoses. By facilitating faster disease recognition, these technologies also enable wider access to healthcare, including remote and underserved areas. This book aims to develop machine learning algorithms that analyze diverse medical data and predict diseases based on their characteristics, ultimately advancing healthcare diagnostics and treatment strategies.
Contents
The proposed book will contain chapters corresponding to the following themes but not limited to
1. Machine Intelligence Systems and Technologies
2. Deep Learning Applications
3. AI and Data Science
4. Next Generation Computing and Applications
5. Emerging Technologies
6. Artificial Neural Networks
7. Ambient Intelligence
8. Hybrid Intelligent Systems
9. Robotics and Cybernetics
10. Biomedical Data Analysis
11. Cognitive Computing
12. Computational Intelligence
13. Video Surveillance and Related Applications
14. Nature Inspired Computing Techniques
15. Image Processing
16. Pattern Recognition and Applications
17. Human Computer Interaction
18. Natural Language Processing
19. Recommendation Systems
20. Data Mining
21. Web Mining
22. ML and DL Applications for Healthcare
23. Internet of Things (IoT)
24. Computer Vision
25. Smart and Intelligent Sensors
26. Soft Computing
27. Spatial Data Analysis
28. Speech and Audio Processing Applications
29. Reinforcement Learning
30. Transfer Learning
