Advancements in AI and Computer Vision are revolutionizing medical diagnostics by enabling real-time analysis of vast data. This book focuses on ML algorithms that analyze medical data and predict diseases based on key features.
Advancements in AI and Computer Vision are revolutionizing medical diagnostics by enabling real-time analysis of vast data. This book focuses on ML algorithms that analyze medical data and predict diseases based on key features.
Sriparna Saha (M.E. & Ph.D, JU) is currently an Assistant Professor (Stage-II) in the Department of Computer Science and Engineering of Maulana Abul Kalam Azad University of Technology, West Bengal, India. She has more than 12 years of experience in teaching and research. Her research area includes AI, CV, HCI etc. with over 90 publications in international journals and conferences. Her major research proposal is accepted for Start Up Grant under UGC Basic Scientific Research Grant. Lidia Ghosh (Gold-Medalist, M.Tech., JU) is an Assistant Professor in the Department of Computer Application at the RCC Institute of Information Technology, India. She was a Postdoctoral Fellow at Liverpool Hope University, UK, and has received multiple prestigious fellowships, including the Rashtriya Uchchatara Shiksha Abhiyan Doctoral Fellowship. She has published over 50 research papers and serves as a reviewer for top IEEE journals. Her research focuses on Cognitive Neuroscience, Deep Learning, Type-2 Fuzzy Sets, and Human Memory Formation.
Inhaltsangabe
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
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
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