Artificial Intelligence in Biomedical and Modern Healthcare Informatics provides a deeper understanding of the current trends in AI and machine learning within healthcare diagnosis, its practical approach in healthcare, and gives insight into different wearable sensors and its device module to help doctors and their patients in enhanced healthcare system.
The primary goal of this book is to detect difficulties and their solutions to medical practitioners for the early detection and prediction of any disease.
The 56 chapters in the volume provide beginners and experts in the medical science field with general pictures and detailed descriptions of imaging and signal processing principles and clinical applications.
With forefront applications and up-to-date analytical methods, this book captures the interests of colleagues in the medical imaging research field and is a valuable resource for healthcare professionals who wish to understand the principles and applications of signal and image processing and its related technologies in healthcare.
1. Impact of Artificial Intelligence on Public Health: A Prospective
Study on Medical Social Work Practice
2. Upshots of Healthcare with AI
3. Artificial Intelligence and Machine Learning Assisted Robotic Surgery:
Current Trends and Future Scope
4. A Deep Perspective of Blockchain Applications in Healthcare Sector and
Industry 4.0
5. Analyzing the role of Machine Learning Techniques in Healthcare Systems
6. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
in Biomedical Fields: A Prospect in Improvising Medical Healthcare Systems
7. Artificial Intelligence in respiratory diseases with special insight
through bioinformatics
8. Electroencephalography (EEG) and Epilepsy
9. A Review on Brain Computer Interface and its Applications
10. Recent Trends in Metabolomics and Artificial Intelligence
11. A comprehensive review on state of art imagined speech decoding
techniques using Electroencephalography
12. Parkinson's Disease Diagnosis, Treatment, and Future Scope: An Epilogue
13. Recent Advances in Removal of Artefacts from EEG Signal Records
14. Computer Aided Diagnosis in Health Care: Case Study on Lung Cancer
Diagnosis
15. AI and its role in predictive preclinical models for drug efficacy
testing
16. Machine Learning-based Solutions for Brain Tumor Detection: Comparative
Study and Limitations
17. Indoor and Home-Based Post-Stroke Rehabilitation Techniques- A Systemic
Review
18. A comprehensive study on implementable antennas for medical applications
19. Deep Learning for Bone Age Assessment: Current Status and Future
Prospects
20. Emerging Applications of Artificial Intelligence in Analyzing EEG Signals
for the Healthcare Sector
21. Epilepsy Detection System using CWT and Deep-CNN
22. Isolated Indian Sign Language Recognition with Multihead Attention
Transformer based network and Mediapipes landmarks
23. Diagnosis of Parkinsons Disease based on Biological and Imaging-derived
features using Machine learning and Deep learning
24. Brain Tumor and Feature Detection from MRI and CT scan using Artificial
Intelligence
25. Neuromodulation via Brain Stimulation: A Promising Therapeutic
Perspective for Alzheimers Disease
26. A Biosensor for the Detection of Viruses using One-Dimensional Photonic
Crystals
27. Artificial Intelligence Based Seizure Detection Systems in
Electroencephalography: Transforming Healthcare for Accurate Diagnosis and
Treatment
28. Artificial Intelligence and Image Enhancement based methodologies used
for detection of tumor in MRIs of human brain
29. Machine learning based workload Identification using Functional
Near-Infrared Spectroscopy (fNIRS) Data
30. Forecasting the COVID-19 pandemic through the hybridization of Machine
Intelligent Algorithms
31. Suppression of Noise Signals from Computed Tomography and Ultrasound
Medical Images and Performance Evaluation
32. Prediction Of Non-Alcoholic Fat Liver Disease Using Machine Learning
33. Evaluation of Diabetes Classification with Machine Learning Framework
34. Various Segmentation Methods/ Techniques for Medical Images and The Role
of IoT
35. Augmented Mass Detection of Breast Cancer in Mammogram Images Using Deep
Intelligent Neural Network Model
36. CNC Machines in Production of Medical Devices
37. Analysis and prediction of Cardiomyopathy using Artificial Intelligence
38. A Preemptive Approach to Polycystic Ovary Syndrome Diagnosis using
Machine Learning
39. Mapping the Landscape of Human Activity Recognition Techniques in Health
Monitoring for Chronic Disease Management
40. Analysis and Organization of Mycological Skin Contaminations by Means of
Medicinal Imagery
41. A Sensitive Biosensor for the Detection of Blood Components Using 2D
Photonic Crystals
42. Machine Learning Assisted EEG Signal Classification for Automated
Diagnosis of Mental Stress
43. CNN based Deep Learning model for Skin Cancer detection using
Dermatoscopic Images
44. Bioelectrical Impedance Analysis Body Composition Estimation of Fat Mass
Percentage in People with Spinal Cord Injury
45. Advanced EEG Signal Processing and Feature Extraction Concepts
46. Fractal Analysis on Biomedical Signal
47. Detection of Metastasis Osteosarcoma Using Deep Fuzzy Gradient Recurrent
Convolutional Neural Network
48. Deep Learning Based Fatigue Detection Using Functional Connectivity
49. Brain Tumor Diagnosis Using Image Classifier
50. ISL Recognition System in Realtime using TensorFlow API
51. Exploring the Exciting Potential and Challenges of Brain-Computer
Interfaces (BCI)
52. Transmission Dynamics of COVID-19 Virus Disease
53. Design of High Voltage Biphasic Pulse Generation Circuit with 3-Level
Isolation Suitable for AED Applications
54. A Novel Scheme of Brain Tumor Detection from MRIs using K-Means
Segmentation and Histogram Analysis
55. Analyzing Post COVID-19 Effects on Self-Consciousness and Awareness
towards Health: A Neuroscience Framework
56. Crowdsourcing and Artificial Intelligence based Modeling Framework for
effective Public Healthcare Informatics and Smart eHealth System
Dr. M.A. Ansari holds PhD degree on Signal and Imaging Processing from Indian Institute of Technology Roorkee. He has 18 years of experience in teaching and research. Currently he is Professor at School of Engineering, Gautam Buddha University, where he supervised 4 PhD and 63 MTech students to date. He authored several book chapters and published almost 30 peer-reviewed articles in international journals. Dr. Ansari main research interests are medical image coding, biomedical instrumentation and control, and digital signal and image processing. R. S. Anand received the B.E., M.E., and Ph.D. degrees from the University of Roorkee, Roorkee, India, in 1985, 1987, and 1992, respectively.,He is currently a Professor with the Electrical Engineering Department, IIT Roorkee, Roorkee. He has authored or coauthored more than 200 research papers in journals and conferences. His current research interests include medical signal and image processing, ultrasonic nondestructive evaluation (NDE), medical diagnosis, and speech signal processing.,Dr. Anand is a Life Member of the Ultrasonic Society of India.
Pragati Tripathi received an M.Tech degree in power electronics from Gautam Buddha University, Greater Noida, India, in 2018. She is working as a Research Scholar with the School of Engineering, Gautam Buddha University. She has also been associated with IIT Delhi and served as a Research Associate with Sharda University, Greater Noida. Her research interests include signal processing, brain mapping, and neuroscience. Rajat Mehrotra is an Assistant Professor in the Electrical & Electronics Engineering Department at GL Bajaj Institute of Technology & Management, Greater Noida, India. He received his BTech in Electrical and Electronics Engineering from the Dr. A.P.J. Abdul Kalam Technical University, Lucknow (Formerly UPTU), in 2008 and his MTech in Telecommunication Engineering from the same university, in 2014 and his PhD. in the field of Medical Image Processing. His research interests include digital image processing, biomedical imaging, and deep learning. Currently, he is involved in research with the School of Engineering at Gautam Buddha University, Greater Noida. He has published his research in various journals of international repute. He has more than 14 years of experience in teaching and research. He has also published multiple patents in his area of research. Md Belal Bin Heyat received the B.tech degree in E.I. from Integral University, Lucknow, UP, India in 2014. He is successfully completed Master of Technology degree in Electronics Circuit & System, department of electronics and communication engineering from Integral University, Lucknow, Uttar Pradesh, India in 2016. He has author and co-author in number of International journals, National journals, Symposium and Conferences. He is an editor and reviewer for three international and national journals. His research interests include electronics, communication engineering, instrumentation, therapy, medical and biomedical engineering.
