Thompson Stephan earned his Ph.D. in Computer Science and Engineering from Pondicherry University, India, in 2018. Currently serving as an Associate Professor in the Department of Computer Science & Engineering at Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, he achieved recognition among the world's top 2% most influential scientists for 2023, a distinction jointly conferred by Elsevier and Stanford University, USA. Acknowledged for academic excellence during his master's degree, he secured a university rank. Additionally, he was honored with the Best Researcher Award-2020 and the Protsahan Research Award in 2023 by the IEEE Bangalore Section, India. His research interests primarily focus on implementing and applying artificial intelligence techniques in practical settings. He has authored numerous technical research papers published in renowned journals and conferences by IEEE, Elsevier, Springer, and others. Actively serving as a reviewer for esteemed international journals and working as a book editor, Thompson Stephan is dedicated to advancing the field.

• PART 1. Foundations of AI in healthcare, 1. Exploring deep learning approaches for cardiac arrhythmia diagnosis, 2. Neural networks and LDA-based machine learning framework for the early detection of breast cancer, 3. Advanced deep learning algorithms for early ocular disease detection using fundus images, PART 2. Disease detection and diagnosis, 4. A vision transformer-based approach for brain tumor detection, 5. Early detection of skin cancer through human-computer collaboration, 6. Improved mass detection in mammogram images with Dual Tree Complex Wavelet Transform and Fourier Descriptors, 7. A deep learning-based model for early detection of COVID-19 using chest X-ray images, 8. Detection of seizure activity in fMRI images using deep learning techniques, PART 3. Disease prediction and public health, 9. Improving prediction accuracy for neo-adjuvant chemotherapy response in breast cancer through 3D image segmentation and deep learning techniques, 10. A machine learning predictive framework for diabetes management using blood parameters, 11. A combined neuro-fuzzy and Naive Bayes approach for swine flu disease prediction, 12. Enhancing decision-making in maternal public healthcare using a knowledge discovery-based predictive analytics framework, PART 4. Patient care and enhancements, 13. Enhancing patient care and treatment through explainable AI: A gap analysis, 14. Improved medical image captioning for chest X-rays using a hybrid VGG-ELECTRA model, 15. Diagnosing Parkinson’s disease using a deep learning model based on electromyography sensors, 16. Enhancing heart disease prediction with Hybridized KNN-MOPSO algorithm

Thompson Stephan earned his Ph.D. in Computer Science and Engineering from Pondicherry University, India, in 2018. Currently serving as an Associate Professor in the Department of Computer Science & Engineering at Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, he achieved recognition among the world's top 2% most influential scientists for 2023, a distinction jointly conferred by Elsevier and Stanford University, USA. Acknowledged for academic excellence during his master's degree, he secured a university rank. Additionally, he was honored with the Best Researcher Award-2020 and the Protsahan Research Award in 2023 by the IEEE Bangalore Section, India. His research interests primarily focus on implementing and applying artificial intelligence techniques in practical settings. He has authored numerous technical research papers published in renowned journals and conferences by IEEE, Elsevier, Springer, and others. Actively serving as a reviewer for esteemed international journals and working as a book editor, Thompson Stephan is dedicated to advancing the field.

• PART 1. Foundations of AI in healthcare, 1. Exploring deep learning approaches for cardiac arrhythmia diagnosis, 2. Neural networks and LDA-based machine learning framework for the early detection of breast cancer, 3. Advanced deep learning algorithms for early ocular disease detection using fundus images, PART 2. Disease detection and diagnosis, 4. A vision transformer-based approach for brain tumor detection, 5. Early detection of skin cancer through human-computer collaboration, 6. Improved mass detection in mammogram images with Dual Tree Complex Wavelet Transform and Fourier Descriptors, 7. A deep learning-based model for early detection of COVID-19 using chest X-ray images, 8. Detection of seizure activity in fMRI images using deep learning techniques, PART 3. Disease prediction and public health, 9. Improving prediction accuracy for neo-adjuvant chemotherapy response in breast cancer through 3D image segmentation and deep learning techniques, 10. A machine learning predictive framework for diabetes management using blood parameters, 11. A combined neuro-fuzzy and Naive Bayes approach for swine flu disease prediction, 12. Enhancing decision-making in maternal public healthcare using a knowledge discovery-based predictive analytics framework, PART 4. Patient care and enhancements, 13. Enhancing patient care and treatment through explainable AI: A gap analysis, 14. Improved medical image captioning for chest X-rays using a hybrid VGG-ELECTRA model, 15. Diagnosing Parkinson’s disease using a deep learning model based on electromyography sensors, 16. Enhancing heart disease prediction with Hybridized KNN-MOPSO algorithm

Thompson Stephan earned his Ph.D. in Computer Science and Engineering from Pondicherry University, India, in 2018. Currently serving as an Associate Professor in the Department of Computer Science & Engineering at Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, he achieved recognition among the world's top 2% most influential scientists for 2023, a distinction jointly conferred by Elsevier and Stanford University, USA. Acknowledged for academic excellence during his master's degree, he secured a university rank. Additionally, he was honored with the Best Researcher Award-2020 and the Protsahan Research Award in 2023 by the IEEE Bangalore Section, India. His research interests primarily focus on implementing and applying artificial intelligence techniques in practical settings. He has authored numerous technical research papers published in renowned journals and conferences by IEEE, Elsevier, Springer, and others. Actively serving as a reviewer for esteemed international journals and working as a book editor, Thompson Stephan is dedicated to advancing the field.

• PART 1. Foundations of AI in healthcare, 1. Exploring deep learning approaches for cardiac arrhythmia diagnosis, 2. Neural networks and LDA-based machine learning framework for the early detection of breast cancer, 3. Advanced deep learning algorithms for early ocular disease detection using fundus images, PART 2. Disease detection and diagnosis, 4. A vision transformer-based approach for brain tumor detection, 5. Early detection of skin cancer through human-computer collaboration, 6. Improved mass detection in mammogram images with Dual Tree Complex Wavelet Transform and Fourier Descriptors, 7. A deep learning-based model for early detection of COVID-19 using chest X-ray images, 8. Detection of seizure activity in fMRI images using deep learning techniques, PART 3. Disease prediction and public health, 9. Improving prediction accuracy for neo-adjuvant chemotherapy response in breast cancer through 3D image segmentation and deep learning techniques, 10. A machine learning predictive framework for diabetes management using blood parameters, 11. A combined neuro-fuzzy and Naive Bayes approach for swine flu disease prediction, 12. Enhancing decision-making in maternal public healthcare using a knowledge discovery-based predictive analytics framework, PART 4. Patient care and enhancements, 13. Enhancing patient care and treatment through explainable AI: A gap analysis, 14. Improved medical image captioning for chest X-rays using a hybrid VGG-ELECTRA model, 15. Diagnosing Parkinson’s disease using a deep learning model based on electromyography sensors, 16. Enhancing heart disease prediction with Hybridized KNN-MOPSO algorithm