Deepika Koundal is a distinguished academic at the University of Eastern Finland, specializing in Artificial Intelligence, Machine Learning, and healthcare analytics. Her research focuses on intelligent data-driven solutions for medical and biomedical applications, contributing significantly to interdisciplinary innovation and global research collaborations. She has published extensively in reputed international journals and actively leads research initiatives in AI-driven healthcare technologies, fostering academic excellence and industry partnerships worldwide.Mudassir Khan is an accomplished academician and researcher specializing in Big Data Analytics, Deep Learning, AI, and healthcare applications. Holding a Ph.D. in Big Data Analytics using Deep Learning, he is currently pursuing a postdoctoral fellowship at Multimedia University, Malaysia, while serving as Assistant Professor at King Khalid University, Saudi Arabia. With over 14 years of experience, he has authored 100+ SCI/SCOPUS-indexed papers, four books, and edited numerous volumes with leading publishers.Rupali A. Mahajan is an accomplished academician and researcher specializing in Artificial Intelligence, Machine Learning, Deep Learning, and Data Science applications. Holding a Ph.D. in Computer Science and Engineering, she completed her postdoctoral research at the Singapore Institute of Technology. She currently serves as Associate Professor in the Department of Data Science at Vishwakarma Institute of Information Technology, Pune. With over 17 years of professional experience, she has authored 7 books, published 50+ research papers, and held 18 patents across diverse domains.Rajesh Dey is a distinguished researcher specializing in Artificial Intelligence, Machine Learning, and FinTech. Currently a Postdoctoral Fellow at IIUM, Malaysia, he holds a Ph.D. in Adaptive Signal Correction. With over 17 years of experience, he has published extensively and contributed to embedded systems, IoT, robotics, and sustainable EV technologies. He has served in key academic and industry roles, including Associate Professor and Technical Director. A certified Design Thinking Coach, he actively collaborates with international universities and research institutions, mentoring students and driving innovation in emerging technologies.

• 1. Decoding Psychiatric Disorders: Multimodal Brain Imaging Powered by Machine Learning and Deep Learning. 2. Generative AI‑Driven Framework for Managing Neurodegenerative Disorders: Focus on Alzheimer’s Detection. 3. Convolutional Pathways to Neurodegeneration: An Image‑Based Predictive Model for Alzheimer’s. 4. Generative AI for Early Diagnosis of Neurodegenerative Diseases: A Study on Multimodal Fusion Framework. 5. Introduction to Neurodegenerative Diseases. 6. Personalized Treatment Planning Through AI Insights. 7. AI‑Powered Early Detection and Monitoring of Neurodegenerative Diseases. 8. Temporal Diffusion–Guided Assessment for Optimized Clinical Categorization of Alzheimer’s Disease Using Multimodal Neural Features. 9. Transforming Healthcare with Generative AI: Opportunities, Risks, and Ethical Considerations. 10. AI‑Driven Early Detection of Multiple Sclerosis Through Explainable Deep Learning. 11. Principles of Precision Healthcare Toward Pancreatic Cancer. 12. Synthetic Data Generation for Emphysema Disease Research. 13. AI‑Driven Early Detection of Neurodegenerative Disorders. 14. Intelligent Multimodal Imaging Framework for Robust and Real‑Time Brain Tumor Detection. 15. AI‑Powered Multimodal Approaches for Early Detection and Personalized Management of Neurodegenerative Diseases. 16. Multimodal Classification of Meningitis Etiologies From CSF Profiles Using a CNN‑BiGRU Hybrid Model Optimized with the Sparrow Search Algorithm. 17. Deep Learning Approaches for Autism Spectrum Disorder Detection: Performance Analysis and a Hybrid ViT–GNN Model. 18. Multimodal Fusion Attention Graph Neural Networks for Alzheimer’s Dementia Detection. 19. Advancements in Magnetic Resonance Imaging Aimed at Alzheimer’s Assessment, Lung Cancer Identification, Characterization, and Staging: An In‑Depth Analysis. 20. Integrating NLP and EHR for Next‑Generation Mental Health Analytics. 21. A Multistage Deep Learning Approach for Intracranial Aneurysm Detection.

Deepika Koundal is a distinguished academic at the University of Eastern Finland, specializing in Artificial Intelligence, Machine Learning, and healthcare analytics. Her research focuses on intelligent data-driven solutions for medical and biomedical applications, contributing significantly to interdisciplinary innovation and global research collaborations. She has published extensively in reputed international journals and actively leads research initiatives in AI-driven healthcare technologies, fostering academic excellence and industry partnerships worldwide.Mudassir Khan is an accomplished academician and researcher specializing in Big Data Analytics, Deep Learning, AI, and healthcare applications. Holding a Ph.D. in Big Data Analytics using Deep Learning, he is currently pursuing a postdoctoral fellowship at Multimedia University, Malaysia, while serving as Assistant Professor at King Khalid University, Saudi Arabia. With over 14 years of experience, he has authored 100+ SCI/SCOPUS-indexed papers, four books, and edited numerous volumes with leading publishers.Rupali A. Mahajan is an accomplished academician and researcher specializing in Artificial Intelligence, Machine Learning, Deep Learning, and Data Science applications. Holding a Ph.D. in Computer Science and Engineering, she completed her postdoctoral research at the Singapore Institute of Technology. She currently serves as Associate Professor in the Department of Data Science at Vishwakarma Institute of Information Technology, Pune. With over 17 years of professional experience, she has authored 7 books, published 50+ research papers, and held 18 patents across diverse domains.Rajesh Dey is a distinguished researcher specializing in Artificial Intelligence, Machine Learning, and FinTech. Currently a Postdoctoral Fellow at IIUM, Malaysia, he holds a Ph.D. in Adaptive Signal Correction. With over 17 years of experience, he has published extensively and contributed to embedded systems, IoT, robotics, and sustainable EV technologies. He has served in key academic and industry roles, including Associate Professor and Technical Director. A certified Design Thinking Coach, he actively collaborates with international universities and research institutions, mentoring students and driving innovation in emerging technologies.

• 1. Decoding Psychiatric Disorders: Multimodal Brain Imaging Powered by Machine Learning and Deep Learning. 2. Generative AI‑Driven Framework for Managing Neurodegenerative Disorders: Focus on Alzheimer’s Detection. 3. Convolutional Pathways to Neurodegeneration: An Image‑Based Predictive Model for Alzheimer’s. 4. Generative AI for Early Diagnosis of Neurodegenerative Diseases: A Study on Multimodal Fusion Framework. 5. Introduction to Neurodegenerative Diseases. 6. Personalized Treatment Planning Through AI Insights. 7. AI‑Powered Early Detection and Monitoring of Neurodegenerative Diseases. 8. Temporal Diffusion–Guided Assessment for Optimized Clinical Categorization of Alzheimer’s Disease Using Multimodal Neural Features. 9. Transforming Healthcare with Generative AI: Opportunities, Risks, and Ethical Considerations. 10. AI‑Driven Early Detection of Multiple Sclerosis Through Explainable Deep Learning. 11. Principles of Precision Healthcare Toward Pancreatic Cancer. 12. Synthetic Data Generation for Emphysema Disease Research. 13. AI‑Driven Early Detection of Neurodegenerative Disorders. 14. Intelligent Multimodal Imaging Framework for Robust and Real‑Time Brain Tumor Detection. 15. AI‑Powered Multimodal Approaches for Early Detection and Personalized Management of Neurodegenerative Diseases. 16. Multimodal Classification of Meningitis Etiologies From CSF Profiles Using a CNN‑BiGRU Hybrid Model Optimized with the Sparrow Search Algorithm. 17. Deep Learning Approaches for Autism Spectrum Disorder Detection: Performance Analysis and a Hybrid ViT–GNN Model. 18. Multimodal Fusion Attention Graph Neural Networks for Alzheimer’s Dementia Detection. 19. Advancements in Magnetic Resonance Imaging Aimed at Alzheimer’s Assessment, Lung Cancer Identification, Characterization, and Staging: An In‑Depth Analysis. 20. Integrating NLP and EHR for Next‑Generation Mental Health Analytics. 21. A Multistage Deep Learning Approach for Intracranial Aneurysm Detection.

Deepika Koundal is a distinguished academic at the University of Eastern Finland, specializing in Artificial Intelligence, Machine Learning, and healthcare analytics. Her research focuses on intelligent data-driven solutions for medical and biomedical applications, contributing significantly to interdisciplinary innovation and global research collaborations. She has published extensively in reputed international journals and actively leads research initiatives in AI-driven healthcare technologies, fostering academic excellence and industry partnerships worldwide.Mudassir Khan is an accomplished academician and researcher specializing in Big Data Analytics, Deep Learning, AI, and healthcare applications. Holding a Ph.D. in Big Data Analytics using Deep Learning, he is currently pursuing a postdoctoral fellowship at Multimedia University, Malaysia, while serving as Assistant Professor at King Khalid University, Saudi Arabia. With over 14 years of experience, he has authored 100+ SCI/SCOPUS-indexed papers, four books, and edited numerous volumes with leading publishers.Rupali A. Mahajan is an accomplished academician and researcher specializing in Artificial Intelligence, Machine Learning, Deep Learning, and Data Science applications. Holding a Ph.D. in Computer Science and Engineering, she completed her postdoctoral research at the Singapore Institute of Technology. She currently serves as Associate Professor in the Department of Data Science at Vishwakarma Institute of Information Technology, Pune. With over 17 years of professional experience, she has authored 7 books, published 50+ research papers, and held 18 patents across diverse domains.Rajesh Dey is a distinguished researcher specializing in Artificial Intelligence, Machine Learning, and FinTech. Currently a Postdoctoral Fellow at IIUM, Malaysia, he holds a Ph.D. in Adaptive Signal Correction. With over 17 years of experience, he has published extensively and contributed to embedded systems, IoT, robotics, and sustainable EV technologies. He has served in key academic and industry roles, including Associate Professor and Technical Director. A certified Design Thinking Coach, he actively collaborates with international universities and research institutions, mentoring students and driving innovation in emerging technologies.

• 1. Decoding Psychiatric Disorders: Multimodal Brain Imaging Powered by Machine Learning and Deep Learning. 2. Generative AI‑Driven Framework for Managing Neurodegenerative Disorders: Focus on Alzheimer’s Detection. 3. Convolutional Pathways to Neurodegeneration: An Image‑Based Predictive Model for Alzheimer’s. 4. Generative AI for Early Diagnosis of Neurodegenerative Diseases: A Study on Multimodal Fusion Framework. 5. Introduction to Neurodegenerative Diseases. 6. Personalized Treatment Planning Through AI Insights. 7. AI‑Powered Early Detection and Monitoring of Neurodegenerative Diseases. 8. Temporal Diffusion–Guided Assessment for Optimized Clinical Categorization of Alzheimer’s Disease Using Multimodal Neural Features. 9. Transforming Healthcare with Generative AI: Opportunities, Risks, and Ethical Considerations. 10. AI‑Driven Early Detection of Multiple Sclerosis Through Explainable Deep Learning. 11. Principles of Precision Healthcare Toward Pancreatic Cancer. 12. Synthetic Data Generation for Emphysema Disease Research. 13. AI‑Driven Early Detection of Neurodegenerative Disorders. 14. Intelligent Multimodal Imaging Framework for Robust and Real‑Time Brain Tumor Detection. 15. AI‑Powered Multimodal Approaches for Early Detection and Personalized Management of Neurodegenerative Diseases. 16. Multimodal Classification of Meningitis Etiologies From CSF Profiles Using a CNN‑BiGRU Hybrid Model Optimized with the Sparrow Search Algorithm. 17. Deep Learning Approaches for Autism Spectrum Disorder Detection: Performance Analysis and a Hybrid ViT–GNN Model. 18. Multimodal Fusion Attention Graph Neural Networks for Alzheimer’s Dementia Detection. 19. Advancements in Magnetic Resonance Imaging Aimed at Alzheimer’s Assessment, Lung Cancer Identification, Characterization, and Staging: An In‑Depth Analysis. 20. Integrating NLP and EHR for Next‑Generation Mental Health Analytics. 21. A Multistage Deep Learning Approach for Intracranial Aneurysm Detection.