Artificial Intelligence for Neurological Disorders

Ajith Abraham, PhD (2001) in artificial intelligence from Monash University, Australia, is the Vice Chancellor of Sai University, India. Prior to joining Sai University, he held senior positions at several prominent institutions and was the founding director of Machine Intelligence Research Labs, a nonprofit scientific network for innovation and research excellence headquartered in Seattle, United States. Dr. Abraham has led or co-led research projects valued at over $110 million, funded by organizations in the United States, the European Union, Italy, the Czech Republic, France, Malaysia, China, and Australia. He has worked in multidisciplinary environments for more than 35 years and is a prolific co-author of research publications in artificial intelligence and its industrial applications. Several of his works have been translated into Chinese and Russian, and one of his books has been translated into Japanese. Dr. Abraham is consistently featured in the Stanford/Elsevier list of the world’s top 2% most-cited scientists. From 2016 to 2021, he served as Editor-in-Chief of Engineering Applications of Artificial Intelligence (EAAI), one of the longest-standing journals in the field (founded in 1988). He has also served on the editorial boards of more than 15 international journals indexed by Thomson ISI. Sujata Dash is a Professor of Information Technology at Nagaland University, India, and an IEEE Senior Member, with over three decades of academic and research experience. She holds a PhD in Computational Modeling and has also completed postdoctoral research at the University of Manitoba, Canada, where she later served as a Visiting Professor. Her research spans machine learning, deep learning, artificial intelligence, bioinformatics, natural language processing, and IoT, with applications across healthcare, data science, and smart systems. Dr. Dash has an extensive publication record with leading publishers including Elsevier, Springer, Wiley, and CRC Press, and serves on the editorial boards of several international journals. She has delivered keynote lectures and chaired sessions at numerous international conferences and has received several awards, including the Global Distinguished Award (IEEE IAS, 2023), Outstanding Scientist Award, and Best Researcher Award. Subhendu Kumar Pani received his PhD from Utkal University, Odisha, India, in 2013. He currently serves as Principal of Krupajal Engineering College, Bhubaneswar, India, and has over 17 years of teaching and research experience. A prolific author, he serves as Series Editor for CRC Press’ Advances in Computational Collective Intelligence, Apple Academic Press’ AAP Advances in Artificial Intelligence & Robotics, and Wiley-Scrivener’s Intelligent Data Analytics for Terror Threat Prediction, and is actively involved as an associate editor, editorial board member, and reviewer for several international journals. He also contributes to national and international conference communities. His research interests include data mining, big data analytics, web data analytics, fuzzy decision-making, and computational intelligence. He is a Fellow of SSARS (Canada) and a Life Member of several professional bodies, including IE, ISTE, ISCA, OBA, OMS, SMIACSIT, SMUACEE, and CSI. He has received multiple research awards in recognition of his contributions. LAURA GARCÍA-HERNÁNDEZ received the M.Sc. degree in computer science from the Universitat Oberta de Catalunya, Spain, in 2007, and the European Ph.D. degree in Engineering from the University of Córdoba, Spain, and also from the Institut Français de Mécanique Avancée, Clermont-Ferrand, France, in 2011. She has been an Invited Professor during a semester in the Institut Français de Mécanique Avancée, Clermont-Ferrand. She is currently an Associate Professor in the Area of Project Engineering at the University of Córdoba, Spain. Her primary areas of research are engineering design optimization, intelligent systems, machine learning, user adaptive systems, interactive evolutionary computation, project management, risk prevention in automatic systems, and educational technology. In these fields, she has authored or co-authored more than 70 international research publications. She has given several invited talks in different countries. She has realized several postdoctoral internships in different countries with a total duration of more than two years. She received the prestigious National Government Research Grant ‘‘José Castillejo’’ for supporting their post-doc research during six months in the University of Algarve, Portugal. She has been an Investigator Principal in two Spanish research projects and has also been an Investigator Collaborator in some research contracts and projects. She is an Expert Member of ISO/TC 184/SC working team and the National Standards Institute of Spain (UNE). Moreover, she is a member of the Spanish Association of Engineering Projects (IPMA Spain). Considering her research, she received the Young Researcher Award granted by the Spanish Association of Engineering Projects (IPMA), Spain, in 2015. Additionally, she received two times the General Council of Official Colleges Award at prestigious International Conference on Project Management and Engineering both 2017 and 2018 editions. She is the Co-Editor-in-Chief of the Journal of Information Assurance and Security. Also, she is an Associate Editor in the following ISI Journals: Applied Soft Computing, Complex & Intelligent Systems, and Journal of Intelligent Manufacturing.

• 1. Early detection of neurological diseases using machine learning and deep learning techniques: A review2. A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave data3. Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proliferation in human brain4. Recurrent neural network model for identifying epilepsy based neurological auditory disorder5. Recurrent neural network model for identifying neurological auditory disorder6. Dementia diagnosis with EEG using machine learning7. Computational methods for translational brain-behavior analysis8. Clinical applications of deep learning in neurology and its enhancements with future directions9. Ensemble sparse intelligent mining techniques for cognitive disease10. Cognitive therapy for brain diseases using deep learning models11. Cognitive therapy for brain diseases using artificial intelligence models12. Clinical applications of deep learning in neurology and its enhancements with future predictions13. An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning14. Neural signaling and communication using machine learning15. Classification of neurodegenerative disorders using machine learning techniques16. New trends in deep learning for neuroimaging analysis and disease prediction17. Prevention and diagnosis of neurodegenerative diseases using machine learning models18. Artificial intelligence-based early detection of neurological disease using noninvasive method based on speech analysis19. An insight into applications of deep learning in neuroimaging20. Incremental variance learning-based ensemble classification model for neurological disorders21. Early detection of Parkinsons disease using adaptive machine learning techniques: A review22. Convolutional neural network model for identifying neurological visual disorder