Artificial Intelligence for Neurological Disorders

by Ajith Abraham PhD (Editor), Sujata Dash PhD (Editor), Subhendu Kumar Pani PhD (Editor), Laura García-Hernández (Editor)

Overview

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation. The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances. - Discusses various AI and ML methods to apply for neurological research - Explores Deep Learning techniques for brain MRI images - Covers AI techniques for the early detection of neurological diseases and seizure prediction - Examines cognitive therapies using AI and Deep Learning methods

Product Details

ISBN-13:	9780323902786
Publisher:	Elsevier Science & Technology Books
Publication date:	09/23/2022
Sold by:	Barnes & Noble
Format:	eBook
Pages:	432
File size:	19 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia.
Sujata Dash holds the position of Professor at the Information Technology School of Engineering and Technology, Nagaland University, Dimapur Campus, Nagaland, India, bringing more than three decades of dedicated service in teaching and mentoring students. She has been honoured with the prestigious Titular Fellowship from the Association of Commonwealth Universities, United Kingdom. As a testament to her global contributions, she served as a visiting professor in the Computer Science Department at the University of Manitoba, Canada. With a prolific academic record, she has authored over 200 technical papers published in esteemed international journals, and conference proceedings, and edited book chapters by reputed publishers Serving as a reviewer and Associate Editor for approximately 15 international journals.
Dr. Subhendu Kumar Pani received his Ph.D. from Utkal University, Odisha, India in the year 2013. He is working as a professor at Krupajal Engineering College under BPUT, Odisha, India. He has more than 20 years of teaching and research experience His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is the recipient of 5 researcher awards. In addition to research, he has guided two PhD students and 31 M. Tech students. He has published 150 International Journal papers (100 Scopus index). His professional activities include roles as Book Series Editor (CRC Press, Apple Academic Press, Wiley-Scrivener), Associate Editor, Editorial board member and/or reviewer of various International Journals. He is an Associate with no. of the conference societies. He has more than 250 international publications, 5 authored books, 25 edited and upcoming books; 40 book chapters into his account. He is a fellow in SSARSC and a life member in IE, ISTE, ISCA, and OBA.OMS, SMIACSIT, SMUACEE, CSI.
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.

Table of Contents

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

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From the Publisher

A comprehensive reference of AI and machine learning-based methods and techniques for neurological research