Artificial Intelligence for Neurological Disorders
Häftad, Engelska, 2022
Av Ajith Abraham, Sujata Dash, Subhendu Kumar Pani, Laura Garc�a-Hern�ndez, Laura García-Hernández
2 569 kr
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
Produktinformation
- Utgivningsdatum2022-09-22
- Mått152 x 229 x undefined mm
- Vikt690 g
- FormatHäftad
- SpråkEngelska
- Antal sidor432
- FörlagElsevier Science
- ISBN9780323902779
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Dr. Ajith Abraham is the Vice Chancellor at Sai University, Chennai. Before joining Sai University, he held the position of vice chancellor at prominent institutions and was also the founding director of Machine Intelligence Research Labs (MIR Labs), a non-profit scientific network for innovation and research excellence with headquarters in Seattle, USA. Dr. Abraham has completed research projects valued at over $110 million as an investigator or co-investigator from the United States, the European Union, Italy, the Czech Republic, France, Malaysia, China, and Australia. He has worked in a multidisciplinary setting for more than 35 years and has authored or co-authored more than 1,500+ research publications in artificial intelligence and related applications in the industry. A handful of his publications have been translated into Chinese and Russian, and one of his books has been translated into Japanese. The Scopus database has approximately 1,400 papers indexed, whereas the Thomson Web of Science has over 1,000 publications indexed.In addition to other esteemed universities, Dr. Abraham has worked with researchers from MIT (USA), the University of Cambridge (UK), Harvard University (USA), and Oxford University (UK). According to Google Scholar, Dr. Abraham possesses over 63,000 scholarly citations with an H-index of over 118. He has delivered over 250 conference plenary talks and tutorials in more than 20 countries. From 2008 to 2021, Dr. Abraham chaired the IEEE Systems, Man, and Cybernetics Society Technical Committee on Soft Computing, which had more than 200 members. From 2011 to 2013, he represented Europe as a Distinguished Lecturer for the IEEE Computer Society (USA). Dr. Abraham is continuously listed in the Stanford/Elsevier list, highlighting the top 2% of the most cited scientists across the globe. Based on 2024 data, ScholarGPS listed Dr. Abraham as one of the world’s top 0.01% cited scientists in the engineering and computer science fields.From 2016 to 2021, Dr. Abraham worked as the chief editor of Engineering Applications of Artificial Intelligence (EAAI) at Elsevier, New York. EAAI is one of the oldest journals (founded in 1988) in the artificial intelligencedomain. Additionally, he sat on the editorial boards of more than 15 international journals indexed by Thomson ISI. Dr. Abraham received his Ph.D. degree in artificial intelligence from Monash University, Melbourne, Australia (2001), a Master of Science degree from Nanyang Technological University, Singapore (1998), and a B.Tech (Hons) degree from the University of Calicut in 1990.Sujata Dash is a Senior Member, IEEE who received the Ph.D. degree in computational modeling from Berhampur University, Orissa, India, in 1995. She is currently an Associate Professor with the P.G. Department of Computer Science and Application, North Orissa University, Baripada, India. She has published more than 150 technical articles in international journals, conferences, and book chapters of reputed publications. She has guided many scholars for their Ph.D. degrees in computer science. She is associated with many professional bodies like IEEE, CSI, ISTE, OITS, OMS, IACSIT, IMS, and IAENG. She is a member of the editorial board of several international journals and also reviewer of many international journals. Her current research interests include machine learning, distributed data mining, bioinformatics, intelligent agent, Web data mining, recommender systems, and image processing. 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.
- 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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