Artificial Intelligence for Neurological Disorders
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...
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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.
Bibliographische Angaben
- Herausgegeben:Abraham, Ajith; Dash, Sujata; Pani, Subhendu Kumar; García-Hernández, Laura
- Verlag: Academic Press
- EAN: 9780323902779
Autoren-Porträt
Sujata Dash is Professor of Computer Science at North Orissa University in the Department of Computer Science, Baripada, India. She is a recipient of Titular Fellowship from Association of Commonwealth Universities, UK and was a visiting professor of Computer Science Department of University of Manitoba, Canada. She has published more than 160 technical papers as well as textbooks, monographs and edited books. She is a member of international professional associations and is a reviewer and editorial board member for multiple international journals. Her current research interest includes Machine Learning, Data Mining, Big Data Analytics, Bioinformatics, Fuzzy sets and systems, Rough sets, Soft Computing and Intelligent Agents.
Inhaltsverzeichnis zu „Artificial Intelligence for Neurological Disorders “
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 data
3. Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proliferation in human brain
4. Recurrent neural network model for identifying epilepsy based neurological auditory disorder
5. Recurrent neural network model for identifying neurological auditory disorder
6. Dementia diagnosis with EEG using machine learning
7. Computational methods for translational brain-behavior analysis
8. Clinical applications of deep learning in neurology and its enhancements with future directions
9. Ensemble sparse intelligent mining techniques for cognitive disease
10. Cognitive therapy for brain diseases using deep learning models
11. Cognitive therapy for brain diseases using artificial intelligence models
12. Clinical applications of deep learning in neurology and its enhancements with future predictions
13. An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning
14. Neural signaling and communication using machine learning
15. Classification of neurodegenerative disorders using machine learning techniques
16. New trends in deep learning for neuroimaging analysis and disease prediction
17. Prevention and diagnosis of neurodegenerative diseases using machine learning models
18. Artificial intelligence-based early detection of neurological disease using noninvasive method based on speech analysis
19. An insight into applications of deep learning in neuroimaging
20. Incremental variance learning-based ensemble classification model for neurological disorders
21. Early detection of Parkinsons disease using adaptive machine learning techniques: A review
22. Convolutional neural network model for identifying neurological visual disorder
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