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
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Illustrationen
Approx. 100 illustrations (50 in full color)
Dateigröße
ISBN-13
978-0-323-90278-6 (9780323902786)
Schweitzer Klassifikation
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
