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,…mehr
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.
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.
Inhaltsangabe
1. Early detection of neurological diseases using machine learning and deep learning techniques: A review 2. 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
1. Early detection of neurological diseases using machine learning and deep learning techniques: A review 2. 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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