Artificial Intelligence for Neurological Disorders 1st Edition Abraham

Artificial Intelligence for Neurological Disorders 1st Edition Abraham- Ebook Instant Download/Delivery ISBN(s): 9780323902779,0323902774,9780323902786, 0323902782

Product details:

• ISBN 10: 0323902782
• ISBN 13: 9780323902786
• Author:Ajith Abraham; Sujata Dash; Subhendu Kumar Pani; Laura García-Hernández
  

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.

Table contents:

Chapter 1: Early detection of neurological diseases using machine learning and deep learning techniques: A review

Chapter 2: A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave dataset

Chapter 3: Machine learning and deep learning models for early-stage detection of Alzheimer’s disease and its proliferation in human brain

Chapter 4: Convolutional neural network model for identifying neurological visual disorder

Chapter 5: Recurrent neural network model for identifying neurological auditory disorder

Chapter 6: Recurrent neural network model for identifying epilepsy based neurological auditory disorder

Chapter 7: Dementia diagnosis with EEG using machine learning

Chapter 8: Computational methods for translational brain-behavior analysis

Chapter 9: Clinical applications of deep learning in neurology and its enhancements with future directions

Chapter 10: Ensemble sparse intelligent mining techniques for cognitive disease

Chapter 11: Cognitive therapy for brain diseases using deep learning models

Chapter 12: Cognitive therapy for brain diseases using artificial intelligence models

Chapter 13: Clinical applications of deep learning in neurology and its enhancements with future predictions

Chapter 14: An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning

Chapter 15: Neural signaling and communication using machine learning

Chapter 16: Classification of neurodegenerative disorders using machine learning techniques

Chapter 17: New trends in deep learning for neuroimaging analysis and disease prediction

Chapter 18: Prevention and diagnosis of neurodegenerative diseases using machine learning models

Chapter 19: Artificial intelligence-based early detection of neurological disease using noninvasive method based on speech analysis

Chapter 20: An insight into applications of deep learning in neuroimaging

Chapter 21: Incremental variance learning-based ensemble classification model for neurological disorders

Chapter 22: A systematic review of adaptive machine learning techniques for early detection of Parkinson’s disease

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