Artificial Intelligence for Neurological Disorders
- 1st Edition - September 22, 2022
- Latest edition
- Editors: Ajith Abraham, Sujata Dash, Subhendu Kumar Pani, Laura García-Hernández
- Language: English
Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurologi… Read more
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Description
Description
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.
Key features
Key features
- 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
Readership
Readership
Table of contents
Table of contents
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: September 23, 2022
- Language: English
About the editors
About the editors
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Ajith Abraham
Ajith Abraham, PhD (2001) in artificial intelligence from Monash University, Australia, is the Vice Chancellor of Sai University, India. Prior to joining Sai University, he held senior positions at several prominent institutions and was the founding director of Machine Intelligence Research Labs, a nonprofit scientific network for innovation and research excellence headquartered in Seattle, United States. Dr. Abraham has led or co-led research projects valued at over $110 million, funded by organizations in the United States, the European Union, Italy, the Czech Republic, France, Malaysia, China, and Australia. He has worked in multidisciplinary environments for more than 35 years and is a prolific co-author of research publications in artificial intelligence and its industrial applications. Several of his works have been translated into Chinese and Russian, and one of his books has been translated into Japanese. Dr. Abraham is consistently featured in the Stanford/Elsevier list of the world’s top 2% most-cited scientists. From 2016 to 2021, he served as Editor-in-Chief of Engineering Applications of Artificial Intelligence (EAAI), one of the longest-standing journals in the field (founded in 1988). He has also served on the editorial boards of more than 15 international journals indexed by Thomson ISI.
SD
Sujata Dash
SP
Subhendu Kumar Pani
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