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Artificial Intelligence for Neurological Disorders, authored by Ajith Abraham, is a comprehensive resource that delves into the latest approaches of AI, big data analytics, and machine learning in neurological research. This book provides a detailed exploration of various machine learning techniques used to detect neurological diseases at the cellular level, including image segmentation, classification, image indexing, neural networks, and image processing methods.
The book discusses several AI techniques for the early detection of neurological diseases, utilizing deep learning applications with brain imaging methods such as EEG, MEG, fMRI, fNIRS, and PET. These applications are crucial for predicting seizures and aiding in neuromuscular rehabilitation.
Deep learning techniques are extensively covered in the book, particularly in the context of brain MRI images. These techniques have the potential to revolutionize the field of neurological research by providing insights into the structure and function of the brain.
Artificial Intelligence for Neurological Disorders also explores cognitive therapies that leverage AI and deep learning methods. These innovative approaches aim to improve patient outcomes and accelerate the development of new treatments for neurological disorders.
The primary goal of this book is to provide researchers and clinicians with a broad understanding of AI, machine learning, and big data analytics in neurological research. By adopting these cutting-edge technologies, professionals in the field can accelerate research, improve diagnostic accuracy, and develop more effective treatments for neurological disorders.
With its comprehensive coverage of AI and machine learning in neurological research, Artificial Intelligence for Neurological Disorders is an essential resource for researchers, clinicians, and students seeking to advance their knowledge in this rapidly evolving field.
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