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Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders | S.N. Kumar | 1st Edition, 2026 | Taylor & Francis Group (English Medium)
|
| 16036-16036 |
The field of neurology is constantly evolving, with new technologies and techniques being developed to aid in the diagnosis and treatment of neurological disorders. Computational intelligence algorithms have emerged as a powerful tool in this field, offering innovative solutions to complex diagnostic challenges. In "Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders", author S.N. Kumar explores the latest advancements in computational intelligence and their applications in diagnosing various neurological conditions. This book is a valuable resource for researchers, clinicians, and students looking to deepen their understanding of how these algorithms can be used to improve the accuracy and efficiency of diagnosis. The first edition of this book, published in 2026 by Taylor & Francis Group, covers a wide range of topics related to computational intelligence algorithms in neurology. From machine learning techniques to artificial neural networks, readers will gain insight into the cutting-edge methods being used to diagnose conditions such as Alzheimer's disease, Parkinson's disease, epilepsy, and more. Kumar provides detailed explanations of each algorithm discussed in the book, along with practical examples and case studies that demonstrate their effectiveness in real-world scenarios. Whether you are a seasoned professional or just starting out in the field of neurology, this book offers valuable insights that can help improve patient outcomes and advance research in the field. With its comprehensive coverage of computational intelligence algorithms for neurological disorders, this book is sure to become an essential reference for anyone working in the field. Stay ahead of the curve with the latest research and techniques by adding "Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders" to your library today.
Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders (2026) is an advanced interdisciplinary textbook written by S.N. Kumar and published by Taylor & Francis Group. This book explores the application of artificial intelligence, machine learning, and computational intelligence techniques in the diagnosis and analysis of neurological disorders.
Neurological diseases such as Alzheimer’s disease, Parkinson’s disease, epilepsy, stroke, and multiple sclerosis require highly accurate and early diagnostic approaches. This book demonstrates how computational models and intelligent algorithms can significantly enhance diagnostic accuracy, assist clinicians, and improve patient outcomes.
The content integrates concepts from computer science, artificial intelligence, neuroscience, and clinical medicine, providing a multidisciplinary perspective on modern neurological diagnostics.
Key topics covered include:
- Fundamentals of computational intelligence in healthcare
- Machine learning algorithms for neurological disease detection
- Deep learning applications in brain imaging analysis
- Pattern recognition in EEG, MRI, and clinical data
- Predictive modeling for disease progression
- Feature extraction and data classification techniques
- Hybrid AI models for diagnostic decision support systems
- Challenges in AI-based medical diagnostics
A major strength of this book is its algorithmic and application-based approach, which explains how intelligent systems are built and used in real clinical environments. It bridges the gap between theoretical AI models and practical neurological diagnosis.
This 1st edition includes recent advancements in deep neural networks, convolutional neural networks (CNNs), and hybrid computational models applied to neuroimaging and electrophysiological data. It also highlights the role of big data analytics in personalized neurological care.
The book is especially useful for:
- Neuroscientists and neurologists
- AI and machine learning researchers
- Biomedical engineers
- Medical imaging specialists
- Computer science students and data scientists working in healthcare
With 384 pages of structured and research-oriented content, this hardbound edition serves as a comprehensive reference for understanding how computational intelligence is transforming neurological disease diagnosis.
| SKU / BOOK Code: | TFR-COMPUTATIONAL-NEURO-KUMAR- |
| Publisher: | Taylor & Francis Group |
| Author: | Kumar S.N. |
| Binding Type: | Hardcover |
| No. of Pages: | 384 |
| ISBN-10: | NA |
| ISBN-13: | NA |
| Edition: | 1st |
| Language: | English Medium |
| Publish Year: | 2026-01 |
| Weight (g): | 400 |
| Product Condition: | New |
| Reading Age: | Above 18 Years |
| Country of Origin: | India |
| Genre: | Computers & Internet |
| Manufacturer: | Taylor & Francis Group |
| Importer: | Taylor & Francis Group |
| Packer: | Fullfilled by Supplier |
The field of neurology is constantly evolving, with new technologies and techniques being developed to aid in the diagnosis and treatment of neurological disorders. Computational intelligence algorithms have emerged as a powerful tool in this field, offering innovative solutions to complex diagnostic challenges. In "Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders", author S.N. Kumar explores the latest advancements in computational intelligence and their applications in diagnosing various neurological conditions. This book is a valuable resource for researchers, clinicians, and students looking to deepen their understanding of how these algorithms can be used to improve the accuracy and efficiency of diagnosis. The first edition of this book, published in 2026 by Taylor & Francis Group, covers a wide range of topics related to computational intelligence algorithms in neurology. From machine learning techniques to artificial neural networks, readers will gain insight into the cutting-edge methods being used to diagnose conditions such as Alzheimer's disease, Parkinson's disease, epilepsy, and more. Kumar provides detailed explanations of each algorithm discussed in the book, along with practical examples and case studies that demonstrate their effectiveness in real-world scenarios. Whether you are a seasoned professional or just starting out in the field of neurology, this book offers valuable insights that can help improve patient outcomes and advance research in the field. With its comprehensive coverage of computational intelligence algorithms for neurological disorders, this book is sure to become an essential reference for anyone working in the field. Stay ahead of the curve with the latest research and techniques by adding "Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders" to your library today.
Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders (2026) is an advanced interdisciplinary textbook written by S.N. Kumar and published by Taylor & Francis Group. This book explores the application of artificial intelligence, machine learning, and computational intelligence techniques in the diagnosis and analysis of neurological disorders.
Neurological diseases such as Alzheimer’s disease, Parkinson’s disease, epilepsy, stroke, and multiple sclerosis require highly accurate and early diagnostic approaches. This book demonstrates how computational models and intelligent algorithms can significantly enhance diagnostic accuracy, assist clinicians, and improve patient outcomes.
The content integrates concepts from computer science, artificial intelligence, neuroscience, and clinical medicine, providing a multidisciplinary perspective on modern neurological diagnostics.
Key topics covered include:
- Fundamentals of computational intelligence in healthcare
- Machine learning algorithms for neurological disease detection
- Deep learning applications in brain imaging analysis
- Pattern recognition in EEG, MRI, and clinical data
- Predictive modeling for disease progression
- Feature extraction and data classification techniques
- Hybrid AI models for diagnostic decision support systems
- Challenges in AI-based medical diagnostics
A major strength of this book is its algorithmic and application-based approach, which explains how intelligent systems are built and used in real clinical environments. It bridges the gap between theoretical AI models and practical neurological diagnosis.
This 1st edition includes recent advancements in deep neural networks, convolutional neural networks (CNNs), and hybrid computational models applied to neuroimaging and electrophysiological data. It also highlights the role of big data analytics in personalized neurological care.
The book is especially useful for:
- Neuroscientists and neurologists
- AI and machine learning researchers
- Biomedical engineers
- Medical imaging specialists
- Computer science students and data scientists working in healthcare
With 384 pages of structured and research-oriented content, this hardbound edition serves as a comprehensive reference for understanding how computational intelligence is transforming neurological disease diagnosis.
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