Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders

Herausgeber: Kumar, S. N.; Zafar, Sherin; Naaz, Sameena

• Gebundenes Buch

Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 336
• Erscheinungstermin: 6. August 2025
• Englisch
• Abmessung: 234mm x 156mm
• ISBN-13: 9781032858906
• ISBN-10: 1032858907
• Artikelnr.: 73480344

Autorenporträt

S. N. Kumar received his B.E. degree from the Department of Electrical and Electronics Engineering, Sun College of Engineering and Technology, in 2007, his M.E. degree in applied electronics from the Anna University of Technology, Tirunelveli, and his Ph.D. degree from the Sathyabama Institute of Science and Technology in 2019. He is currently an Associate Professor with the Department of Electrical and Electronics Engineering, Amal Jyothi College of Engineering, Kanjirappally, and his research areas include medical image processing and embedded systems. Sherin Zafar is an Assistant Professor of Computer Science and Engineering at the School of Engineering Sciences and Technology, Jamia Hamdard University, with a decade of successful experience in teaching and research management. She specializes in wireless networks, soft computing, and network security. Sameena Naaz is a Senior Lecturer at the Department of Computer Science, School of Arts, Humanities and Social Sciences at the University of Roehampton, London, UK, with more than 22 years of experience. She received her M.Tech. degree in Electronics with Specialization in Communication and Information Systems from Aligarh Muslim University in 2000 and completed her Ph.D. from Jamia Hamdard in the field of distributed systems in 2014. Her research interests include distributed systems, cloud computing, big data, machine learning, data mining, and image processing.

Inhaltsangabe

PART A: Introduction and Challenges Chapter 1 Introduction to Neurological
Disorders Chapter 2 Navigating the Complexities of the Brain Challenges and
Opportunities in Computational Neurology Chapter 3 Challenges and
Opportunities in Computational Neurology Chapter 4 Ethical Issues in
Neurodisorder Diagnosis Chapter 5 Ethical Issues in Neurodisorder
Diagnosis: Computational Intelligence towards Compassionate Psychiatric
Treatment Part-B: Neuroimaging and Diagnostic Techniques Chapter 6
Improving Magnetic Resonance Imaging (MRI) for Better Understanding of
Neurological Disorders Chapter 7 Advancements in Neuroimaging technique in
Encephalopathy Chapter 8 Targeted Drug Delivery for Neurological Disorders
Chapter 9 Intelligent Deep Learning Algorithms for Autism Spectrum Disorder
Diagnosis Chapter 10 Advanced Neuroimaging with Generative Adversarial
Networks Chapter 11 Machine Learning Strategy with Decision Trees for
Parkinson's Detection by Analyzing the Energy of the Acoustic Data Chapter
12 Adaptive Convolution Neural Network-based Brain Tumor Detection from MR
Images Chapter 13 STN-DRN: Integrating Spatial Transformer Network with
Deep Residual Network for Multiclass Classification of Alzheimer's Disease
Part C: Machine Learning & AI Applications in Neurological Disorders
Chapter 14 Evaluation of Supervised Learning Algorithms in Detection of
Neurodisorders: A Focus on Parkinson's Disease Chapter 15 Comparative
Analysis of Supervised and Unsupervised Learning Algorithms in the
Detection of Alzheimer's disease Chapter 16 Deep Learning Techniques in
Neurological Disorder Detection Chapter 17 From Data to Diagnosis:
Supervised Learning's Impact on Neuro-disorder detection, with a focus on
Autism Spectrum Disorder Chapter 18 Parkinson's Disease Detection from
Drawing Images using Deep Pretrained Models Chapter 19 Optimizing Digital
Healthcare for Alzheimer's: A Deep Federated Learning Convolutional Neural
Network Scheme (DFLCNNS) Chapter 20 Artificial Intelligence: A Game-Changer
in Parkinson's Disease Neurorehabilitation Chapter 21 Targeting Upper Limb
Sensory Gaps: New Rehab Insights for Chronic Neck Pain