Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders

Part I: Introduction and Challenges

1. Introduction to Neurological Disorders

T. Manonmani, Mohit Malik, P. Abinaya

2. Navigating the Complexities of the Brain: Challenges and Opportunities in Computational Neurology

Ginni Arora, Alvaro Rocha, and Syamsundar Patta

3.  Challenges and Opportunities in Computational Neurology

S. Vijayanand and C. Priya

4.  Ethical Issues in Neurodisorder Diagnosis

Rufina Hussain, Safdar Tanweer, Sameena Naaz, and  Sherin Zafar

5. Ethical Issues in Neurodisorder Diagnosis: Computational Intelligence toward Compassionate Psychiatric Treatment

Bhupinder Singh, Rishabha Malviya, and Christian Kaunert

Part II: Neuroimaging and Diagnostic Techniques

6.  Improving Magnetic Resonance Imaging (MRI) for Better Understanding of Neurological Disorders

 Mohd Abdullah Siddiqui, Sohrab A. Khan, Charu Chhabra, Sahar Zaidi, and Habiba Sundus

7. Advancements in Neuroimaging Techniques in Encephalopathy

 Firdaus Jawed, Rabia Aziz,  Sohrab Ahmad Khan, Sumbul Ansari, and Shahnawaz                    Answer

8. Targeted Drug Delivery for Neurological Disorders

Bhupen Kalita

9. Intelligent Deep Learning Algorithms for Autism Spectrum Disorder Diagnosis

 V. Thamilarasi, R. Roselin, P. Pushpa, M. Kannan, and B. P. Sreejith Vignesh

10. Advanced Neuroimaging with Generative Adversarial Networks

Basil Hanafi, Mohammad Ubaidullah Bokhari, and Imran Khan

 11. Machine Learning Strategy with Decision Trees for Parkinson's Detection by Analyzing the Energy of the Acoustic Data

Arun P, Enrico M. Staderini, Madhukumar S, Careena P, Sarath P V, and Sreesh P R

12. Adaptive Convolution Neural Network-Based Brain Tumor Detection from MR Images

C. Prajitha, K. Thamaraiselvi, Rinesh. S, K. P. Sridhar, and Abubeker K M

13. STN-DRN: Integrating Spatial Transformer Network with Deep Residual Network for Multiclass Classification of Alzheimer’s Disease

Prabu Selvam, Sudharson S, and  Senthil Prakash PN

Part III: Machine Learning & AI Applications in Neurological Disorders

14. Evaluation of Supervised Learning Algorithms in Detection of Neurodisorders:  A Focus on Parkinson's Disease

Chitigala Mouleeshwari, Kishor Kumar Reddy C, Manoj Kumar Reddy, and Srinath Doss

15. Comparative Analysis of Supervised and Unsupervised Learning Algorithms in the Detection of Alzheimer’s Disease

Binson V A, Starlet Ben Alex, and Rangith Kuriakose

 16. Deep Learning Techniques in Neurological Disorder Detection

Manisha Nagar, Shikha Singh, Sanjay Singh, and Ruchi Jain

17.  From Data to Diagnosis: Supervised Learning's Impact on Neurodisorder Detection, with a focus on Autism Spectrum Disorder

S.Srividhya and S.R.Lavanya

18. Parkinson's Disease Detection from Drawing Images using Deep Pretrained Models

Sourabh Shastri, Sachin Kumar, and Vibhakar Mansotra

 19. Optimizing Digital Healthcare for Alzheimer's Disease: A Deep Federated Learning Convolutional Neural Network Scheme (DFLCNNS)

Swathi Sambangi  , T Kusuma  , D Srinivasa Rao  , G Lakshmeshwari  , and Rakhee

 20. Artificial Intelligence: A Game-Changer in Parkinson’s Disease Neurorehabilitation

Nabeela Rehman,  Arshya Anwar, and Sahar Zaidi

 21. Targeting Upper-Limb Sensory Gaps: New Rehab Insights for Chronic Neck Pain

Sahar Zaidi, Sohrab Ahmad Khan, Charu Chhabra, Habiba Sundus, and Irshad Ahmad

Biography

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.