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Subrat SaurabhAuthor of Kuch Woh Pal
Neurological disorders pose a significant global health burden, affecting millions
of individuals and imposing considerable challenges on healthcare systems. Early and
accurate diagnosis is crucial for effective management and treatment. In recent years,
deep learning methods have emerged as powerful tools for medical image analysis,
offering promising avenues for automated detection and diagnosis of neurological
disorders. This abstract provides an overview of the current state of research in this
field, highlighting key methodologies, challenges, and future directions. Neurological
disorders encompass a broad range of conditions affecting the nervous system,
including the brain, spinal cord, and peripheral nerves. Traditional diagnostic
approaches often rely on clinical assessments, which may be subjective and timeconsuming. The advent of deep learning techniques has revolutionized medical image
analysis, enabling the development of automated systems that can assist in the early
and accurate detection of neurological disorders. Epilepsy is a neurological disorder
characterized as the recurrence of two or more unprovoked seizures. The common and
significant tool for aiding in the identification of epilepsy is electroencephalography
(EEG).
Dr. Kishori Shekokar, Dr. Shweta Dour
Dr. Kishori Sudhir Shekokar is a distinguished academician with over 16 years of extensive experience in teaching, research, and innovation. She earned her Ph.D. in Computer Science from Navrachana University, Vadodara, and completed her Bachelor’s and Master’s degrees in Computer Science and Engineering from PRMIT, Amravati University, Maharashtra. She currently serves as an Assistant Professor in the Artificial Intelligence and Data Science (AI-DS) Department at Parul Institute of Engineering and Technology, Parul University, Vadodara. Her academic journey is marked by a strong specialization in Computer Vision, Machine Learning, and Deep Learning, with additional expertise in Data Science.
Dr. Shekokar has made significant contributions to academia and research through her work in advanced domains. Her research output includes numerous publications in high-impact journals and presentations at prestigious conferences. These works have addressed innovative applications of machine learning and deep learning, such as epilepsy detection using neural networks and enhancing power grid stability through advanced algorithms.
Dr. Shweta Dour is a distinguished academician and researcher specializing in Electronics and Telecommunications Engineering. She holds a Ph.D. from Bhagwant University, an M.E. from the University of Mumbai, and a B.E. from Rajasthan University, all with first-class distinctions. With over 24 years of experience in academia, she is currently serving as an Assistant Professor at Navrachana University, Vadodara.
Currently, she serves as an Assistant Professor at Navrachana University, Vadodara, where she has been contributing since 2013.Her expertise spans various domains, including data science, machine learning, and deep learning, supported by certifications from IIT Roorkee. Dr. Dour has guided multiple Ph.D. candidates and authored numerous journal publications and conference papers on advanced topics like epilepsy detection using deep learning and power grid stability.She holds patents for innovative solutions, such as a deep learning-based skin segmentation system and an IoT-based digital locking device. Dr. Dour has also authored a book on machine learning and natural language processing.
