Dr. Ambily Francis
Editor
Department of Electronics and Communication, Sahrdaya College of Engineering and Technology, Kerala .
Email: ambilyfrancis@sahrdaya.ac.in , ambily222000@gmail.com
About
Dr. Ambily Francis has carried out her professional duties with great commitment and skill for over five years. She joined Sahrdaya College of Engineering and Technology in 2013, after gaining nearly three years of teaching experience in other self-financing colleges. Since 2020, she has been the NBA Coordinator for the Department of Electronics and Communication, playing a key role in maintaining quality standards and achieving accreditation goals. She completed her PhD in 2023, focusing on computational methods for Alzheimer’s disease detection, a testament to her research expertise. In the same year, she assumed the role of Head of the Department, overseeing academic and administrative activities with a focus on the department’s development. Additionally, she served as the Staff Secretary during the 2023-2024 academic year, showcasing her organizational abilities and leadership qualities. Her dedication to teaching, research, and administrative responsibilities has significantly benefited the institution. She organized faculty development programs and workshops for the enrichment of students and faculty.
Dr. Ambily Francis is a dedicated researcher with expertise in Medical Image Processing, Image Compression, Spiking Neural Networks, and VLSI-based AI Implementations. Her research primarily focuses on developing advanced computational techniques for biomedical diagnostics, with a particular emphasis on Alzheimer’s Disease detection, Diabetic Retinopathy analysis, and efficient medical image processing methods. She has published over 10 research papers in reputed international journals and conferences, contributing significantly to the fields of image analysis, deep learning, and neural network-based medical applications.
Her work explores novel approaches to feature extraction, classification, and enhancement of medical images, leveraging deep learning techniques to improve diagnostic accuracy. She has also contributed to biologically inspired Spiking Neural Networks (SNNs), aiming to develop energy-efficient and real-time processing models. Additionally, her research in image compression techniques focuses on optimizing medical image storage and transmission while preserving critical diagnostic details. More recently, she has explored VLSI-based implementations to accelerate AI-driven healthcare applications, ensuring efficient real-time performance.
Research Interest
As an active member of IEEE, ISTE, and IAENG, Dr. Ambily Francis is committed to advancing research in medical imaging and fostering interdisciplinary collaborations to bridge the gap between technology and healthcare.