Dr Lavanya Srinivasan
Lavanya Srinivasan is a lecturer in Computer Science at the School of Computing and Engineering. Her area of research interest is image processing using computer vision techniques. Her research goal is to innovate ideas that allow machines to learn from and understand the information conveyed from images.
-
Qualifications
PhD, Image Processing
ME, Systems Engineering
BE, Electronics and Communication Engineering
Research
-
Research and publications
Lavanya Srinivasan, “Automatic Sketch Generation for Person Recognition,” IEEE Access, 2023.
Lavanya Srinivasan, “Frequency Based Gait Gender Identification,” IEEE Access, 2023.
Lavanya Srinivasan, “Automatic Gait Gender Classification Using Convolutional Neural Networks,” ACM International Conference on Image Processing and Machine Vision, 2023.
Lavanya Srinivasan, “Analysis of Gait Biometric in the frequency domain,” IEEE Conference on Cloud Computing, Computer Vision and Image Processing, 2022.
Lavanya Srinivasan, "Identification and classification of COVID Images using Machine Learning, " International Conference on AI in Aging and Age-related Diseases, Elsevier, 2022.
Lavanya Srinivasan, “Gait Biometric for Person Re-Identification,” International Conference on Biomedical Signal, Image and Vision Processing, 2021.
Lavanya Srinivasan, “Gait Biometric for Person Re-Identification,” International Journal of Biomedical and Biological Engineering, Vol.15(12), pp.326 – 330, 2021.
Lavanya Srinivasan, “Inverse maximum likelihood-based edge detection for segmentation of breast lesion using active contour,” International Journal of Biomedical Engineering and Technology, Vol. 22 (3), pp.272-283, 2016.
Lavanya Srinivasan, “An approach to identify a lesion in infrared breast thermography images using segmentation and fractal analysis,” International Journal of Biomedical Engineering and Technology, Vol.19 (3), pp.220-229, 2015.
-
Research degree supervision
Fall Detection and Prevention Using Machine Learning and Deep Learning
An unexpected incident that forces people to take a seat on the lower level (floor or ground) is referred to as a fall. It consequently results in injuries that frequently have catastrophic consequences. Psychological complaints are also seen as a fall's aftermath. Anxiety, depression, activity restriction and a fear of falling are all possible mental health conditions.
The main physiological problem that older people face is a fear of falling, which limits their day-to-day activities. Because of their dread, older persons restrict their activities, which can impair their mobility and independence by weakening their muscles and causing inadequate gait balance. Understanding falls can be categorized into two categories for this purpose: fall detection and fall prevention. Fall detection is the process of identifying a fall by tracking a person's movements. Fall detection is the practice of spotting a fall by observing someone moving. Therefore, the research incorporates Machine Learning and Deep Learning for high classification accuracy for fall detection and prevention.
For full details of Dr Srinivasan's PhD degree opportunities, visit the SCE research degree page.
