His research concentrates on designing, analysing, and implementing provably suitable Machine learning methods. His primary research interest is Machine-learning algorithms in various application fields. These areas include computational biology, optimisation, software engineering and nature-inspired algorithms.
Dr. Anand focused his research on re-enforcement learning and Association rule learning algorithms. He has designed many research algorithms in machine learning and nature-inspired optimisation fields. He handles presentations and technical talks in colleges, research institutions and R & D organisations.
Her area of interest is health informatics, a multidisciplinary field including both computer science and medicine. Her research work includes in the field of organ transplantation and its survival rate prediction by machine learning techniques. She is a Post Doctoral Fellow selected for Chief Minister's Nava Kerala Post Doctoral Fellowship
Her prime research areas include medical image processing, machine learning algorithms and computer vision. She has come up with various deep learning architectures, which helped in different medical diagnoses. The bio-medical image analysis includes CT and MRI images of the chest and brain.
Her area of research includes the design of nature-inspired hyper-heuristic algorithms for global optimization problems. She is applying the designed hyper-heuristic algorithms in Engineering applications.
Her research area includes analysing chromosomal aberrations that lead to the leukemic transformation of an otherwise normal cell. The challenge is to define the genes located at the sites of translocations in malignant cells and determine the alterations in gene function associated with the translocation. She is currently working on Machine learning models to predict the stage and type of acute lymphoblastic leukaemia.
Her focus is to understand the possibility of transmitting an animal virus (RNA viruses) to humans (Zoonosis) to predict a possible outbreak before the onset. She is working on Machine learning models to train the system which can predict Protein-Protein interactions that are topologically and functionally similar to experimentally known interactions which facilitate the possibility of viral transmission from animals to humans.
Her research area is machine learning approaches for Cyber Forensics Investigation. The primary aim is to identify the areas where machine learning techniques can be applied during the Cyber Forensics Investigation phases. Currently, she is working on enhancing keyword search by implementing clustering methods on current forensic tools.
Beena is researching the design and development of population-based multi-objective meta-heuristic algorithms for optimization problems and currently focusing on solving optimization problems in Robotic path planning using Nature-Inspired Metaheuristic Algorithms.
Her research interests include medical image processing, machine learning, and computer vision. The emphasis is on lung diseases that cause chest congestion. The work is primarily focused on lung disease identification and infection region detection. She is also interested in developing real-time applications for computer-assisted lung disease severity assessment.
Her research interest is in applying machine learning in the disaster management domain. She is currently working on landslide identification and quantitative risk analysis using machine learning.
Her research interests include digital image processing and applied machine learning. She has proposed architectures to diagnose skin cancer and identify sign languages efficiently. She is currently focusing on developing deepfake detection algorithms.