
Accomplished and High-performing engineer with a proven track record, specializing in software development and database design. Excelled in creating innovative solutions and enhancing user experiences, demonstrating exceptional teamwork and problem-solving skills. Proficient in Python and R, significantly improved productivity and software performance. with solid experience in providing academic and administrative support to faculty members and students. Skilled at conducting research, managing projects and assisting with course materials. Strong problem-solving skills and committed to staying current with best practices and trends in higher education.
Software Development
Malnutrition in children and adolescents presents multifaceted challenges, encompassing both undernutrition and overnutrition and significantly affecting their physical growth and cognitive outcomes across their lifespan. Advanced computing algorithms, particularly machine learning (ML) and deep learning (DL) have emerged as pivotal tools for identifying, treating, and designing interventions for child malnutrition. This study is a systematic literature review that delineates the utilization of ML models in addressing malnutrition among children and adolescents using the PRISMA framework.
Anemia is a major undernutrition concern in developing countries. Anemia in early childhood leads to lower immunity and diminished cognitive development and is one of the major causes of early childhood mortality. In India, the major burden of anemia is seen in the Empowered Action Group (EAG) states. Concerted efforts are needed to reduce the burden of anemia. This study uses machine learning (ML) models to predict anemia among children aged six to fifty-nine months using data from the fifth round of the Indian Demographic Health Survey (DHS), also known as the National Family Health Survey – 5 (NFHS – 5) in the EAG states
Microsoft Excel
Power BI
Tableau
Microsoft Excel