Atharva Tundalwar

Paper describing application of deep learning (CNN) approaches for real-time diagnosis of rice leaf diseases and proposing remedies, authored with Rajani P.K., Prerna Mhaisane, Kalyani Patil, Bhagyashree Marathe, and others.

A comparative study applying several supervised machine-learning algorithms (e.g., Random Forest, XGBoost, KNN, Logistic Regression, SVM) to predict heart disease using a Kaggle dataset; evaluates models by accuracy, precision, recall and F1-score and reports XGBoost as having the highest accuracy in experiments.

Co-authored work on staged machine-learning approaches for heart disease prediction.

Multiple listings and proceedings attributing research on heart disease prediction using machine-learning techniques, appearing in conference/journal collections and institutional compilations.