Aniirudh Ramesh

This paper demonstrates an NHSMDP-based naive algorithm that provides an optimized egress plan for evacuees during active-shooter incidents, aiming to reduce casualties and time spent in the shooter's line of sight by leveraging prior building knowledge and real-time shooter location updates.

Demonstrates the effectiveness of a non-homogeneous semi-Markov-Decision-Process (NHSMDP) based naive algorithm for providing optimized egress plans during active-shooter incidents, showing significant reduction in casualties and exposure time compared to intuitive responses.

Focuses on the application of pre-trained deep learning algorithms for the accurate classification of diseases in tomato crops, which is crucial for early detection and mitigation in agricultural production.

Explores the use of bag of features and multiclass support vector machines for classifying diseases in maize crops, contributing to automated plant disease detection.

Investigates a transfer learning approach for classifying diseases in grape crops, showcasing advancements in applying deep learning techniques to agricultural challenges.