Project Title: UAV Mission Alarm Prediction using Advanced Machine Learning Techniques

Description: This project focuses on the development and application of predictive models for unmanned aerial vehicle (UAV) missions. Using advanced machine learning techniques such as logistic regression and random forests, the project accurately forecasts alarms for various parameters including TEAM, AVO, PLO, and DEMPC during UAV missions.

Key Achievements:

• Developed and deployed a comprehensive machine learning environment utilizing Scala for Databricks, facilitating efficient AI model management and achieving project objectives effectively.
• Conducted research at ASU on UAV simulators, integrating Artificial Intelligence to analyze and optimize teamwork dynamics in complex military environments. Emphasis was placed on understanding and mitigating challenges related to uncertainty and teammate interdependency.
• Led data collection efforts by designing and implementing protocols to gather relevant data points from UAV simulations, contributing to the study's success.

This project demonstrates the integration of cutting-edge machine learning techniques into UAV mission planning and optimization, ultimately enhancing operational efficiency and effectiveness.