APOORV GARG

Project-Malware Detection using ML

Created datasets with numerous inputs from malware file using python.
Testing models like Neural Network. Decision Trees, Logistic Regression etc to obtain maximum accuracy in classification of malware.

Project -Anti-UAV(Ongoing)

Spearheaded development of Anti-UAV (Unmanned Aerial Vehicle) system, focusing on enhancing security and airspace control.
Designed and implemented advanced object detection models like RetinaNet, YOLOv4, and YOLOv7, leveraging state-of-the-art deep learning techniques to identify potential threats and intrusions.
Orchestrated tracking mechanisms to monitor and trace UAV movements within designated areas, ensuring real-time threat assessment.
Utilized stereo vision technology to calculate distances accurately, enhancing system's ability to respond to UAVs effectively.
Conceptualized and engineered entire system, from ideation to proof of concept (POC), resulting in impressive initial performance metrics and improved security.

Project -NHAI(Highway Health Monitoring)

Prior to Anti-UAV project, demonstrated strong data engineering skills by curating custom dataset for crosswalk detection models through web scraping and data manipulation.
Trained crosswalk detection models using YOLO and Faster RCNN.

Project -Concrete Strength Prediction

Contributed to development of machine learning models for predicting concrete strength, enhancing project efficiency and safety standards.

• Research included finding by how much production planning in NCC is lacking from the original framework of Last Planner System and also improvement measures were suggested to improve the company’s efficiency.
• Conducted research projects, gathering data and information from a variety of sources.
• Worked in team environment to maintain high levels of productivity.
• Got recognition for the research work both by institution and the company.