Note
Please star ⭐ the repository to show your support.
- Automated threat monitoring is currently not intelligent or context-aware based on custom instructions.
- Existing threat prediction models require training data and time.
- This project uses existing models for motion detection and human detection, and uses LLMs for real-time context-aware threat prediction.
Created by Praneeth Vadlapati (@prane-eth)
The research paper is available at https://doi.org/10.55041/IJSREM33034
Open the file Experiment-AW.ipynb
and find the setup instructions in the first cell.
Run the code.
For more projects, open the profile: @Pro-GenAI
Copyright © 2024 Praneeth Vadlapati
Please refer to the LICENSE file for more information.
To request a permission to use my work, please contact me using the link below.
The code is not intended for use in production environments. This code is for educational and research purposes only. No author is responsible for any misuse or damage caused by this code. Use it at your own risk. The code is provided as is without any guarantees or warranty.
- Special thanks to Groq (https://groq.com/) for a fast LLM inference which saved me time for this research project.
- Flowchart image credits:
- Camera: https://pixabay.com/vectors/silhouette-security-cam-speed-3613225/
- Motion detection: https://pixabay.com/vectors/sprinting-running-jogging-sports-150117/
- Human detection: https://pixabay.com/vectors/mens-locker-room-man-human-toilet-155828/
- LLM: https://pixabay.com/vectors/circuits-brain-network-chip-5076887/
- First alert: https://pixabay.com/vectors/exclamation-mark-warning-danger-98739/
- Threat warning: https://pixabay.com/vectors/traffic-sign-attention-road-sign-38589/
For personal queries, please find my contact details here: linktr.ee/prane.eth