• Abdullah Matwawala
• Ishita Bej
• Sanjana Gupta
• Shreya Kadge
• Sharique Ahmad

Crime is a pervasive issue that affects communities around the world, and its prediction and prevention are crucial for ensuring the safety and well-being of citizens. This project focuses on the development of a deep learning-based predictive model for estimating crime rates in a given geographic area. By harnessing the power of deep learning techniques, such as neural networks and convolutional neural networks (CNNs), we aim to provide law enforcement agencies and policymakers with a valuable tool for proactive crime prevention and resource allocation.Moreover, this project contributes to the broader field of deep learning applications in social issues, highlighting the potential of artificial intelligence to address complex societal challenges.

deep learning, lstm, cnn, crime area, predictions, location, feature selection, sos

"Human safety and prevention through crime identification and tracking", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.h182-h188, April-2024, Available :http://www.jetir.org/papers/JETIR2404722.pdf

"Human safety and prevention through crime identification and tracking", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. pph182-h188, April-2024, Available at : http://www.jetir.org/papers/JETIR2404722.pdf