Deep Learning Algorithm for Detecting and Analyzing Criminal Activity


  • Raddam Sami Mehsen



deep learning, data forensic, digital security, digital crime, criminal activity


When applied to an entire field, automation and autonomous systems are among the rare creative superpowers capable of catapulting progress at an exponential rate. The arrival of machine intelligence will give such automated machines the intelligence to perform their tasks with power of outcome, drastically reducing the need for human intervention in redundant processes. Large-scale technological progress can be traced back to responsibilities that are simplified and, as a result, more easily distinguished by means of automation. In accordance with these guidelines, we propose creating a product that eliminates or significantly reduces the need for human intervention in primary issue statements that can be automated and processed. The public safety infrastructure of today relies on surveillance cameras, but these devices are merely video recorders; they have no intelligence of their own. Automated video streams are now required for automatic event detection thanks to the massive amount of data produced by surveillance cameras. The project's main objective is to increase public safety through the mechanization of crime measurement and review using actual Closed-Circuit Television footage (CCTV). This is achieved by assigning the task of recognizing criminal behavior to a system that can do so automatically, allowing for more precise tracking. In this study, we present a model with a precision of 0.95 for assault and 0.97 for abuse.


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How to Cite

Mehsen, R. S. (2023). Deep Learning Algorithm for Detecting and Analyzing Criminal Activity. International Journal of Computing, 22(2), 248-253.