Crime Prediction and Criminal Identification System Using Machine Learning - Jadhav Rupa; Jadhav Neha; Khopade Bhagyashree; Vedpathak Vaishnavi; S.B. Bhankar; K.S. Khamkar (International Journal of Electronics Automation, 2024)
Advanced machine learning and data analytics-driven crime prediction and criminal identification systems have become game-changing instruments for contemporary law enforcement. Utilizing past crime statistics, surveillance footage, and additional resources, these systems forecast criminal activity, manage resources efficiently, and improve investigation capacities. With an emphasis on their importance in enhancing public safety and lowering crime rates, this paper presents an overview of criminal identification and prediction systems. Examining the technologies and algorithms that support these systems, it highlights how they can be used for effective investigations, proactive enforcement, and resource efficiency. The study also looks at the problems and moral issues that these systems raise, such as transparency, algorithmic fairness, and data privacy. It also talks about how to enhance the usefulness of models by continuously refining them and improving the quality of the data. Crime is one of the greatest and most overwhelming issues in our public and it’s anything but a significant errand. Every day there are tremendous quantities of wrongdoings perpetrated regularly. This requires monitoring every one of the wrongdoings and keeping an information base for same which might be utilized for future reference. Various kinds of wrongdoings and the full thought of the insurance and security of residents in any are huge segments that assume an imperative part straight-forwardly in the nature of the existences of inhabitants.
Keywords: Crime prediction; Data collection; ML; Prediction model; identification