V3I3P94

Enhanced Surveillance Analytics for Crime Prediction Using Supervised ML Algorithms Combined with Convolutional Neural Networks

Dr. V. Maniraj^1*, V. Abinaya²

Abstract

Crime prediction through machine learning provides an advanced and intelligent approach to understanding and anticipating criminal activities by extracting meaningful patterns from large scale datasets. The project integrates supervised learning algorithms with Convolutional Neural Network techniques to analyze crime occurrences using spatiotemporal and categorical features. The system begins with a structured preprocessing phase in which raw crime records are cleaned, timestamp fields are transformed into standardized formats, and essential temporal features are derived to capture variations across hours, days, and seasons. Missing information is handled systematically and feature concatenation is performed to create a more descriptive dataset. Exploratory data visualizations are then generated to observe crime trends, spatial distributions and correlations within the data. Following preprocessing, supervised machine learning models are trained and validated to ensure high predictive performance. The Convolutional Neural Network module plays a crucial role in extracting deep and complex patterns from spatial and temporal inputs such as latitude, longitude, and time intervals. The dataset is divided into training and testing sets to enable reliable performance evaluation, ensuring that the model generalizes well on unseen data and maintains accuracy across different scenarios. This functionality demonstrates the practical value of combining supervised learning with Convolutional Neural Network based feature extraction, providing a powerful tool for security agencies, smart city planners and surveillance systems. The system incorporates model saving and loading mechanisms, allowing seamless scalability, deployment and continuous updates as new data becomes available. Its modular architecture supports future improvements, such as integrating real-time crime feeds, expanding datasets and applying more advanced deep learning frameworks to further enhance prediction precision. A comprehensive, scalable and user oriented crime prediction framework that effectively merges supervised machine learning techniques with Convolutional Neural Networks. By utilizing data driven insights, the system offers reliable and actionable predictions that can support proactive crime prevention and improved public safety.

Keywords:

Crime Prediction, Machine Learning, Convolutional Neural Network (CNN), Spatiotemporal Analysis, Supervised Learning, Data Preprocessing, Pattern Recognition, Smart Surveillance