Crime Classification Using GRU and 1CNN Techniques

Abstract

Criminal activity is a widespread social problem that affects a nation's standard of living, rate of economic development, and international standing. Numerous crimes have proliferated worldwide in recent years. It has a variety of effects on people of all types. Classification of crime is necessary to create a safe society. Many researchers classify the crimes using machine learning techniques with a common dataset, which presents numerous computational opportunities and challenges. Using the learning capabilities of several deep learning architectures, we are trying to classify the criminal activity from the real-time dataset of particular city. In the proposed work, Gated Recurrent Unit (GRU) and Convolutional Neural Network (1CNN) deep learning architecture are used for crime classification. We have conducted performance comparison for classifying the crime with real-time dataset. The effectiveness of the aforementioned deep learning models was assessed by Training and Validation Loss curve. It was discovered that 1CNN models achieve better result compared to GRU. 1CNN produced 97% accuracy.