An Effective Method for Forecasting Crime Analysis using Deep Learning Classifiers

Crime is one of our society's most serious issues. As a result of its pervasiveness in our culture and society, the task of preventing crime is one of the utmost crucial ones. The suggested model in this article is created to identify crime trends, and the aim of this research is to assess the efficacy of criminal inquiries based on inferences. The study draws conclusions from the crime scene and uses those conclusions to predict the offender. Here, the data set includes It includes details on the crime, such as the location's description, the type of crime, the date, the time, the latitude, and the longitude. Before training the model, data pre-processing will be performed, followed by feature selection and scaling to ensure high accuracy. The categorization techniques help the inquiry reach its objective, and this tactic helps identify a connection between two mathematical values. This technique forecasts the values of the dependent variables based on the independent variables, allowing the multilayer perceptron and machine learning reliant algorithm to classify the datasets and anticipate crimes with a higher degree of accuracy. The proposed method has obtained accuracy rates of 99.2%, recall rates of 98.91 %, precision rates of 98.08%, and F1-score rates of 96.44% in crime detection utilizing MLP architecture.