An Exploration of Crime Type and Prediction Using RALASD Feature Selection Algorithm with Deep Learning Technique

Abstract

In daily life, there is an enormous number of crimes that are frequently committed. The crime tracking and maintaining crime dataset is a challenging.Prediction of crime is an administration of regulation in the society by analyzing the statistical by using the data employed from a source. The source of data is used for the analysis of the crime patterns and crime rates in the particular region using data mining and deep learning techniques. The key objective of this work is to analyze the crime activities based on the information set using data mining techniques and predicting the crime rates using deep learning techniques. The work employs the crime dataset with various crime types occurs in the various region for the analysis. The analysis of the crime dataset includes pre-processing steps to construct a crime profile for the prediction. The pre-processing steps include removal of missing values and duplicate information from the dataset and finally convert the dataset into an encoded format for further identification. Then the encoded format of the dataset is employed in the selection phase for the attribute selection using feature selection strategy to reduce and select significant crime variables for the prediction. Finally, the crime prediction process is performed with the deep learning strategy. The prediction of the crime types is performed with the selected subset of the crime variables to increase the prediction accuracy.