An Automated Crime Prediction Model using Artificial Bee Colony Optimizer with Deep Learning on FIR Data
DOI:
https://doi.org/10.63682/jns.v14i33S.10452Keywords:
Crime prediction, FIR Data, Deep learning, Law enforcement, Machine learning, Artificial bee colony optimizerAbstract
The increasing complexity of modern law enforcement demands sophisticated tools for crime prediction using FIR (First Information Report) data. Crime prediction uses machine learning (ML) and data analysis techniques to predict or forecast the likelihood of a particular kind of crime being committed in the future. Crime prediction model utilizes different data sources such as demographic information, historical crime data, socioeconomic factors, weather conditions, and even geographic data. Predicting crime time and location based on FIR data is a very important research content and application in the fields of public safety and law enforcement. The ability to forecast when and where crime events occur holds great potential for more effectively addressing and preventing crime events. This task includes using machine learning techniques, historical FIR data, and advanced data analysis to make informed predictions. With this motivation, this study designs an artificial bee colony optimization with a deep learning-based crime prediction (ABCODL-CP) technique. The purpose of the ABCODL-CP technique is to investigate the FIR data to forecast the location and time of the crime incidents. To accomplish this, the ABCODL-CP technique undergoes detailed data pre-processing to convert the FIR data into a useful format. Moreover, the Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer can be used to convert unstructured FIR narratives into structured numerical representations, thereby capturing the importance of terms within the reports. Besides, the ABCODL-CP technique uses the Attention Convolutional BiLSTM Neural Network (AC-BiLSTM) model for crime location and time prediction. Finally, the ABC algorithm is applied for hyperparameter tuning, ensuring the classifier's accuracy and reliability in forecasting crime location and time. The experimental evaluations performed on the real-time FIR dataset underline the effective performance of the ABCODL-CP technique, thereby assisting law enforcement in resource allocation, patrol planning, and proactive intervention
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