Predictive crime analytics is a process of evaluating a dataset to discover veiled patterns that can be useful in forecasting crime occurrence. The application of a machine learning algorithm is widely used in developing artificial intelligence to integrate into computer systems. This project aims to develop a management system for crime records implementing predictive crime analytics in forecasting crime occurrences. Through predictive crime analytics and software development life cycle, a management information system that is capable of forecasting crime was successfully developed. A predictive model was designed using multinomial logistic regression, which acquires a totality accuracy of 86.60% and prediction confidence. It is also shown in this paper that regression is better than other classification algorithms in terms of predicting crime occurrence. Moreover, discussed in this research, among all the barangay in Calauan, Laguna. Dayap is the most vulnerable in different index crimes like rape, murder, robbery, theft, homicide, and illegal drugs.
This research was conducted to help the traffic policy makers and general public in preventing road incidents using the collected traffic accident dataset between the years 2016 and 2019. Data mining using classification algorithm was utilized to develop a predictive model for predicting occurrences of traffic accidents. Classification algorithms such as decision tree, k-nn, naïve bayes and neural network have been compared in identifying better classification capability in classifying stage of felony. Neural network shows a very promising result in classifying road accident with a total accuracy result of 87.63%. Nonetheless, k-nn and naïve bayes both acquired a higher than 80% accuracy which shows that this classification algorithms were also good in predicting road accidents. Moreover, public vehicle is more prone in accident rather than private vehicle in both stage of felony and accident may occur between or on 3:00pm and 6:00pm.
Purpose -This study developed an online crime reporting system that uses artificial intelligence to analyze crime incident reports to provide up-to-date crime statistics, map crime hot locations, and manage dynamic databases.Method -The knowledge discovery process in databases (KDD) was utilized for the model development. Scrum, an agile development technique, proved helpful in the iterative and Research Implications -The academe and local law enforcement agencies must collaboratively develop strategies that reinforce the importance of community engagement. The system provides public access through geo-mapping that supports the community, or locally based crime prevention, instead of targeting individuals, targets areas where the risks of becoming involved in crime or being victimized are high.
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