Prediction of Homicides in Urban Centers: A Machine Learning Approach

by   José Ribeiro, et al.

Relevant research has been standing out in the computing community aiming to develop computational models capable of predicting occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crimes, and analyzing crimes according to time. This, due to the social impact and also the complex origin of the data, thus showing itself as an interesting computational challenge. This research presents a computational model for the prediction of homicide crimes, based on tabular data of crimes registered in the city of Belém - Pará, Brazil. Statistical tests were performed with 8 different classification methods, both Random Forest, Logistic Regression, and Neural Network presented best results, AUC   0.8. Results considered as a baseline for the proposed problem.


Comparison Analysis of Tree Based and Ensembled Regression Algorithms for Traffic Accident Severity Prediction

Rapid increase of traffic volume on urban roads over time has changed th...

Predicting the Popularity of Reddit Posts with AI

Social media creates crucial mass changes, as popular posts and opinions...

Towards Identifying Paid Open Source Developers - A Case Study with Mozilla Developers

Open source development contains contributions from both hired and volun...

Random Forest classifier for EEG-based seizure prediction

Epileptic seizure prediction has gained considerable interest in the com...

AppsPred: Predicting Context-Aware Smartphone Apps using Random Forest Learning

Due to the popularity of context-awareness in the Internet of Things (Io...

Do elderly want to work? Modeling elderly's decision to fight aging Thailand

Thailand has entered into an aging society since the year 2000. Using th...

Deep Learning Models to Predict Pediatric Asthma Emergency Department Visits

Pediatric asthma is the most prevalent chronic childhood illness, afflic...