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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...
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Towards Identifying Paid Open Source Developers - A Case Study with Mozilla Developers
Open source development contains contributions from both hired and volun...
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AppsPred: Predicting Context-Aware Smartphone Apps using Random Forest Learning
Due to the popularity of context-awareness in the Internet of Things (Io...
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The Impact of Countdown Clocks on Subway Ridership in New York City
Protecting the passengers' safety and increasing ridership are two never...
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A Comparison of Statistical and Machine Learning Algorithms for Predicting Rents in the San Francisco Bay Area
Urban transportation and land use models have used theory and statistica...
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Continuous User Authentication via Unlabeled Phone Movement Patterns
In this paper, we propose a novel continuous authentication system for s...
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Deep Factorization Machines for Knowledge Tracing
This paper introduces our solution to the 2018 Duolingo Shared Task on S...
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Prediction of Homicides in Urban Centers: A Machine Learning Approach
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.
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