Banca de DEFESA: RAMIRO DE VASCONCELOS DOS SANTOS JÚNIOR

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : RAMIRO DE VASCONCELOS DOS SANTOS JÚNIOR
DATE: 02/05/2024
TIME: 16:00
LOCAL: Sala Google Meet - meet.google.com/xsk-pwvq-jqp
TITLE:

Using Machine Learning to Classify Criminal Macrocauses in Smart City Contexts

KEY WORDS:

Crime Analysis, Criminal Macrocause, Machine Learning, Predictive Policing, Public Safety, Smart Cities

PAGES: 105
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Matemática da Computação
SPECIALTY: Modelos Analíticos e de Simulação
SUMMARY:

Our research presents a new approach to classifying macrocauses of crime, specifically
focusing on predicting and classifying the characteristics of ILVCs. Using a dataset
from Natal, Brazil, we experimented with five machine learning algorithms, namely
Decision Trees, Logistic Regression, Random Forest, SVC, and XGBoost. Our methodology
combines feature engineering, FAMD for dimensionality reduction, and SMOTE-NC for
data balancing. We achieved an average accuracy of 0.962, with a standard deviation of
0.016, an F1-Score of 0.961, with a standard deviation of 0.016, and an AUC ROC curve of
0.995, with a standard deviation of 0.004, using XGBoost. We validated our model using
the abovementioned metrics, corroborating their significance using the ANOVA statistical
method. Our work aligns with smart city initiatives, aiming to increase public safety and
the quality of urban life. The integration of predictive analysis technologies in a smart
city context provides an agile solution for analyzing macrocauses of crime, potentially
influencing the decision-making of crime analysts and the development of effective public
security policies. Our study contributes significantly to the field of machine learning applied
to crime analysis, demonstrating the potential of these techniques in promoting safer urban
environments. We also used the Design Science methodology, which includes a consistent
literature review, design iterations based on feedback from crime analysts, and a case
study, effectively validating our model. Applying the classification model in a smart city
context can optimize resource allocation and improve citizens’ quality of life through a
robust solution based on theory and data, offering valuable information for public safety
professionals.

COMMITTEE MEMBERS:
Presidente - 1678918 - NELIO ALESSANDRO AZEVEDO CACHO
Interno - 2177445 - BRUNO MOTTA DE CARVALHO
Externo ao Programa - 1669545 - DANIEL SABINO AMORIM DE ARAUJO - UFRNExterno à Instituição - ARAKEN DE MEDEIROS SANTOS - UFERSA
Externa à Instituição - THAIS GAUDENCIO DO REGO - UFPB