A HYBRID MACHINE LEARNING MODEL FOR CRIME RATE PREDICTION

Authors

  • Abdulladeef Abubakar Ibrahim
    Computer Science
  • Yusuf Musa Malgwi
    Computer Science
  • Yahaya Ali
    Electrical and Electronic Technology

Keywords:

Crime rate prediction, Machine learning, Convolutional Neural Networks, K-means clustering, Public safety, Resource management

Abstract

Crime prediction is vital for public safety and resource management. This study developed a hybrid machine learning model integrating Convolutional Neural Networks (CNN) and K-means clustering for crime rate prediction. Historical crime data from Mubi and Yola from the year 2015 to 2023 yielded training and testing accuracies exceeding 90%, surpassing traditional models (Random Forest and Decision Tree Classifiers). Results underscore the effectiveness of CNN and K-means integration in recognizing spatial patterns and clustering data, demonstrating improved predictive accuracy and forecasting capabilities of predicting crimes up to 2030. This research contributes to advanced crime prediction systems, informing law enforcement agencies' proactive crime prevention and resource allocation.

Author Biography

Yusuf Musa Malgwi

Department of Computer Science Modibbo Adama University Yola.

PhD 

Dimensions

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Published

02-12-2024

How to Cite

A HYBRID MACHINE LEARNING MODEL FOR CRIME RATE PREDICTION . (2024). FUDMA JOURNAL OF SCIENCES, 8(6), 101-106. https://doi.org/10.33003/fjs-2024-0806-2789

Issue

Vol. 8 No. 6 (2024): FUDMA Journal of Sciences - Vol. 8 No. 6

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

A HYBRID MACHINE LEARNING MODEL FOR CRIME RATE PREDICTION . (2024). FUDMA JOURNAL OF SCIENCES, 8(6), 101-106. https://doi.org/10.33003/fjs-2024-0806-2789