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Abstract:Crime poses a significant challenge to the prosperity and growth of nations. Various factors,
including poverty, deficiencies in the legal system, unstable economic conditions, and insufficient
technological capabilities for crime analysis and detection, contribute to its emergence. This work
proposes a novel framework for crime identification and detection utilizing generative and deep
learning models. Initially, we extract latent features from an available dataset using a combination
of generative model, Deep Learning (DL) and Machine Learning (ML), techniques, including a
Variational Auto-Encoder (VAE), transformers, Convolutional Neural Networks (CNN), and Principal
Component Analysis (PCA), followed by K-means clustering. We then evaluate the effectiveness
of this clustering approach using renowned classifiers, such as Random Forest (RF), Decision
Tree (DT), Gradient Boosting (GB), and Naive Bayes (NB). As a result, the framework, which
utilizes VAE for feature extraction and combines it with RF as the classifier, achieves the highest
accuracy of 0.9993. The strength of the proposed framework lies in its unsupervised learning
approach, which attains significant information from data without relying on labeled datasets.
This data-driven methodology adeptly leverages generative and deep learning models for feature
extraction, subsequently employing these features for crime detection. Furthermore, we analyze
individual attributes in latent spaces and apply classifiers, with the VAE demonstrating exciting
performance, achieving an accuracy of 0.999. |