Research Article
An Optimized Machine Learning and Big Data Approach to Crime Detection
Table 1
Summary of related works.
| References | Dataset | Methods used | Evaluation metrics | Limitations |
| Navalgund and K. (2018) [4] | YouTube and Google | VGGNet -19 | Accuracy, recall, F1-score and support | Detection of crime hotspots and probability of occurrences not included. | Younghyun Lee et al. (2011) [5] | Real-time elevator data collected using surveillance camera of pixels | Violent frame detector, motion vector extraction, and foreground segmentation | Detection rate, no. of people in the elevator, false-positive rates (FPR) | Includes only detection but not prediction or probabilities of occurrence results | Nakib et al. (2018) [6] | Real-time data | Softmax regression model, CNN | Accuracy | The size of the dataset was relatively small. The model was not evaluated against the other classical models. | Ranjan et al. (2018) [7] | Image collected from various internet sources and then morphed to test the methods | SVM and ANN | Accuracy, sensitivity and specificity | Comparison of the results with other traditional approaches were not included. Availability of larger dataset also is a challenge | Vynokurova et al. (2020) [8] | Real-time dataset | SVM and random forest-based hybrid approach | Accuracy | Comparison of the results with other traditional approaches were not included. Availability of larger dataset also is a challenge |
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