Abstract The importance of early prediction in reducing the impact of crime cannot be overstated. Machine learning algorithms have proven to be effective in this regard, but their inability to capture key features automatically can be a hindrance. To overcome this challenge, we propose a deep neural network model that is capable of extracting salient features automatically for predicting crime categories using real-world crime data sourced from the Chicago open data portal. To ensure the robustness of our proposed model, we carried out an extensive exploratory data analysis to determine the impact of socioeconomic indicators on crime occurrences. Additionally, we implemented a data upsampling technique to handle class imbalance issues, and we leveraged hyperparameter optimization algorithms to fine-tune the model. The results of our study were impressive. Our proposed model outperformed the baseline model and other algorithms, with an average improvement of 6% in macro F1 score. This suggests that our model is highly effective, if not superior, in predicting crime categories accurately. Overall, our study provides a solid framework for using deep neural network models in crime prediction, while highlighting the importance of automatic feature extraction in enhancing the accuracy of predictions. By reducing the impact of crime through early prediction, we can help to create a safer and more secure society.

Abstract Accurate fall detection for the assistance of older people is crucial to reduce incidents of deaths or injuries due to falls. Meanwhile, vision‐based fall detection system has shown some significant results to detect falls. Still, numerous challenges need to be resolved. The impact of deep learning has changed the landscape of the vision‐based system, such as action recognition. The deep learning technique has not been successfully implemented in vision‐based fall detection system due to the requirement of a large amount of computation power and requirement of a large amount of sample training data. This research aims to propose a vision‐based fall detection system that improves the accuracy of fall detection in some complex environments such as the change of light condition in the room. Also, this research aims to increase the performance of the pre‐processing of video images. The proposed system consists of Enhanced Dynamic Optical Flow technique that encodes the temporal data of optical flow videos by the method of rank pooling, which thereby improves the processing time of fall detection and improves the classification accuracy in dynamic lighting condition. The experimental results showed that the classification accuracy of the fall detection improved by around 3% and the processing time by 40–50 ms. The proposed system concentrates on decreasing the processing time of fall detection and improving the classification accuracy. Meanwhile, it provides a mechanism for summarizing a video into a single image by using dynamic optical flow technique, which helps to increase the performance of image preprocessing steps.