Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

It is difficult for lightweight neural networks to produce accurate 6DoF pose estimation effects due to their accuracy being affected by scale changes. To solve this problem, we propose a method with good performance and robustness based on previous research. The enhanced PVNet-based method uses depth-wise convolution to build a lightweight network. In addition, coordinate attention and atrous spatial pyramid pooling are used to ensure accuracy and robustness. This method effectively reduces the network size and computational complexity and is a lightweight 6DoF pose estimation method based on monocular RGB images. Experiments on public datasets and self-built datasets show that the average ADD(-S) estimation accuracy and 2D projection index of the improved method are improved. For datasets with large changes in object scale, the estimation accuracy of the average ADD(-S) is greatly improved.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

Online banking has become more popular and widespread. A huge number of customers open new bank accounts every day, which leads to a rise in fraudulent account openings. Many fraudulent activities related to this have been reported over time. It has remained a difficult task to detect fraudulent accounts efficiently. Artificial Intelligence (AI) is performing well in all the domains in the real world. So, the objective of this research is to build the most effective model to detect electronic financial transaction fraud in bank account opening. This research work aims to solve this problem (1) how to use deep reinforcement learning (DRL) for the detection of fraudulent bank accounts and (2) develop a Deep Q-Networks (DQN) based solution architecture for the detection of bank account opening fraud in an efficient way. Our proposed model achieved the highest fraud detection accuracy with 97% using the DQN algorithm with the benchmark Kaggle dataset for bank account fraud detection.

The visual analysis of trajectory topics is helpful for mining potential trajectory patterns, but the traditional visual analysis method ignores the evolution of the temporal coherence of the topic. In this paper, a novel visual analysis method for dynamic topic analysis of traffic trajectory is proposed, which is used to explore and analyze the traffic trajectory topic and evolution. Firstly, the spatial information is integrated into trajectory words, calculating the dynamic trajectory topic model based on dynamic analysis modeling and, consequently, correlating the evolution of the trajectory topic between adjacent time slices. Secondly, in the trajectory topic, a representative trajectory sequence is generated to overcome the problem of the trajectory topic model not considering the word order, based on the improved Markov Decision Process. Subsequently, a set of meaningful visual codes is designed to analyze the trajectory topic and its evolution through the parallel window visual model from a spatial-temporal perspective. Finally, a case evaluation shows that the proposed method is effective in analyzing potential trajectory movement patterns.

Analyzing the evolution of trajectory topics is fundamental to understanding urban mobility and human activity. Existing methods, however, often struggle to capture complex spatio-temporal semantics and are constrained by fixed time windows, which limits multi-scale temporal analysis. This paper presents a novel method to model the dynamic topics of trajectories to address these limitations. The proposed method combines a domain-specific trajectory embedding strategy, a flexible dynamic topic modeling pipeline, and an interactive visualization system to address these limitations. Firstly, the method introduces a novel embedding method that uses a retrained RoBERTa model with a word-level tokenizer on Morton-coded trajectories to effectively learn spatial context and sequential patterns. Secondly, a BERTopic-based approach is employed for topic modeling, featuring an adjustable time window that allows for flexible analysis of topic dynamics across different temporal scales without model retraining. Furthermore, an interactive visualization system with coordinated spatio-temporal views translates abstract model outputs into an intuitive format, enabling direct exploration of evolving trajectory topics. Experiments on a large-scale taxi trajectory dataset demonstrate the proposed method’s effectiveness in identifying coherent and meaningful patterns of dynamic trajectory topics.