Developed Credit Card Fraud Detection Alert Systems via Notification of LINE Application

Chumuang, Narumol; Hiranchan, Sansanee; Ketcham, Mahasak; Yimyam, Worawut; Pramkeaw, Patiyuth; Tangwannawit, Sakchai

doi:10.1109/isai-nlp51646.2020.9376829

Cited by 24 publications

(1 citation statement)

References 10 publications

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“…Furthermore, after detecting violent attacks and attack activity using the proposed model in the edge device, we used commercial social media and integrated it into the edge device for sending an alert message to the mobile of the victim. The attack alert service provided by commercial social media apps allows for the delivery of notifications like image messages and sends notifications to social media app users [46] without using or creating full-fledged bots. In this study, we used and obtained the first commercial social media app notification access token and replaced the variable value of the 'token' along with the commercial social media app notification access token about which we provided information in the previous process step.…”

Section: Edge Computing Device and Attack Alerting Methodsmentioning

confidence: 99%

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Kumar,

Shih,

Guo

et al. 2024

Future Internet

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Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

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Section: Edge Computing Device and Attack Alerting Methodsmentioning

confidence: 99%