Knowledge Management in Road Accident Detection Based on Developed Deep Learning - Bvss Subbarao; Ramana K (Advances in Engineering and Intelligence Systems)
Business organizations and the research community try to precisely detect occurrences and assist in the case of a disaster. Most development systems are hardware-based, making them pricey and unavailable in every vehicle. A vehicle's sensors can be destroyed in various ways, including through minor accidents or fixed interactions. In some instances, the sensors are incapable of detecting an accident. Intelligent phone sensors are a great alternative because of their dependability and availability. Smartphone sensors can detect collisions. Few methods detect failures using cell phones. These systems, however, have a low error rate. The study proposes an Internet of Things-based system built on low-cost devices. The suggested system has two stages: identification and reporting of accidents. These systems rely on sensors to detect mobile phone failures. The suggested system employs a variety of smartphone sensors. The study involves creating a smartphone application that continually reads sensor data and sends it to the cloud for further processing. The crash was discovered by threshold analysis. The critical contribution of this research is creating a scheme that alerts nearby hospitals and ambulances when an accident occurs. The system will have more minor inaccuracies, precisely identify accidents, and perform better than earlier techniques using four sensory inputs. This paper introduces novel types of deep learning for accident detection.