A combination of Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and LSTM (ConvLSTM) is constructed in this work for the fault diagnosis post-accident prediction Loss Coolant Accidents (LOCAs) Nuclear Power Plants (NPPs). The advantages ConvLSTM, such as effective feature determination extraction, are applied to classification LOCA cases. accuracy enhanced via collaborative C...

Many victims in traffic accidents do not receive optimal care due to the fact that the severity of their injuries is not realized early on. Triage protocols are based on physiological and anatomical criteria and subsequently on mechanisms of injury in order to reduce undertriage. In this study the value of accident characteristics for field triage is evaluated by developing an on scene injury s...

The prediction of traffic accidents is one of most important issues in our life. In the prediction of traffic accidents, a GIS platform to extract the important features including day, temperature, humidity, weather conditions, and month of occurred traffic accidents has been used. In this study, a decision making system (DMS) based on correlation-based feature selection and classifier algorith...

background: predicting the methane percentage of biogas is necessary for selecting the optimized technologies of using landfill biogas for energy. the aim of this study was to predict of methane fraction in biogas from landfill bioreactors by artificial neural network (ann) modeling. methods: in this study, two different systems were applied to predict the methane fraction in landfill gas as a ...

In this paper a methodology for the development of multiple impact models for road networks is described. Road impacts can be categorized into impacts for different stakeholder groups, namely the public, the owner and the road users. The current investigations address multiple impacts only for road users, but the developed methodology can be extended to estimate the impacts of all stakeholder g...