Risk Prediction System Based on Risk Prediction Model with Complex Event Processing: Risk Prediction in Real Time on Complex Event Processing Engine

2014 ◽  
Author(s):  
Yoon-Ki Kim ◽  
Chang-Sung Jeong
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Complex Event Processing ◽  
Risk Prediction Model ◽  
Event Processing ◽  
Prediction System ◽  
Processing Engine

A Real-time Risk-Prediction Model for Pediatric Venous Thromboembolic Events

PEDIATRICS ◽  
2021 ◽  
pp. e2020042325
Author(s):  
Shannon C. Walker ◽  
C. Buddy Creech ◽  
Henry J. Domenico ◽  
Benjamin French ◽  
Daniel W. Byrne ◽  
...  
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Thromboembolic Events ◽  
Venous Thromboembolic Events ◽  
Venous Thromboembolic

scholarly journals Application of a Rule-Based Approach in Real-Time Crash Risk Prediction Model Development Using Loop Detector Data

2015 ◽  
Vol 16 (8) ◽  
pp. 786-791 ◽  
Author(s):  
Ali Pirdavani ◽  
Ellen De Pauw ◽  
Tom Brijs ◽  
Stijn Daniels ◽  
Maarten Magis ◽  
...  
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Model Development ◽  
Risk Prediction Model ◽  
Crash Risk ◽  
Rule Based ◽  
Loop Detector ◽  
Rule Based Approach

scholarly journals Predicting Real-Time Crash Risk for Urban Expressways in China

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Miaomiao Liu ◽  
Yongsheng Chen
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Fuzzy Inference ◽  
Risk Prediction Model ◽  
Training Data ◽  
Sensor Data ◽  
Crash Risk ◽  
Inference System

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

Development of Neurobehavioral Deterioration Risk Prediction Model for Welder: A Proposed Study

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model
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