Car Accident Severity Classification Using Machine Learning

2021 ◽  
Author(s):  
Abdulrahman Atwah ◽  
Amjed Al-Mousa
Keyword(s):  
Machine Learning ◽  
Severity Classification ◽  
Accident Severity ◽  
Car Accident

Machine learning based accident prediction in secure IoT enable transportation system

2021 ◽  
pp. 1-13 ◽  
Author(s):  
Bhabendu Kumar Mohanta ◽  
Debasish Jena ◽  
Niva Mohapatra ◽  
Somula Ramasubbareddy ◽  
Bharat S. Rawal
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Information Exchange ◽  
Secure Communication ◽  
Intelligent Transportation System ◽  
Transportation System ◽  
Information And Communications Technology ◽  
Classification Models ◽  
Architecture Model ◽  
Accident Severity

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

scholarly journals Machine learning-based approach for disease severity classification of carpal tunnel syndrome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dougho Park ◽  
Byung Hee Kim ◽  
Sang-Eok Lee ◽  
Dong Young Kim ◽  
Mansu Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Carpal Tunnel Syndrome ◽  
Carpal Tunnel ◽  
Rating Scale ◽  
External Validation ◽  
Gradient Boosting ◽  
Severity Classification ◽  
Extreme Gradient Boosting ◽  
Tunnel Syndrome ◽  
Multi Class Classification

AbstractIdentifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.

U.K. Car Accident and Insurance Predictor using Machine Learning

2021 ◽  
Author(s):  
Ayush Patel ◽  
Alankar Uniyal ◽  
Ritesh Dhanare
Keyword(s):  
Machine Learning ◽  
Car Accident

scholarly journals An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3155
Author(s):  
Olivia Vargas-Lopez ◽  
Carlos A. Perez-Ramirez ◽  
Martin Valtierra-Rodriguez ◽  
Jesus J. Yanez-Borjas ◽  
Juan P. Amezquita-Sanchez
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Learning Algorithm ◽  
Support Vector ◽  
Training Strategy ◽  
Machine Learning Approach ◽  
Personal Consequences ◽  
Statistical Time ◽  
Detection Of Stress ◽  
Car Accident

The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.

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