scholarly journals Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Meisam Ghasedi ◽  
Maryam Sarfjoo ◽  
Iraj Bargegol
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Logit Model ◽  
Predictive Power ◽  
Dominant Role ◽  
Learning Approaches ◽  
Lighting Condition ◽  
Rainy Weather ◽  
Pedestrian Accidents ◽  
Effective Principal

AbstractThe purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

scholarly journals Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258535
Author(s):  
Orion Weller ◽  
Luke Sagers ◽  
Carl Hanson ◽  
Michael Barnes ◽  
Quinn Snell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protective Factors ◽  
Predictive Power ◽  
Protective Factor ◽  
Peer Acceptance ◽  
Risk And Protective Factors ◽  
Learning Approaches ◽  
Suicidal Thoughts ◽  
And Behavior ◽  
Modern Machine

Introduction Addressing the problem of suicidal thoughts and behavior (STB) in adolescents requires understanding the associated risk factors. While previous research has identified individual risk and protective factors associated with many adolescent social morbidities, modern machine learning approaches can help identify risk and protective factors that interact (group) to provide predictive power for STB. This study aims to develop a prediction algorithm for STB among adolescents using the risk and protective factor framework and social determinants of health. Methods The sample population consisted of more than 179,000 high school students living in Utah and participating in the Communities That Care (CTC) Youth Survey from 2011-2017. The dataset includes responses to 300+ questions from the CTC and 8000+ demographic factors from the American Census Survey for a total of 1.2 billion values. Machine learning techniques were employed to extract the survey questions that were best able to predict answers indicative of STB, using recent work in interpretable machine learning. Results Analysis showed strong predictive power, with the ability to predict individuals with STB with 91% accuracy. After extracting the top ten questions that most affected model predictions, questions fell into four main categories: familial life, drug consumption, demographics, and peer acceptance at school. Conclusions Modern machine learning approaches provide new methods for understanding the interaction between root causes and outcomes, such as STB. The model developed in this study showed significant improvement in predictive accuracy compared to previous research. Results indicate that certain risk and protective factors, such as adolescents being threatened or harassed through digital media or bullied at school, and exposure or involvement in serious arguments and yelling at home are the leading predictors of STB and can help narrow and reaffirm priority prevention programming and areas of focused policymaking.

Online Detection of Combustion Instabilities Using Supervised Machine Learning

2020 ◽  
Author(s):  
Michael McCartney ◽  
Thomas Indlekofer ◽  
Wolfgang Polifke
Keyword(s):  
Machine Learning ◽  
Hurst Exponent ◽  
Predictive Power ◽  
Dynamic Pressure ◽  
Supervised Machine Learning ◽  
Ar Model ◽  
Fluctuation Analysis ◽  
Learning Approaches ◽  
Combustion Instabilities ◽  
Online Detection

Abstract In this paper we investigate how supervised machine learning can be leveraged to improve online prediction of thermoacoustic combustion instabilities using dynamic pressure readings. We discuss the current state of the art tools for the online detection of combustion instabilities, namely the Hurst exponent and Auto-Regressive model, the precursors they detect and how they go about doing so. We show that the generality of these tools comes at the cost of predictive power, which can be recovered using supervised machine learning. To demonstrate this we apply two different supervised machine learning approaches (using Hidden Markov Models and Automatic Machine Learning) to the classification of the state of a combustor given the dynamic pressure readings. We then observe the changes in predictive power when different information is added or removed from the signal. We find that the HMM based approach reduces to the AR model when the signal is normalised. We also find that the performance of a model trained on a signal transformed using the Detrended Fluctuation Analysis (DFA) can be met by a model trained on the Hurst exponent and the DFA transformation at a single (short) scale.

scholarly journals Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Vahid Najafi Moghaddam Gilani ◽  
Seyed Mohsen Hosseinian ◽  
Meisam Ghasedi ◽  
Mohammad Nikookar
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Logit Model ◽  
Dominant Role ◽  
Poor Quality ◽  
Urban Traffic ◽  
Multiple Logistic Regression ◽  
Artificial Neural Network Ann

Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R2 of 0.854 and an accuracy prediction power of 89.17%. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9%) than the logit model. In addition, the ANN model’s greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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