scholarly journals Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques and Artificial Neural Network: A Modeling Comparison

2021 ◽  
Vol 13 (10) ◽  
pp. 5670
Author(s):  
Gholamreza Shiran ◽  
Reza Imaninasab ◽  
Razieh Khayamim
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Decision Tree ◽  
Multinomial Logistic Regression ◽  
Crash Severity ◽  
Traffic Crash ◽  
Road Crash ◽  
Mlr Model

The classification of vehicular crashes based on their severity is crucial since not all of them have the same financial and injury values. In addition, avoiding crashes by identifying their influential factors is possible via accurate prediction modeling. In crash severity analysis, accurate and time-saving prediction models are necessary for classifying crashes based on their severity. Moreover, statistical models are incapable of identifying the potential severity of crashes regarding influencing factors incorporated in models. Unlike previous research efforts, which focused on the limited class of crash severity, including property damage only (PDO), fatality, and injury by applying data mining models, the present study sought to predict crash frequency according to five severity levels of PDO, fatality, severe injury, other visible injuries, and complaint of pain. The multinomial logistic regression (MLR) model and data mining approaches, including artificial neural network-multilayer perceptron (ANN-MLP) and two decision tree techniques, (i.e., Chi-square automatic interaction detector (CHAID) and C5.0) are utilized based on traffic crash records for State Highways in California, USA. The comparison of the findings of the relative importance of ten qualitative and ten quantitative independent variables incorporated in CHAID and C5.0 indicated that the cause of the crash (X1) and the number of vehicles (X5) were known as the most influential variables involved in the crash. However, the cause of the crash (X1) and weather (X2) were identified as the most contributing variables by the ANN-MLP model. In addition, the MLR model showed that the driver’s age (X11) accounts for a larger proportion of traffic crash severity. Therefore, the sensitivity analysis demonstrated that C5.0 had the best performance for predicting road crash severity. Not only did C5.0 take a shorter time (0.05 s) compared to CHAID, MLP, and MLR, it also represented the highest accuracy rate for the training set. The overall prediction accuracy based on the training data was approximately 88.09% compared to 77.21 and 70.21% for CHAID and MLP models. In general, the findings of this study revealed that C5.0 can be a promising tool for predicting road crash severity.

scholarly journals RETRACTED: Analysis and prediction of β-turn types using multinomial logistic regression and artificial neural network

2007 ◽  
Vol 35 (12) ◽  
pp. e8-e15
Author(s):  
Poursheikhali Asgary Mehdi ◽  
Abdolmaleki Parviz ◽  
Kazemnejad Anoshirvan ◽  
Jahandidehs Samad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Artificial Neural

The Validity of Multinomial Logistic Regression and Artificial Neural Network in Predicting Sukuk Rating: Evidence from Indonesian Stock Exchange

2020 ◽  
Vol 23 (04) ◽  
pp. 2050032
Author(s):  
Muhammad Luqman Nurhakim ◽  
Zainul Kisman ◽  
Faizah Syihab
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Stock Exchange ◽  
Multinomial Logistic Regression ◽  
Global Market ◽  
Analysis Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The Sukuk (shariah bond) market is developing in Indonesia and potentially will capture the global market in the future. It is an attractive investment product and a hot current issue in the capital market. Especially, the problem of predicting an accurate and trustworthy rating. As the Sukuk market developed, the issue of Sukuk rating emerged. As ordinary investors will have difficulty predicting their ratings going forward, this research will provide solutions to the problems above. The objective of this study is to determine the Indonesian Sukuk rating determinants and comparing the Sukuk rating predictive model. This research uses Artificial Neural Network (ANN) and Multinomial Logistic Regression (MLR) as the predictive analysis model. Data in this study are collected by purposive sampling and employing Sukuk rated by PEFINDO, an Indonesian rating agency. Findings in this study are debt, profitability and firm size significantly affecting Sukuk rating category and the ANN performs better predictive accuracy than MLR. The implications of the results of the research for the issuer and bondholder are a higher level of credit enhancement, a higher level of profitability, and the bigger size of firm rewarding higher Sukuk rating.

Data mining of digital phytosanitary

2021 ◽  
Author(s):  
T. Z. Ibragimov ◽  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Bayesian Classifier ◽  
Design Parameters ◽  
Data Set ◽  
Septoria Leaf Blotch ◽  
Leaf Blotch ◽  
Artificial Neural

methods of data mining were used to predict the Septoria leaf blotch of wheat. A system has been developed that allows parallel forecasting with the same data set using the methods of an artificial neural network, a decision tree, and a naive Bayesian classifier. The system allows you to interactively adjust the design parameters for each of the methods, see the results obtained and evaluate their effectiveness.

scholarly journals Analysis and identification of -turn types using multinomial logistic regression and artificial neural network

2007 ◽  
Vol 23 (23) ◽  
pp. 3125-3130 ◽  
Author(s):  
M. Poursheikhali Asgary ◽  
S. Jahandideh ◽  
P. Abdolmaleki ◽  
A. Kazemnejad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Artificial Neural

scholarly journals Predicting Breast Cancer Survivability

2020 ◽  
Vol 4 (1) ◽  
pp. 17-30
Author(s):  
Omead I. Hussain
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Decision Trees ◽  
Modeling Tools ◽  
Data Mining Techniques ◽  
Artificial Neural ◽  
Using Data

this study concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods: two from machine learning (artificial neural network and decision trees) and one from statistics (logistic regression), and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 87 variables and the total of the records are 457,389; which became 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we find the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2 which is in our view of point is the suitable system to use according to the facilities and the results given to us. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. However, we have found out that the neural network has a much better performance than the other two techniques. Finally, we can say that the model we chose has the highest accuracy which specialists in the breast cancer field can use and depend on.

scholarly journals Application of Data Mining Algorithms for Dementia in People with HIV/AIDS

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Luana Ibiapina Cordeiro Calíope Pinheiro ◽  
Maria Lúcia Duarte Pereira ◽  
Marcial Porto Fernandez ◽  
Francisco Mardônio Vieira Filho ◽  
Wilson Jorge Correia Pinto de Abreu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Hiv Aids

Dementia interferes with the individual’s motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.

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