scholarly journals Academic Failure at University and Data Processing Methods Based on Decision Trees and Neural Networks: Research Methodology

2021 ◽  
Vol 07 (10) ◽  
Author(s):  
Smail ADMEUR ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Research Methodology ◽  
Academic Failure ◽  
First Year ◽  
First Year Students ◽  
Research Article ◽  
Long Time ◽  
Data Processing Methods

For a long time, academic failure among university students sparked heated controversy. Many educational psychologists try to figure it out and then explain it. Statisticians have tried to predict it. Our research (article) aims to classify students into several categories, as well as to use the decision tree and artificial neural networks to classify first-year students and identify variables that may explain the problem.

scholarly journals Comparison of methods on a model for predicting university success

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
Jean-Philippe Vandamme
Keyword(s):  
Academic Failure ◽  
Theoretical Models ◽  
Correct Prediction ◽  
First Year ◽  
First Year Students ◽  
Long Time ◽  
Diverse Data ◽  
University Success ◽  
Extract Information

For a long time, academic failure in the first year of university has fueled many debates. Many educational psychologists have tried to understand it and then explain it. Many statisticians have tried to predict it. Our research aims to establish a model making it possible to determine, as early as possible in the year, the group of first-year students on whom priority must be given to the educational resources available to improve the success rate. For this, we have transposed in the form of a questionnaire the hypotheses posed in many theoretical models. Then, after having collected sufficient and diverse data via this questionnaire, the objective was to extract information via statistical methods or data mining and thus allow the classification of students into three classes as homogeneous as possible. This article describes the methodology adopted, the variables that were analyzed and the methods that were used and compared. With the parallelization of the results provided by the various methods (discriminant analyzes, regressions, approximate sets, decision trees, etc.), it is possible to highlight their differences in performance. Indeed, some methods have been shown to be more effective in terms of correct prediction rates made, while others have been particularly interesting for their ability to highlight the predictors of university success.

scholarly journals Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aref M. al-Swaidani ◽  
Waed T. Khwies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Supplementary Cementitious Material ◽  
Natural Pozzolans ◽  
Long Time ◽  
Ann Models ◽  
Artificial Neural ◽  
And Performance ◽  
And Training

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

The Use of Quantitative Methods in Investment Decisions

2019 ◽  
pp. 256-275 ◽  
Author(s):  
Serkan Eti
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Quantitative Methods ◽  
Simulation Analysis ◽  
Investment Decision ◽  
Tree Algorithms ◽  
Artificial Neural

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

Underwriting Automobile Insurance Using Artificial Neural Networks

2011 ◽  
pp. 3865-3870
Author(s):  
Fred Kitchens
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Automobile Insurance ◽  
Long Time ◽  
Artificial Neural ◽  
Pencil And Paper

For hundreds of years, actuaries used pencil and paper to perform their statistical analysis It was a long time before they had the help of a mechanical adding machine. Only recently have they had the benefit of computers. As recently as 1981, computers were not considered important to the process of insurance underwriting. Leading experts in insurance underwriting believed that the judgment factor involved in the underwriting process was too complex for any computer to handle as effectively as a human underwriter (Holtom, 1981). Recent research in the application of technology to the underwriting process has shown that Holtom’s statement may no longer hold true (Gaunt, 1972; Kitchens, 2000; Rose, 1986). The time for computers to take on an important role in the insurance underwriting process may be upon us. The author intends to illustrate the applicability of artificial neural networks to the insurance underwriting process.

Artificial neural networks and decision tree model analysis of liver cancer proteomes

2007 ◽  
Vol 361 (1) ◽  
pp. 68-73 ◽  
Author(s):  
John M. Luk ◽  
Brian Y. Lam ◽  
Nikki P.Y. Lee ◽  
David W. Ho ◽  
Pak C. Sham ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Liver Cancer ◽  
Decision Tree ◽  
Model Analysis ◽  
Decision Tree Model ◽  
Tree Model ◽  
Artificial Neural

By Measure: Creativity in Design

2007 ◽  
Vol 21 (5) ◽  
pp. 353-359 ◽  
Author(s):  
Brad Hokanson
Keyword(s):  
Problem Solving ◽  
Empirical Research ◽  
Research Methodology ◽  
Creative Thinking ◽  
First Year ◽  
Specific Training ◽  
Creative Problem Solving ◽  
First Year Students ◽  
Design Students ◽  
Creative Problem

Specific training may be required to develop creativity in design students. At the very least, training is valuable in developing creativity in first-year students. Creativity is a skill that can be examined, used and taught - and it is one that is central to designing. This paper presents the results of empirical research from a class in creative problem solving for design students. The nature of creativity and the structure of the class are described, and this is followed by an outline of the research methodology and the use of the verbal Torrance Test of Creative Thinking. Creativity, as measured through the test, significantly increased.

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