Student Performance Score Prediction Using Artificial Neural Network with the Support of Exploratory Factor Analysis and Clustering

2020 ◽  
Author(s):  
Selcuk Ogutcu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Student Performance ◽  
Exploratory Factor Analysis ◽  
Performance Score ◽  
Artificial Neural

Hybrid modelling of the consumption of organic foods in Iran using exploratory factor analysis and an artificial neural network

2018 ◽  
Vol 120 (1) ◽  
pp. 44-58 ◽  
Author(s):  
Yaser Sobhanifard
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Food Consumption ◽  
Organic Food ◽  
Organic Foods ◽  
Content Type ◽  
Artificial Neural ◽  
Organic Food Consumption

Purpose The purpose of this paper is to explore a hybrid model of the consumption of organic foods, combining the use of exploratory factor analysis (EFA) and an artificial neural network (ANN). Design/methodology/approach The study has three phases. In the first phase, the Delphi method is employed, and 15 motives for the consumption of organic food are identified; these motives are used to develop the model in the second phase. Finally, in the last phase, an ANN is used to rank the motives to determine their priority. Findings The EFA model explored includes four factors that have a positive effect on the level of organic food consumption. These are naturalness, trust, sanitariness and marketing. Results from the use of an ANN indicate that the main variables in organic food consumption are claims, psychological variables and doubt. From the results of the EFA model it is clear these three variables are components of the factor of trust. Practical implications Marketers can use the model developed in this paper to satisfy the needs of their customers and hence enhance their market share and profitability. This study shows that improvements in truth in the claims made for organic products, perceived security from using these products and doubts about the safety of other foods can lead marketers to their goal. Informative advertisements can inculcate trust and naturalness among consumers as main factors. Originality/value The main contribution of this study is the light it sheds on how consumers think about organic foods. It develops a model incorporating motives for consuming organic food and determining the priorities held by consumers of organic foods.

scholarly journals Analyzing student performance using evolutionary artificial neural network algorithm

2018 ◽  
Vol 7 (2.26) ◽  
pp. 67 ◽  
Author(s):  
A S. Arunachalam ◽  
T Velmurugan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Student Performance ◽  
Fitness Function ◽  
Research Work ◽  
Function Evaluation ◽  
Success Factor ◽  
Optimization Approach ◽  
Artificial Neural

Educational Data Mining (EDM) and Learning Systematic (LS) research have appeared as motivating areas of research, which are clarifying beneficial understanding from educational databases for many purposes such as predicting student’s success factor. The ability to predict a student’s performance can be beneficial in modern educational systems. This research work aims at developing an evolutionary approach based on genetic algorithm and the artificial neural network. The traditional artificial neural network lacks predicting student performance due to the poor modeling structure and the capability of assigning proper weights to each node under the hidden layer. This problem is overwhelmed with the aid of genetic algorithm optimization approach which produces appropriate fitness function evaluation in each iteration of the learning process. The performances gradually increase the accuracy of the prediction and classification more precisely.

Application of Factor Analysis and Artificial Neural Network in Electron Spectroscopic Depth Profiling

1996 ◽  
Vol 2 (1) ◽  
pp. 35-41
Author(s):  
Henning Bubert ◽  
Tamara Niebuhr
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Depth Profiling ◽  
Data Sets ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Actual Problem ◽  
Artificial Neural ◽  
Electron Spectrometry

Depth profiling has been performed by using Auger electron spectrometry and X-ray photoelectron spectrometry in combination with Ar-ion sputtering. The data obtained by both surface-analytical methods have been evaluated by means of factor analysis and partly by applying artificial neural network in order to determine the compositional layering of different thin-films such as TiNx on Ti, Cr2O3/CrN sandwich layer, and copper oxide on Cu. Both multivariate statistical methods applied to the same data sets lead to results that agree well within statistical deviations provided that the structure of the artificial neural network is constructed appropriate to the actual problem.

scholarly journals Artificial neural network ensemble modeling with exploratory factor analysis for streamflow forecasting

2015 ◽  
Vol 17 (4) ◽  
pp. 614-639 ◽  
Author(s):  
Sung Eun Kim ◽  
Il Won Seo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Ensemble Model ◽  
Ensemble Modeling ◽  
Ann Model ◽  
Streamflow Forecasting ◽  
Initial Weight ◽  
Input Variables

An artificial neural network (ANN) is a powerful data-driven modeling tool. The selection of the input variable is an important task in the development of an ANN model. However, at present in ANN modeling, the input variables are usually determined by trial and error methods. Further, the ANN modeler usually selects a single ‘good’ result, and accepts it as the final result without detailed explanation of the initial weight parameter. In this way, the ANN model cannot guarantee that the model will produce the optimal result for later predictions. In this study, the ANN ensemble model with exploratory factor analysis (EFA) was developed and applied to three stations in the Nakdong River, Korea for the 1-day ahead streamflow forecasting. EFA was used to select the input variables of the ANN model, and then the ensemble modeling was applied to estimate the performance of the ANN to remove the influence of initial weight parameters on the model results. In the result, the ANN ensemble model with the input variables proposed by EFA produced more accurate and reliable forecasts than other models with several combinations of input variables. Nash–Sutcliffe efficiency (NSE) results in the validation were 0.92, 0.95, and 0.97, respectively.

scholarly journals Prediction of University Student Performance Based on Tracer Study Dataset Using Artificial Neural Network

2021 ◽  
Vol 5 (2) ◽  
pp. 72-82
Author(s):  
Zahrina Aulia Adriani ◽  
Irma Palupi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Standard Deviation ◽  
Student Performance ◽  
Cross Validation ◽  
Training Data ◽  
Tracer Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Fold Cross Validation

In order to increase student performance, several universities use machine learning to analyze and evaluate their data so that it enables to improve the quality of education in the university. To get a new insight from the tracer study dataset as the relevance between university performance and student capability with business and industries work, the author will develop a model to predict student performance based on the tracer study dataset using Artificial Neural Network (ANN). For obtaining attributes that correspond to labels, Phi Coefficient Correlation will be used to select the attributes with high correlation as Feature Selection. The author is also performing the oversampling method using Synthetic Minority Oversampling Technique (SMOTE) because this dataset is imbalanced and evaluates the model using K-Fold Cross-Validation. According to K-Fold Cross Validation, the result shows that K = 3 has a low standard deviation of evaluation score and it's the best candidate of K to split the dataset. The average standard deviation is 0.038 for all score evaluations (Accuracy, Precision, Recall, and F-1 Score). After applied SMOTE to treating the imbalanced dataset with the data splitting 65 training data and 35 testing data, the accuracy value increases by 10% from 0.77 to 0.87.

An Integrated Predicting Model of New-Built Regional Logistics Center’s Demand Based on the Artificial Neural Network

2012 ◽  
Vol 253-255 ◽  
pp. 1512-1517
Author(s):  
Jian Feng Luo ◽  
Tian Shan Ma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Back Propagation ◽  
Demand Forecast ◽  
Logistics Center ◽  
Bp Artificial Neural Network ◽  
Regional Logistics ◽  
Artificial Neural ◽  
Logistics Demand

In order to predict the scale of logistics demand for a new-built regional center, economic indicators and the other related measuring indicator of the scale for logistics demand is studied. The factor analysis and back propagation (BP) artificial neural network theory are applied to set up a model for predicting the scale of the logistics center’ s demand. The factor analysis is applied to this model to reduce the number of indicators of the input layer in the BP artificial neural network, and to reduce complexity. Then model is introduced to fit historical data of the scale of new –built a regional logistics center’ s demand .Finally,a third-layer BP artificial neural network is constructed. This model was applied to predict the scale of the logistics demand in an example and the forecasting result shows that forecasting accuracy of the model is good. It also provides a new way of a new-built regional logistics center’ s demand forecast.

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