Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

Mason, Cindi R.; Twomey, Janet M.; Wright, David W.; Whitman, Lawrence E.

doi:10.1007/s11162-017-9473-z

Cited by 58 publications

(44 citation statements)

References 41 publications

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“…For experimental simulation, PyCharm software was employed on PC with 3.2 GHz with i5 processor. In order to estimate the efficiency of proposed RFBT-RF algorithm, the performance of the proposed method was compared with the LR based FS methods [20]. In experimental analysis, three databases were used.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, compare to the LR method, Table 3. Comparative study of proposed and existing work of student performance prediction in academic area Methodologies Database Precision (%) Recall (%) Accuracy (%) Probabilistic neural network [20] Collected for 682 first-year freshman students at a case study public urban university from 2010 to 2011…”

Section: Analysis Of Existing Uci and Proposed Database Performancementioning

confidence: 99%

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Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

Deepika*¹,

Sathyanarayana²

2019

IJIES

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Now-a-days, research in educational mining focuses on modelling student's performance. Many universities include large volumes of data related to student's details, performance, management details, educational process, and etc. Moreover, most of the data remains unused because inability of the university administration to handle it, also huge volumes of data are difficult to perform. In this paper, hybrid Feature Selection (FS) method namely Relief-F and Budget Tree-Random Forest (RFBT-RF) is proposed for selecting active features to reduce high dimensionality and handle uncertainty of data. The proposed feature selection method selects only relevant features instead of selecting redundant and irrelevant features for the classifiers. Also, RFBT-RF method is applied on multiple classifiers like Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) for predicting the Student Academic Performance (SAP). RFBT-RF method was applied on three databases such as UCI (maths), UCI (Portuguese) and Collected dataset. Results showed that, RFBT-RF algorithm achieved 6.85% of improved SAP accuracy compare to the existing Logistic Regression (LR) model.

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Section: Resultsmentioning

confidence: 99%

Section: Analysis Of Existing Uci and Proposed Database Performancementioning

confidence: 99%

Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

Deepika*¹,

Sathyanarayana²

2019

IJIES

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“…The relationship between given model's sensitivity and specificity is illustrated by the ROC curve [26,27]. It is a set of values of sensitivity and specificity calculated for each possible cut-off point, marked in the coordinate system, in which (1-specificity) is represented on the axis of abscissas, while sensitivity on the axis of ordinates.…”

Section: Logistic Regression Model Diagnosticsmentioning

confidence: 99%

Logistic regression in modeling and assessment of transport services

Borucka

2020

Open Engineering

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AbstractTransport companies operate in a dynamically developing and competitive market. Maintaining the already held position and further expansion requires adjusting the level of services provided to the needs and requirements of customers, as well as continuous surveying, monitoring and adjusting the implemented strategy. There are different methods for such an analysis. This article proposes logistic regression. The research was conducted on the basis of a distribution and trade company dealing with the supply of automotive spare parts. As the most profitable group of customers are local car repair shops, it was this group that was subject to analysis.The quality of service assessment was considered from the point of view of delivery time. The dichotomous form of the predictor taking two values - late and on-time delivery - was determined. From among the possible ones, regressors whose influence was statistically significant and whose modification was possible were selected. The research showed which of them (and how strongly) affect the dependent variable, which allowed for modification of strategy and implementation of new solutions increasing the number of satisfied customers.

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“…As a major advantage, PNN does not require expensive computational costs because of the fast and straightforward training procedure when compared with ANN. The performance of PNN is assured by the Parzen nonparametric estimator and the Bayes decision rule, which lessen the predicted risk of misclassification [51,52].…”

Section: Predicting Reliability Of the Obtained Optimal Designmentioning

confidence: 99%

Integration of Dimension Reduction and Uncertainty Quantification in Designing Stretchable Strain Gauge Sensor

et al. 2020

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Interests in strain gauge sensors employing stretchable patch antenna have escalated in the area of structural health monitoring, because the malleable sensor is sensitive to capturing strain variation in any shape of structure. However, owing to the narrow frequency bandwidth of the patch antenna, the operation quality of the strain sensor is not often assured under structural deformation, which creates unpredictable frequency shifts. Geometric properties of the stretchable antenna also severely regulate the performance of the sensor. Especially rugged substrate created by printing procedure and manual fabrication derives multivariate design variables. Such design variables intensify the computational burden and uncertainties that impede reliable analysis of the strain sensor. In this research, therefore, a framework is proposed not only to comprehensively capture the sensor’s geometric design variables, but also to effectively reduce the multivariate dimensions. The geometric uncertainties are characterized based on the measurements from real specimens and a Gaussian copula is used to represent them with the correlations. A dimension reduction process with a clear decision criterion by entropy-based correlation coefficient dwindles uncertainties that inhibit precise system reliability assessment. After handling the uncertainties, an artificial neural network-based surrogate model predicts the system responses, and a probabilistic neural network derives a precise estimation of the variability of complicated system behavior. To elicit better performance of the stretchable antenna-based strain sensor, a shape optimization process is then executed by developing an optimal design of the strain sensor, which can resolve the issue of the frequency shift in the narrow bandwidth. Compared with the conventional rigid antenna-based strain sensors, the proposed design brings flexible shape adjustment that enables the resonance frequency to be maintained in reliable frequency bandwidth and antenna performance to be maximized under deformation. Hence, the efficacy of the proposed design framework that employs uncertainty characterization, dimension reduction, and machine learning-based behavior prediction is epitomized by the stretchable antenna-based strain sensor.

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Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression

Cited by 58 publications

References 41 publications

Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction

Logistic regression in modeling and assessment of transport services

Integration of Dimension Reduction and Uncertainty Quantification in Designing Stretchable Strain Gauge Sensor

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