Correlation research of phase angle variation and coating performance by means of Pearson’s correlation coefficient

Fu, Ting; Tang, Xu; Cai, Zekai; Zuo, Yu; Tang, Yuming; Zhao, Xuhui

doi:10.1016/j.porgcoat.2019.105459

Cited by 55 publications

(33 citation statements)

References 21 publications

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“…Pearson r correlation was the most widely used correlation statistic to measure linearly related variables' relationship. Pearson r correlation measured the degree of relationship between the two attributes [40].…”

Section: Methodsmentioning

confidence: 99%

Predicting Student Program Completion Using Naïve Bayes Classification Algorithm

Perez¹,

Perez²

2021

IJMECS

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Data mining approaches provide different educational institutions opportunities to find hidden patterns from the data stored in the database. Many researchers have used these data to develop a model that would assist the institution administrators in decision-making. This study was performed to predict student program completion using the Naï ve Bayes classifier technique. The dataset utilized in this study was obtained from Bulacan State University -Sarmiento Campus in the Philippines under BS Information Technology program from five-year graduates' data for Academic Year 2012-2016. This dataset was pre-processed, cleansed, transformed, and balanced before constructing the model. Ten predictors were used for predicting student completion. The feature selection technique was used to filter and evaluate the significance of each factor. The significant variables assessed by the feature selection technique (Weight by Correlation) were the final parameters in creating the model. The Naï ve Bayes classifier was applied to predict the students' completion using the 70:30 ratios for training and testing dataset distribution. Correlation analysis identified the weight of individual attributes to the label attribute. From 10 possible predictor variables, only four (4) predictor variables were selected after correlation analysis. The identified significant attributes affecting program completion, namely (in order of significance): parents' monthly income, mother and father's educational attainment, and High School GPA attributes. The significant attributes identified in correlation analysis splitted into 70% training data or 447 records and 30% testing data or 191 records. There were 84 out of 191 data samples, or 44% of students were predicted to complete the program. On the other hand, 107 out of 191 data samples, or 56%, were predicted as not completing the program. The accuracy values performed an 84% rating with 80.46% class precision, and 83.33% class recall in the testing dataset (n=191). The outcomes of this study have a significant impact on HEIs, particularly on college completion rates. This study shall be highly significant and beneficial specifically to university administrators as this be a tool for them to identify students who will complete college based on variables included in the model.

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Section: Methodsmentioning

confidence: 99%

Predicting Student Program Completion Using Naïve Bayes Classification Algorithm

Perez¹,

Perez²

2021

IJMECS

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“…The obtained results are used for comparative analyses to check the proposed model's efficiency, accuracy, and precision. A correlation check between the estimated and real values with two coefficients (Pearson's and Spirman's) (Fu et al, 2020;Zhang et al, 2020) was used. This can show the connection between the set of input sizes with the size of the system, which represents the effort invested in the successful implementation of software projects.…”

Section: Our Approach -Applied Methodologymentioning

confidence: 99%

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

Ranković

Ivanović

et al. 2021

Connection Science

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This paper proposes a new, improved COmmon Software Measurement International Consortium function point (COSMIC FP) method that uses Artificial Neural Network (ANN) architectures based on Taguchi's Orthogonal Array to estimate software development effort. The minimum magnitude relative error (MRE) to evaluate these architectures considering the cost effect function, the type of data used in the training, testing, and validation of the proposed models, was used. Applying the fuzzification and clustering method to obtain seven different datasets, we would like to achieve excellent reliability and accuracy of the obtained results. Besides examining the influence of four input values, we aim to reduce the risks of potential errors, increase the coverage of a wide range of different projects and increase the efficiency and success of completing many various software projects. The main contributions of our work are as follows: the influence of four input values of the COSMIC FP method on the change of mean (MRE), development of two simple ANN architectures, the attainment of a small number of performed iterations in software effort estimation (less than 7), reduced software effort estimation time, the use of different values of the International Software Benchmarking Standards Group and other datasets used in the experiment.

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“…By examining correlation coefficients, such as Spearman’s rho and Pearson’s coefficient [ 50 , 51 ], we will determine whether the agreement between the actual and estimated value is good, i.e., whether our approach is reliable and efficient, using Formulas (40)–(43):

PRED(k) = count(MRE) < 25% PRED(k) = count(MRE) < 30% PRED(k) = count(MRE) < 50%, where k = 25, 30 and 50, respectively …”

Section: Proposed Approachmentioning

confidence: 99%

“…By examining correlation coefficients, such as Spearman's rho and Pearson's coefficient [50,51], we will determine whether the agreement between the actual and estimated value is good, i.e., whether our approach is reliable and efficient, using Formulas ( 40)-( 43):…”

mentioning

confidence: 99%

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Ranković

Ivanović

et al. 2021

Entropy

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Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

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Correlation research of phase angle variation and coating performance by means of Pearson’s correlation coefficient

Cited by 55 publications

References 21 publications

Predicting Student Program Completion Using Naïve Bayes Classification Algorithm

Predicting Student Program Completion Using Naïve Bayes Classification Algorithm

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

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