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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Jitendra Choudhary
Medi-Caps University

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The synergistic effect of QR decomposition with t-SNE Mohsin Ali; Jitendra Choudhary
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1159-1169

Abstract

The study utilized non-parametric tests, specifically, the Mann-Whitney U test, to evaluate the performance of a proposed model called QRPCA-t-SNE, along with two other models, MDS and UMAP. The study compared these three models with two datasets on performance metrics such as model accuracy, training accuracy, testing accuracy, mean square error, AUC scores, precision, recall, and F1 scores. Once the model's performance was conducted, the Anderson-Darling test was to check for data normality before applying the hypothesis for model proof. The analysis revealed that Model 1 (QRPCA-t-SNE) significantly outperformed Model 2 (UMAP) and Model 3 (MDS) in terms of accuracy, with p-values of 0.0027 and 0.0003, respectively. This finding suggests that Model 1 (QRPCA-t-SNE) is suitable for high-accuracy and reliability applications, providing valuable insights into predictive analytics with a 95% confidence interval (confidence level α= 0.05).
Evaluating test case minimization with DB K-means Sanjay Sharma; Jitendra Choudhary
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp555-563

Abstract

This paper evaluates a new method for test case minimization using clustering methods. Clustering is a method used on data sets to generate clusters of the same behavior; thus, unnecessary and redundant data sets are removed. Hence, minimized data sets are generated that represent the same coverage as the original data sets. This is achieved by a new method based on clustering that separates data sets into two sets, outlier and non-outlier, after reducing redundant test cases, combines minimized data sets named DB K-means. The methods individually worked on outlier and non-outlier data sets and removed redundant data sets to minimize test cases. The result of the proposed method is better than the simple clustering method used for test case minimization. The software development would only be complete with software testing. Enhancing software quality requires testing numerous test cases, a laborious and time-consuming process, testing a program using a set of inputs known as test cases. Test case minimization approaches are critical in software testing, as they optimize testing resources and provide comprehensive coverage. Minimization is the process of choosing a subset of test cases that accurately captures the behavior of the entire test suite to minimize duplicacy and increase efficiency.
Co-Authors Mohsin Ali Sanjay Sharma