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Journal articles on the topic 'Validation techniques'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Anderson, Will. "Software Validation Techniques." Drug Information Journal 21, no. 4 (1987): 461–69. http://dx.doi.org/10.1177/009286158702100413.

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2

Estrade, Jean-Louis. "Validation des techniques myotensives." Kinésithérapie, la Revue 9, no. 95 (2009): 14. http://dx.doi.org/10.1016/s1779-0123(09)70051-8.

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3

Shah, Dhwanir, and Lokesh Kumar Sharma. "Contrastive Study of Machine Learning Techniques for Credit Card Fraud Detection." Indian Journal Of Science And Technology 18, no. 16 (2025): 1248–59. https://doi.org/10.17485/ijst/v18i16.572.

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Objective: To assess the efficacy of five machine learning algorithms—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and XGBoost—in detecting credit card fraud, utilizing a simulated Kaggle dataset created through Sparkov for credit card transactions. Methods: The dataset was partitioned into three training-test ratios: 60%:40%, 70%:30%, and 80%:20%. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was employed. The performance of the models was measured using Accuracy, Precision, Recall, F1-score, and ROC-AUC, with validation

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4

Usov, A. I., G. G. Omel’yanyuk, G. I. Bebeshko, I. P. Lyubetskaya, and I. B. Afanas’ev. "Methodological Features of Validating Forensic Expert Techniques." Theory and Practice of Forensic Science 18, no. 1 (2023): 76–96. http://dx.doi.org/10.30764/1819-2785-2023-1-76-96.

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The article reviews and summarizes the experience of validating forensic expert techniques in the Russian Federal Centre of Forensic Science of the Ministry of Justice of the Russian Federation. The authors point out the methodological features of practical implementation of the validation procedure. They demonstrate that the specificity, diversity, and complexity of the objects of expert study require the classification of the applied methods in terms of metrology, identification of the main validation parameters of quantitative and qualitative methods, organization of experiments, and evalua

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5

Amandeep, Kaur*and Monika Gupta. "DEVELOPMENT AND VALIDATION OF METHOD BY HPLC TECHNIQUES." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 07 (2018): 7057–65. https://doi.org/10.5281/zenodo.1325897.

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<em>The primary focus of the review article is on general approaches and considerations toward development of chromatographic methods for separation, identification, and quantification of compounds, which may be applied within the various functions in the drug development continuum. This article also discusses the issues and parameters that must be considered in the validation of analytical methods. At the end of the review, a scope of the present research study is covered.</em> <strong>Keywords: </strong><em>Introduction, Method development, Steps for HPLC method development, Method validatio

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6

Kirwan, Barry. "Validation of human reliability assessment techniques: Part 1 — Validation issues." Safety Science 27, no. 1 (1997): 25–41. http://dx.doi.org/10.1016/s0925-7535(97)00049-0.

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7

Kirwan, Barry. "Validation of human reliability assessment techniques: Part 2 — Validation results." Safety Science 27, no. 1 (1997): 43–75. http://dx.doi.org/10.1016/s0925-7535(97)00050-7.

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8

Guerbai, Yasmine, Youcef Chibani, and Yassine Meraihi. "Techniques for Selecting the Optimal Parameters of One-Class Support Vector Machine Classifier for Reduced Samples." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–15. http://dx.doi.org/10.4018/ijamc.290533.

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Usually, the One-Class Support Vector Machine (OC-SVM) requires a large dataset for modeling effectively the target class independently to other classes. For finding the OC-SVM model, the available dataset is subdivided into two subsets namely training and validation, which are used for training and validating the optimal parameters. This approach is effective when a large dataset is available. However, when training samples are reduced, parameters of the OC-SVM are difficult to find in absence of the validation subset. Hence, this paper proposes various techniques for selecting the optimal pa

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9

Worth, Andrew P., Martin D. Barratt, and J. Brian Houston. "The Validation of Computational Prediction Techniques." Alternatives to Laboratory Animals 26, no. 2 (1998): 241–47. http://dx.doi.org/10.1177/026119299802600208.

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10

Drechsler, R., and D. Große. "System level validation using formal techniques." IEE Proceedings - Computers and Digital Techniques 152, no. 3 (2005): 393. http://dx.doi.org/10.1049/ip-cdt:20045073.

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11

Subramanian, Mr P., and Ms R. Indumathi. "Image Registration Methods and Validation Techniques." International Journal of Computer & Organization Trends 4 (January 25, 2014): 11–14. http://dx.doi.org/10.14445/22492593/ijcot-v4p303.

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12

Bowman, Neil T. "Validation of building energy evaluation techniques." International Journal of Ambient Energy 7, no. 3 (1986): 145–50. http://dx.doi.org/10.1080/01430750.1986.9675493.

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13

Ling, You, and Sankaran Mahadevan. "Quantitative model validation techniques: New insights." Reliability Engineering & System Safety 111 (March 2013): 217–31. http://dx.doi.org/10.1016/j.ress.2012.11.011.

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14

Anas, Hafiz, Muhammad Ilyas, Qandeel Tariq, and Muhammad Hummayun. "Requirements Validation Techniques: An Empirical Study." International Journal of Computer Applications 148, no. 14 (2016): 5–10. http://dx.doi.org/10.5120/ijca2016910911.

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15

Hibbert, D. B. "Method validation of modern analytical techniques." Accreditation and Quality Assurance 4, no. 8 (1999): 352–56. http://dx.doi.org/10.1007/s007690050381.

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16

Nava, Jaime, and Vladik Kreinovich. "Towards interval techniques for model validation." Computing 94, no. 2-4 (2011): 257–69. http://dx.doi.org/10.1007/s00607-011-0166-8.

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17

Williams, J. C. "Validation of human reliability assessment techniques." Reliability Engineering 11, no. 3 (1985): 149–62. http://dx.doi.org/10.1016/0143-8174(85)90068-x.

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18

Miller, Michael E., Siu L. Hui, and William M. Tierney. "Validation techniques for logistic regression models." Statistics in Medicine 10, no. 8 (1991): 1213–26. http://dx.doi.org/10.1002/sim.4780100805.

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19

Bhatt, Chintan M., Parth Patel, Tarang Ghetia, and Pier Luigi Mazzeo. "Effective Heart Disease Prediction Using Machine Learning Techniques." Algorithms 16, no. 2 (2023): 88. http://dx.doi.org/10.3390/a16020088.

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The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with H

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20

Dhwanir, Shah, and Kumar Sharma Lokesh. "Contrastive Study of Machine Learning Techniques for Credit Card Fraud Detection." Indian Journal of Science and Technology 18, no. 16 (2025): 1248–59. https://doi.org/10.17485/IJST/v18i16.572.

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Abstract <strong>Objective:</strong> To assess the efficacy of five machine learning algorithms—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and XGBoost—in detecting credit card fraud, utilizing a simulated Kaggle dataset created through Sparkov for credit card transactions. <strong>Methods:</strong> The dataset was partitioned into three training-test ratios: 60%:40%, 70%:30%, and 80%:20%. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was employed. The performance of the models was measured usi

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21

Harrell, Reginal M., William Van Heukelem, and J. Howard Kerby. "A Comparison of Triploid Induction Validation Techniques." Progressive Fish-Culturist 60, no. 3 (1998): 221–26. http://dx.doi.org/10.1577/1548-8640(1998)060<0221:acotiv>2.0.co;2.

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22

Dridi, Mohamed H. "Pedestrian Flow Simulation Validation and Verification Techniques." Current Urban Studies 03, no. 02 (2015): 119–34. http://dx.doi.org/10.4236/cus.2015.32011.

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23

Bhat, M. Iqbal Jeelani, Manish Kr Sharma, Khalid-ul Islam, Rizwan Yousuf, and Zakir Hussain. "Performance appraisal of validation techniques in R." INTERNATIONAL RESEARCH JOURNAL OF AGRICULTURAL ECONOMICS AND STATISTICS 11, no. 2 (2020): 260–68. http://dx.doi.org/10.15740/has/irjaes/11.2/260-268.

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In this article various statistical models were fitted utilizing simulated symmetric and asymmetric data. Fitting of models were carried out with the help of various libraries like minpack.lm, matrices and nlme in R studio (version 3.5.1, 2018) and various selection criteria like RMSE, MAE, AIC, BIC were used for fitting of models. In order to evaluate different validation techniques the simulated data was divided in training and testing data sets and various functions in R were developed for the purpose of validation. Co-efficient summary revealed that all statistical models were statisticall

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24

Hussain Fahad, Zena, Ansam Ahmed Alwan, and Zena Tariq Nayyef. "Validation and Availability Techniques for Computer Faults." Journal of Engineering and Applied Sciences 14, no. 3 (2019): 785–88. http://dx.doi.org/10.36478/jeasci.2019.785.788.

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25

Zahorik, Pavel, and Zijiang J. He. "Virtual auditory display validation using transaural techniques." Journal of the Acoustical Society of America 137, no. 4 (2015): 2230. http://dx.doi.org/10.1121/1.4920136.

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26

Épshtein, N. A. "Validation of HPLC Techniques for Pharmaceutical Analysis." Pharmaceutical Chemistry Journal 38, no. 4 (2004): 212–28. http://dx.doi.org/10.1023/b:phac.0000038422.27193.6c.

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27

Gonzalez, A. J., Lingli Xu, and U. M. Gupta. "Validation techniques for case-based reasoning systems." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 28, no. 4 (1998): 465–77. http://dx.doi.org/10.1109/3468.686707.

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28

Joel, Guillod, Schmid-Saugeon Philippe, Guggisberg David, et al. "Validation of segmentation techniques for digital dermoscopy." Skin Research and Technology 8, no. 4 (2002): 240–49. http://dx.doi.org/10.1034/j.1600-0846.2002.00334.x.

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29

Bolshakova, N., and F. Azuaje. "Cluster validation techniques for genome expression data." Signal Processing 83, no. 4 (2003): 825–33. http://dx.doi.org/10.1016/s0165-1684(02)00475-9.

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30

Nagymáté, Gergely, and Rita M. Kiss. "Motion capture system validation with surveying techniques." Materials Today: Proceedings 5, no. 13 (2018): 26501–6. http://dx.doi.org/10.1016/j.matpr.2018.08.107.

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31

Birn, RM, DA Handwerker, and PA Bandettini. "Comparison and Validation of fMRI Calibration Techniques." NeuroImage 47 (July 2009): S186. http://dx.doi.org/10.1016/s1053-8119(09)72075-0.

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32

Chakravarty, M. Mallar, Abbas F. Sadikot, Jürgen Germann, Gilles Bertrand, and D. Louis Collins. "Towards a validation of atlas warping techniques." Medical Image Analysis 12, no. 6 (2008): 713–26. http://dx.doi.org/10.1016/j.media.2008.04.003.

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33

Hack, E., E. Patterson, T. Siebert, and R. Thalmann. "Calibration and validation of full-field techniques." EPJ Web of Conferences 6 (2010): 46003. http://dx.doi.org/10.1051/epjconf/20100646003.

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34

Schiller, Nelson B. "Validation of New Pulsed Doppler Echocardiographic Techniques." Chest 105, no. 6 (1994): 1902–3. http://dx.doi.org/10.1378/chest.105.6.1902b.

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35

Murthy Divakaruni, S., Bill K. H. Sun, and Owen L. Deutsch. "Signal validation techniques and power plant applications." Progress in Nuclear Energy 22, no. 3 (1988): 181–213. http://dx.doi.org/10.1016/0149-1970(88)90005-4.

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36

Yücelbaş, Cüneyt, and Şule Yücelbaş. "Enhanced Cross-Validation Methods Leveraging Clustering Techniques." Traitement du Signal 40, no. 6 (2023): 2649–60. http://dx.doi.org/10.18280/ts.400626.

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37

Annapareddy, Soujanya Reddy. "Advancements in Firmware Testing and Validation Techniques." ESP Journal of Engineering & Technology Advancements 4, no. 3 (2024): 142–50. https://doi.org/10.56472/25832646/jeta-v4i3p114.

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38

Griffith, D. Todd, Thomas G. Carne, and Joshua A. Paquette. "Modal Testing for Validation of Blade Models." Wind Engineering 32, no. 2 (2008): 91–102. http://dx.doi.org/10.1260/030952408784815817.

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The focus of this paper is a test program designed for wind turbine blades. Model validation is a comprehensive undertaking which requires carefully designing and executing experiments, proposing appropriate physics-based models, and applying correlation techniques to improve these models based on the test data. Structural models are useful for making decisions when designing a new blade or assessing blade performance, and the process of model validation is needed to ensure the quality of these models. Blade modal testing is essential for validation of blade structural models, and this report

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39

Alonso, Jose Vicente, and Lorenzo Escot. "Robust Cross-Validation of Predictive Models Used in Credit Default Risk." Applied Sciences 15, no. 10 (2025): 5495. https://doi.org/10.3390/app15105495.

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Model validation is a challenging Machine Learning task, usually more difficult for consumer credit default models because of the availability of small datasets, the modeling of low-frequency events (imbalanced data), and the bias in the explanatory variables induced by the train/test sets split of the validation techniques (covariate shift). While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. This experimental research focuses on evaluat

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40

Berrada, Faouzi, Saad Bennis, and Luc Gagnon. "Validation des données hydrométriques par des techniques univariées de filtrage." Canadian Journal of Civil Engineering 23, no. 4 (1996): 872–92. http://dx.doi.org/10.1139/l96-895.

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This study aims at validating the historical hydrological data. For the purpose of clarity, the paper has been divided into two parts. The first part, a theoretical one, presents a very broad range of filtering techniques that goes from the simple filter, using the median or the moving average, to the Fraser smoother coupled with the Kalman one. A chronological series simulated and then artificially affected by noise effects, simultaneously with white noise and a certain number of high-amplitude perturbations, was conceived. Its use should allow to reach conclusions regarding the validity and

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41

Bennis, Saad, Sylvain Côté, and Narut Kang. "Validation des données hydrométriques par des techniques multivariées de filtrage." Canadian Journal of Civil Engineering 23, no. 1 (1996): 218–30. http://dx.doi.org/10.1139/l96-023.

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The purpose of this research project was to develop a method for automatic validation of historical daily natural runoff data. Reservoir level measurements, on which natural runoff calculations are directly based were validated. Depending on the number of limnimeters installed, two different approaches were used to validate and adjust reservoir level times series. The best conditions (those discussed here) are when a reservoir has several water-level stations. Under these conditions, multivariate filtering is used to validate time series of recorded levels at each station. This method, called

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42

Kirwan, Barry. "The validation of three human reliability quantification techniques — THERP, HEART and JHEDI: Part 1 — technique descriptions and validation issues." Applied Ergonomics 27, no. 6 (1996): 359–73. http://dx.doi.org/10.1016/s0003-6870(96)00044-0.

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43

Ashvini Byri, Rajas Paresh Kshirsagar, Vishwasrao Salunkhe, Pandi Kirupa Gopalakrishna, Prof.(Dr) Punit Goel, and Dr Satendra Pal Singh. "Advancements in Post Silicon Validation for High Performance GPUs." Darpan International Research Analysis 12, no. 3 (2024): 679–710. http://dx.doi.org/10.36676/dira.v12.i3.129.

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The rapid evolution of high-performance Graphics Processing Units (GPUs) has necessitated significant advancements in post-silicon validation techniques to ensure reliability and performance in diverse applications, from gaming to artificial intelligence. Post-silicon validation serves as a critical phase in the GPU design process, where physical chips are tested and verified against predefined specifications. This paper explores the latest methodologies employed in post-silicon validation, highlighting innovative strategies such as automated testing frameworks, advanced debugging tools, and m

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44

Nurhopipah, Ade, and Uswatun Hasanah. "Dataset Splitting Techniques Comparison For Face Classification on CCTV Images." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 4 (2020): 341. http://dx.doi.org/10.22146/ijccs.58092.

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The performance of classification models in machine learning algorithms is influenced by many factors, one of which is dataset splitting method. To avoid overfitting, it is important to apply a suitable dataset splitting strategy. This study presents comparison of four dataset splitting techniques, namely Random Sub-sampling Validation (RSV), k-Fold Cross Validation (k-FCV), Bootstrap Validation (BV) and Moralis Lima Martin Validation (MLMV). This comparison is done in face classification on CCTV images using Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM) algorit

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45

Shah, Dhwanir, and Lokesh Kumar Sharma. "Benchmarking Machine Learning Techniques for Credit Card Fraud Detection." Indian Journal Of Science And Technology 18, no. 21 (2025): 1681–95. https://doi.org/10.17485/ijst/v18i21.903.

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Objective: An analysis aimed at measuring the success of five machine learning algorithms—Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and XGBoost—in recognizing credit card fraud, based on a Kaggle dataset offered by Mr. Anurag Verma for credit card transactions. Methods: The dataset was segmented into an 80% training set and a 20% testing set. To counteract class imbalance, the Synthetic Minority Over-sampling Technique was applied. The models' performance was assessed using Accuracy, Precision, Recall, F1-score, ROC-AUC, and PRUAC, with validation executed via

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46

Budiarti, Lia Wahyuningsih, and Edi Istiyono. "Development of Affective Assessment Instruments (Attitude Toward Physics) With Peer and Self Assessment Techniques to Grow Attitudes of Responsibility and Cooperation of High School Students." Jurnal Penelitian Pendidikan IPA 9, no. 1 (2023): 495–97. http://dx.doi.org/10.29303/jppipa.v9i1.1774.

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This study aims to develop an assessment instrument (Attitude Toward Physics) with Peer and Self Assessment Techniques to Grow an Attitude of Responsibility and Student Cooperation towards learning physics. The research method used is development research. The research design is in the form of a 4-D model developed by Thiagarajan (1994) with stages that include define, design, develop, and dissemination. Test the validity of the instrument using content validation carried out by the validator. Analysis of the reliability of the instrument using the Alpha Crounbach formula. The validation resul

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47

Warwick, Phillip E. "Validation of Radioanalytical Techniques for Nuclear Waste Characterisation." Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT) 17, no. 4 (2019): 363–73. http://dx.doi.org/10.7733/jnfcwt.2019.17.4.363.

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48

Akinnuwesi, Boluwaji Ade, Stephen Gbenga Fashoto, Elliot Mbunge, Petros Mashwama, and Patrick Adeomo Owate. "A SWOT Analysis of Software Requirement Validation Techniques." International Journal of Software Innovation 10, no. 1 (2022): 1–24. http://dx.doi.org/10.4018/ijsi.297132.

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Existing software requirement validation (SRV) techniques are theoretical concepts with no real-life application. No report on what could be considered as the best of the SRV techniques. Our study focus on systematic literature review of existing SRV techniques, emphasizing on their strengths, weaknesses, opportunities and threats (SWOT) as well as the involvement of end-users in SRV process. We opined to identify SRV technique(s) that could be considered best and user-centric. Four hundred and twenty-four articles were identified initially but after applying the exclusion criteria, 59 article

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49

Ali, Jibran, Stefano Massucco, and Federico Silvestro. "Aggregation Strategy for Reactive Power Compensation Techniques—Validation." Energies 12, no. 11 (2019): 2047. http://dx.doi.org/10.3390/en12112047.

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Reactive power provision is a vital ancillary service, which provides opportunities to service market and power generators. The net reactive power in a balanced power grid needs to be zero, and the imbalance occurs due to the capacitive and inductive behavior of the extensive transmission lines, and because of the intermittent behavior of load-demand. This mismanagement in reactive power causes voltage instability, and hence the paper compares the most common reactive power compensation techniques, which are prevalent in both literature and commercial levels. The paper perceives the trade-off

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50

Ekstrøm, Claus Thorn. "Teaching ‘Instant Experience’ with Graphical Model Validation Techniques." Teaching Statistics 36, no. 1 (2013): 23–26. http://dx.doi.org/10.1111/test.12027.

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