Artykuły w czasopismach: „Validation techniques”

1

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

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

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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 (May 11, 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 conducted through 10-fold cross-validation. Findings: XGBoost consistently surpassed the other models, achieving precision and recall rates of up to 99.05% and 99.81%, respectively. The Decision Tree and Random Forest models produced precision scores of 96.86% and 94.65%, with recall values of 99.08% and 99.49%. Logistic Regression and SVM exhibited comparatively lower performance across all training-test splits. Novelty: This study introduces a unique methodology by evaluating machine learning algorithms across different training-test splits and validating the outcomes through 10-fold cross-validation to ensure the robustness of the models. Keywords: Credit card Fraud, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, K-Fold cross validation, OneHotEncoding

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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 (May 17, 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 evaluation of validation parameters using mathematical analysis methods. They also propose to divide methods into two types: forensic expert measurement methods (FMT) and forensic expert testing methods (FTT). Based on the generalization of information presented in several regulatory documents and scientific publications, the following parameters are identified for FMT: metrological characteristics or properties of the method (specificity, linearity, sensitivity, range of determined values, detection limit, quantitative determination limit) and quality indicators of the method (precision, correctness, accuracy of the analysis result, or uncertainty). When validating FTT, it is proposed to evaluate the reliability of the method and the competence of the expert.An experiment to assess validation parameters is performed using enough control samples with established characteristics of controlled indicators and with the participation of a sufficient number of experts. Requirements for control samples are provided.The authors also give examples of probabilistic evaluation of validation parameters for two qualitative testing methods: microscopic examination of textile fibers and detection of gunshot residue using scanning electron microscopy and X-ray microanalysis. The reliability of these methods is assessed by calculating the likelihood ratio, and the specificity of interpreting the results of FMT and FTT validation is noted.The decision on compliance with the requirements is made if the interval of the established extended uncertainty for the obtained result does not exceed the tolerance field. In the absence of tolerances, FMT is considered suitable for solving forensic expert tasks if the values of the extended uncertainty of the measurement results of the controlled indicator do not exceed the values established during validation. For FTT, a low probabilistic proportion of false positive and false negative results in determining the presence/absence of controlled indicators, as well as experimentally confirmed competence of the expert during validation, are indicators of the suitability of the method for its intended use

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

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

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Amandeep, Kaur*and Monika Gupta. "DEVELOPMENT AND VALIDATION OF METHOD BY HPLC TECHNIQUES." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 07 (August 1, 2018): 7057–65. https://doi.org/10.5281/zenodo.1325897.

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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. Keywords: Introduction, Method development, Steps for HPLC method development, Method validation, Advantages of method validation and Parameters for validation.

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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 (January 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 parameters using only a training subset. The experimental evaluation conducted on several real-world benchmarks proves the effective use of the new selection parameter techniques for validating the model of OC-SVM classifiers versus the standard validation techniques

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

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

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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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Anas, Hafiz, Muhammad Ilyas, Qandeel Tariq, and Muhammad Hummayun. "Requirements Validation Techniques: An Empirical Study." International Journal of Computer Applications 148, no. 14 (August 22, 2016): 5–10. http://dx.doi.org/10.5120/ijca2016910911.

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Hibbert, D. B. "Method validation of modern analytical techniques." Accreditation and Quality Assurance 4, no. 8 (August 3, 1999): 352–56. http://dx.doi.org/10.1007/s007690050381.

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Nava, Jaime, and Vladik Kreinovich. "Towards interval techniques for model validation." Computing 94, no. 2-4 (December 15, 2011): 257–69. http://dx.doi.org/10.1007/s00607-011-0166-8.

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

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

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Bhatt, Chintan M., Parth Patel, Tarang Ghetia, and Pier Luigi Mazzeo. "Effective Heart Disease Prediction Using Machine Learning Techniques." Algorithms 16, no. 2 (February 6, 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 Huang starting that can improve classification accuracy. The underlying model applies DBSCAN to remove outliers. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used. GridSearchCV was used to hypertune the parameters of the applied model to optimize the result. The proposed model is applied to a real-world dataset of 70,000 instances from Kaggle. Models were trained on data that were split in 80:20 and achieved accuracy as follows: decision tree: 86.37% (with cross-validation) and 86.53% (without cross-validation), XGBoost: 86.87% (with cross-validation) and 87.02% (without cross-validation), random forest: 87.05% (with cross-validation) and 86.92% (without cross-validation), multilayer perceptron: 87.28% (with cross-validation) and 86.94% (without cross-validation). The proposed models have AUC (area under the curve) values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The conclusion drawn from this underlying research is that multilayer perceptron with cross-validation has outperformed all other algorithms in terms of accuracy. It achieved the highest accuracy of 87.28%.

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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 (May 6, 2025): 1248–59. https://doi.org/10.17485/IJST/v18i16.572.

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Abstract 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 conducted through 10-fold cross-validation. Findings: XGBoost consistently surpassed the other models, achieving precision and recall rates of up to 99.05% and 99.81%, respectively. The Decision Tree and Random Forest models produced precision scores of 96.86% and 94.65%, with recall values of 99.08% and 99.49%. Logistic Regression and SVM exhibited comparatively lower performance across all training-test splits. Novelty: This study introduces a unique methodology by evaluating machine learning algorithms across different training-test splits and validating the outcomes through 10-fold cross-validation to ensure the robustness of the models. Keywords: Credit card Fraud, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, K-Fold cross validation, OneHotEncoding

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Griffith, D. Todd, Thomas G. Carne, and Joshua A. Paquette. "Modal Testing for Validation of Blade Models." Wind Engineering 32, no. 2 (March 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 discusses modal test techniques required to achieve validation. Choices made in the design of a modal test can significantly affect the final test result. This study aims to demonstrate the importance of the proper pre-test design and test technique for validating blade structural models.

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

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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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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 (September 15, 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 statistically significant across both symmetric as well as asymmetric distributions. In preliminary analysis TFEM (Type First Exponential Model) was found out to be the best linear model across the distributions with lower values of RMSE, MAE, BIAS, AIC and BIC. Among non-linear models, Haung model was found out to be best model across both the distributions as it has lower values of RMSE, MAE etc. Different validation techniques like Half splitting, LOOCV and 5-folded cross validation were used in the present study. Based on the results of evaluation 5-folded cross validation performed better, as it resulted in lower rates of prediction error in comparison to its counter parts.

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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 (December 20, 2019): 785–88. http://dx.doi.org/10.36478/jeasci.2019.785.788.

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

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

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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 (July 1998): 465–77. http://dx.doi.org/10.1109/3468.686707.

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Joel, Guillod, Schmid-Saugeon Philippe, Guggisberg David, Cerottini Jean Philippe, Braun Ralph, Krischer Joakim, Saurat Jean-Hilaire, and Kunt Murat. "Validation of segmentation techniques for digital dermoscopy." Skin Research and Technology 8, no. 4 (November 2002): 240–49. http://dx.doi.org/10.1034/j.1600-0846.2002.00334.x.

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

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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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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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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 (December 2008): 713–26. http://dx.doi.org/10.1016/j.media.2008.04.003.

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

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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 (January 1988): 181–213. http://dx.doi.org/10.1016/0149-1970(88)90005-4.

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Yücelbaş, Cüneyt, and Şule Yücelbaş. "Enhanced Cross-Validation Methods Leveraging Clustering Techniques." Traitement du Signal 40, no. 6 (December 30, 2023): 2649–60. http://dx.doi.org/10.18280/ts.400626.

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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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Alonso, Jose Vicente, and Lorenzo Escot. "Robust Cross-Validation of Predictive Models Used in Credit Default Risk." Applied Sciences 15, no. 10 (May 14, 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 evaluating existing robust cross-validation variants to address the issues of validating credit default models. In addition, some improvements to those methods are proposed and compared with a wide range of validation techniques, including fuzzy methods. To reach solid and practical conclusions, this work limits its scope to logistic regression, as it is the best-practice modeling technique in real-world applications of this context. It is shown that robust cross-validation algorithms lead to more stable estimates, as expected due to the more homogeneous partitions, which have a positive impact on the selection of credit default models. In addition, the enhancements proposed to existing robust techniques lead to improved results when there are data restrictions.

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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 (August 1, 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 performance of the different filters. The second part, a practical one, uses several actual historical series relative to measures of the water level in a reservoir, the natural inflow, and inlet and outlet flows. Its essential objective is to validate the natural inflow data (calculated by hydric balance). A general chart of the procedure employed to carry out this validation is thus presented. The results obtained are then compared. All the techniques are used so as to make the validation automatic. This resulted in creating a very general application software called Valideb. It allows the user to analyze and judge the results of a filtering method and its effect on each component of the hydric balance. Key words: validation, filtering, smoothing, natural inflow, water level, flow, covariance, noise, Kalman, Fraser. [Journal translation]

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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 (February 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 the multifilter method consists of comparing deviations between the value predicted with an autoregressive model, the measured historical value, and an estimate obtained using a regression model at neighbouring stations. Among the measured value and the estimate derived from the linear regression model, the closest value to the forecast was retained. One advantage in validating historical hydrometric data is the availability of data before and after the date to be validated. In other words, to validate the value of level Nt not only are the values Nt−1Nt−2, … available, but also the values Nt+1, Nt+2 … at the station to be validated as well as at neighbouring stations. To take advantage of this, the multifilter validation process was performed twice: in the usual time direction and backwards. The historical value was considered faulty and discarded only if it was rejected in both the forward and backward validation processes. All techniques developed have been incorporated into the software called ValiDeb and successfully tested at the Gatineau River site in Quebec. Key words: validation, filtering, multivariate, equipment redundance, analysis, levels, runoff, Kalman. [Journal translation]

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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 (December 1996): 359–73. http://dx.doi.org/10.1016/s0003-6870(96)00044-0.

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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 (September 30, 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 machine learning algorithms that enhance fault detection and correction capabilities. We discuss the challenges associated with validating complex architectures, including power efficiency, thermal management, and system-level interactions, which are pivotal in meeting the demands of high-performance computing. Furthermore, the integration of hardware emulation and simulation techniques is examined, showcasing how these tools facilitate early identification of design flaws and optimize the validation workflow. This research underscores the importance of a holistic validation approach that incorporates both software and hardware perspectives, ensuring that GPUs not only meet performance metrics but also maintain high levels of reliability. The findings indicate that as GPU designs become increasingly intricate, adopting advanced post-silicon validation techniques will be essential for sustaining innovation and delivering cutting-edge performance in next-generation applications. Through this exploration, we aim to provide insights into the future landscape of GPU validation, emphasizing the critical role it plays in the successful deployment of high-performance computing technologies

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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 (October 31, 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) algorithm. This study is also applied in two image datasets. The results of the comparison are reviewed by using model accuracy in training set, validation set and test set, also bias and variance of the model. The experiment shows that k-FCV technique has more stable performance and provide high accuracy on training set as well as good generalizations on validation set and test set. Meanwhile, data splitting using MLMV technique has lower performance than the other three techniques since it yields lower accuracy. This technique also shows higher bias and variance values and it builds overfitting models, especially when it is applied on validation set.

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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 (January 31, 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 results from the validator state that the affective assessment instrument (Attitude Toward Physics) with Peer and Self Assessment Techniques to Grow Students' Attitudes of Responsibility and Cooperation towards learning physics is feasible to use.

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Warwick, Phillip E. "Validation of Radioanalytical Techniques for Nuclear Waste Characterisation." Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT) 17, no. 4 (December 31, 2019): 363–73. http://dx.doi.org/10.7733/jnfcwt.2019.17.4.363.

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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 (January 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 articles were identified for review. We defined one generic research question (GRQ) and five specific research questions (SRQi, i = 1,2,3,4,5) to guide our review and provide the required details for SWOT analysis of SRV techniques. A software is yet to be developed for users’ requirements validation both on a small and larg scale and none of the SRV techniques incorporate user story framework.

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Ali, Jibran, Stefano Massucco, and Federico Silvestro. "Aggregation Strategy for Reactive Power Compensation Techniques—Validation." Energies 12, no. 11 (May 28, 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 between the compared techniques, and realizes to use the aggregation of different techniques to present a coordinated control mechanism that complies with the Italian regulations. The parameters for the proposed aggregation include the amount of reactive power, real power losses during reactive power provision, and response time. The paper then implements IEEE 9 bus transmission-generation system in DIgSILENT to set up the platform for validation of the proposed strategy. Finally, it simulates Transmission System Operator (TSO) test cases on the implemented test system.

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Ekstrøm, Claus Thorn. "Teaching ‘Instant Experience’ with Graphical Model Validation Techniques." Teaching Statistics 36, no. 1 (September 19, 2013): 23–26. http://dx.doi.org/10.1111/test.12027.

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Hirzberger, J., A. Feller, T. L. Riethmüller, A. Gandorfer, and S. K. Solanki. "Performance validation of phase diversity image reconstruction techniques." Astronomy & Astrophysics 529 (April 18, 2011): A132. http://dx.doi.org/10.1051/0004-6361/201015717.

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