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Journal articles on the topic 'Validation techniques'

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Author: Grafiati
Published: 4 June 2021
Last updated: 26 January 2023

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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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3

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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4

Reinehr, Robert C. "Demonstrating Personality Scale Validation Procedures." Teaching of Psychology 18, no. 4 (December 1991): 241–42. http://dx.doi.org/10.1207/s15328023top1804_14.

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A technique is described for demonstrating personality scale validation techniques to students in introductory psychology classes. This technique promotes a better grasp of the principles of test validation, as evidenced by better class discussion, better written reports, and improved examination grades. Introducing practical applications before presenting theoretical constructs is an effective approach to the teaching of personality scale validation techniques.
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5

Hasan, Inamul, R. Mukesh, P. Radha Krishnan, R. Srinath, Dhanya Prakash Babu, and Negash Lemma Gurmu. "Wind Tunnel Testing and Validation of Helicopter Rotor Blades Using Additive Manufacturing." Advances in Materials Science and Engineering 2022 (September 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/4052208.

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This research paper aims to validate the aerodynamic performance of rotor blades using additive manufacturing techniques. Wind tunnel testing is a technique used to find the flow characteristics of the body. Computational fluid dynamics (CFD) techniques are used for aerodynamic analysis, and validation should be done using wind tunnel testing. In the aerodynamic testing of models, additive manufacturing techniques help in validating the results by making models easily for wind tunnels. Recent developments in additive manufacturing help in the aerodynamic testing of models in wind tunnels. The CFD analysis of helicopter rotor blades was analyzed in this research, and validation was done using additive manufacturing techniques. Computational analysis was carried out for static analysis for the forward speeds of Mach numbers 0.3, 0.4, and 0.5. The results obtained were satisfactory to the previous results and were validated with wind tunnel testing. Results proved that the error percentage was lower, and the computational analysis was valid. In this research, models were designed using the FDM technique for wind tunnel testing as it is cost-effective and easy to manufacture.
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Baharin, Mohd Noor, Zulkifli Mohd Nopiah, Shahrum Abdullah, and Mohd Jailani Mohd Nor. "A Study on Validation of Fatigue Damage Clustering Analysis Technique Based on Clustering Validation Index." Applied Mechanics and Materials 165 (April 2012): 140–44. http://dx.doi.org/10.4028/www.scientific.net/amm.165.140.

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This paper presents the comparative study on two types of the clustering technique for decomposing Variable Amplitude (VA) loadings signals based on its amplitude. These two techniques are used to recognize clusters or patterns of fatigue damaging events in the record which will bring aboutthe majority of fatigue damage. However, one of the problems that existswhencomparing which technique will produce better clusters is the fact thata clustering validation index isneeded. In this study, techniques that were used were theFuzzy C-means and C-means. At first, the VA data weresegmented using the Running Damage Extraction (RDE) technique. Then, each segment produced wasanalysed using the strain life approach and global statistical signal values. Finally, the accuracy of each clustering technique wasmeasured based on the OV coefficient index. From the study, the index shows that the Fuzzy C-means technique produced much better clusters rather than the C-mean clustering technique.
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7

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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8

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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9

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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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 (July 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 (August 22, 2016): 5–10. http://dx.doi.org/10.5120/ijca2016910911.

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15

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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16

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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17

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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18

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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19

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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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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21

Falessi, Davide, Jacky Huang, Likhita Narayana, Jennifer Fong Thai, and Burak Turhan. "On the need of preserving order of data when validating within-project defect classifiers." Empirical Software Engineering 25, no. 6 (August 31, 2020): 4805–30. http://dx.doi.org/10.1007/s10664-020-09868-x.

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Abstract We are in the shoes of a practitioner who uses previous project releases’ data to predict which classes of the current release are defect-prone. In this scenario, the practitioner would like to use the most accurate classifier among the many available ones. A validation technique, hereinafter “technique”, defines how to measure the prediction accuracy of a classifier. Several previous research efforts analyzed several techniques. However, no previous study compared validation techniques in the within-project across-release class-level context or considered techniques that preserve the order of data. In this paper, we investigate which technique recommends the most accurate classifier. We use the last release of a project as the ground truth to evaluate the classifier’s accuracy and hence the ability of a technique to recommend an accurate classifier. We consider nine classifiers, two industry and 13 open projects, and three validation techniques: namely 10-fold cross-validation (i.e., the most used technique), bootstrap (i.e., the recommended technique), and walk-forward (i.e., a technique preserving the order of data). Our results show that: 1) classifiers differ in accuracy in all datasets regardless of their entity per value, 2) walk-forward outperforms both 10-fold cross-validation and bootstrap statistically in all three accuracy metrics: AUC of the selected classifier, bias and absolute bias, 3) surprisingly, all techniques resulted to be more prone to overestimate than to underestimate the performances of classifiers, and 3) the defect rate resulted in changing between the second and first half in both industry projects and 83% of open-source datasets. This study recommends the use of techniques that preserve the order of data such as walk-forward over 10-fold cross-validation and bootstrap in the within-project across-release class-level context given the above empirical results and that walk-forward is by nature more simple, inexpensive, and stable than the other two techniques.
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22

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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23

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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24

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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25

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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27

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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28

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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29

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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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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Rubiana, Iman, Budi Indrawan, and Arief Abdul Malik. "Development and Validation of UNSIL Basketball Guide (PUBbG) Applications Based on Android." JUARA : Jurnal Olahraga 7, no. 1 (March 6, 2022): 241–48. http://dx.doi.org/10.33222/juara.v7i1.1536.

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This research aims to develop and validate the Android-based Unsil Basketball Guide (PUBbG) application. The method used in this research is the Research and Development method. Three experts were involved in validating the design and feasibility of the android application made and user validation by 66 students using the proportional random sampling technique. The instruments used are media and material expert validation instruments and user validation instruments using the System Usability Scale (SUS). Data were analyzed using descriptive statistical techniques. The results showed that the overall application validity test results obtained percentages of 84%, 80%, and 90%, which means that the developed application is valid and feasible to use. The results of user validity using the SUS score are in the excellent category with a value of 72.95, and the results of the application effectiveness assessment based on a user survey get 8.68 results, which means that the application is practical to be able to improve the basic techniques of Bolabakset. This study concludes that the application product that has been made is in the appropriate category based on the results of expert evaluations and is effective for improving basic basketball techniques based on survey results to users.
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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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Restika Rahmadani and Sari Herlina. "LEARNING TOOL DEVELOPMENT WITH THE PROBLEM BASED LEARNING (PBL) MODEL-ORIENTED MATHEMATICAL CRITICAL THINKING ABILITY." Mathematics Research and Education Journal 4, no. 2 (October 30, 2020): 16–22. http://dx.doi.org/10.25299/mrej.2020.vol4(2).9287.

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This study aims to produce learning tools in the form of Learning Implementation Plans (RPP) and Student Worksheets (LKPD) with Problem Based Learning (PBL) oriented Mathematical Critical Thinking Ability which has been tested for validity. The type of research used is Development Research using a modified ADDIE development model, namely the Analysis, Design, Development, and Evaluation stages. Instruments and data collection techniques are in RPP and LKPD validation sheets by validating learning tools by 2 Mathematics Education Lecturers, FKIP UIR, and 2 Mathematics Subject Teachers. The data analysis technique used is data analysis of the validation results of learning devices using descriptive statistics. The results showed that the results of the RPP validity for each aspect were 84.13%, with a very valid validity level. Each validator was 84.84%, with a very valid validity level. In comparison, the LKPD validity results for each aspect were 84.59%, with a very valid validity level, and each validator is 84.42% with a very valid validity level. Based on the results of the development of these learning tools, a Learning Device with a Problem Based Learning (PBL) Model oriented to Mathematical Critical Thinking Skills on Quadrangular Material has been tested for validity
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42

Prasetyo, Aditya Budi, and Tri Ginanjar Laksana. "Optimization of K-Nearest Neighbors Algorithm with Cross Validation Techniques for Diabetes Prediction with Streamlit." Journal of Applied Informatics and Computing 6, no. 2 (December 8, 2022): 194–204. http://dx.doi.org/10.30871/jaic.v6i2.4182.

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The problem that occurs in the application of K-Nearest Neighbors as a classification algorithm is the frequent occurrence of overfitting in data processing. This can be overcome by using cross-validation techniques in evaluating the algorithm model and minimizing overfitting. Then the performance of diabetes prediction accuracy is unknown using the K-Nearest Neighbors algorithm with cross-validation technique. The data used comes from the National Institute of Digestive and Kidney Diabetes in 2021. The case study in this study is to find out the initial screening for diabetes is supported by the results of algorithm accuracy and real time application of streamlit-based users. The purpose of this study was to optimize the accuracy results with a cross validation technique supported by the k-nearest neighbors algorithm in the study of diabetes data. The method used is the k-nearest neighbors algorithm which is supported by cross validation technique for optimal accuracy results. Then the application of a streamlit-based interactive web application for testing the accuracy results used by the user to see the probability that the user has diabetes. The results showed that the optimization of the Cross Validation technique supported by the KNearest Neighbors algorithm model worked well. The results of the confusion matrix using the cross validation technique are more accurate in terms of the advantages of using the cross-validation technique itself. So that the classification report which has a value of 95% is more accurate than the accuracy which is worth 92% because of the use of cross-validation techniques that can minimize overfitting in addition to considerations of the accuracy value and the implementation of streamlit-based interactive web applications for user testing is going well.
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43

Sharma, Shivani, Navdeep Singh, Amar Deep Ankalgi, Arti Rana, and Mahendra Singh Ashawat. "Modern Trends in Analytical Techniques for Method Development and Validation of Pharmaceuticals: A Review." Journal of Drug Delivery and Therapeutics 11, no. 1-s (February 15, 2021): 121–30. http://dx.doi.org/10.22270/jddt.v11i1-s.4515.

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The process of drug development requires a suitable technique which helps the scientist to analyze the drug molecule is an accurate, precise, and easiest way. For the quantitative and qualitative estimation of drugs in analytical chemistry it is very important to identify the best method for method development. This study helps the author to understand the various analytical techniques available for the process of drug development which includes spectroscopy, chromatography, electrochemical techniques, electrophoretic, flow injection analysis, and hyphenated technique. All these methods contain different analytical process with a variety of separate techniques. Also, we discuss about the modern trend which are available, and implacable in all these methods to improve the analytical behavior of these techniques. In method development process the validation of document must be required in the form of accuracy, precision, specificity, limit of detection, linearity, and range is considered. So, this review article contains the brief summary of available analytical techniques, and the latest trend in method development, or the process of method validation, and development of method. The discussed methods in this review article were revealed by the scientist, and these techniques must require in new drug development process, which helps the person to utilize the potential of these techniques. Trend in the analytical chemistry to overcome the error in method development was necessary, and the latest trends in method development technique were useful to defeat errors in analytical techniques. Keywords: Analytical techniques, Modern trends, Process of method development, and validation.
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44

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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45

Ouf, Shimaa, and Ahmed I. B. ElSeddawy. "A PROPOSED PARADIGM FOR INTELLIGENT HEART DISEASE PREDICTION SYSTEM USING DATA MINING TECHNIQUES." Journal of Southwest Jiaotong University 56, no. 4 (August 30, 2021): 220–40. http://dx.doi.org/10.35741/issn.0258-2724.56.4.19.

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The data mining techniques-based systems could have a crucial impact on the employees’ lifestyle to predict heart diseases. There are many scientific papers, which use the techniques of data mining to predict heart diseases. However, limited scientific papers have addressed the four cross-validation techniques of splitting the data set that plays an important role in selecting the best technique for predicting heart disease. It is important to choose the optimal combination between the cross-validation techniques and the data mining, classification techniques that can enhance the performance of the prediction models. This paper aims to apply the four-cross-validation techniques (holdout, k-fold cross-validation, stratified k fold cross-validation, and repeated random) with the eight data mining, classification techniques (Linear Discriminant Analysis, Logistic regression, Support Vector Model, KNN, Decision Tree, Naïve Bayes, Random Forest, and Neural Network) to improve the accuracy of heart disease prediction and select the best prediction models. It analyzes these techniques on a small and large dataset collected from different data sources like Kaggle and the UCI machine-learning repository. The evaluation metrics like accuracy, precision, recall, and F-measure were used to measure the performance of prediction models. Experimentation is performed on two datasets, and the results show that when the dataset is colossal (70000 records), the optimal combination that achieves the highest accuracy is holdout cross-validation with the neural network with an accuracy of 71.82%. At the same time, Repeated Random with Random Forest considers the optimal combination in a small dataset (303 records) with an accuracy of 89.01%. The best models will be recommended to the physicians in business organizations to help them predicting heart disease in employees into one of two categories, cardiac and non-cardiac, at an early stage. The early detection of heart diseases in employees will improve productivity in the business organization.
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46

Ouf, Shimaa, and Ahmed I. B. ElSeddawy. "A PROPOSED PARADIGM FOR INTELLIGENT HEART DISEASE PREDICTION SYSTEM USING DATA MINING TECHNIQUES." Journal of Southwest Jiaotong University 56, no. 4 (August 30, 2021): 220–40. http://dx.doi.org/10.35741/issn.0258-2724.56.4.19.

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Abstract:
The data mining techniques-based systems could have a crucial impact on the employees’ lifestyle to predict heart diseases. There are many scientific papers, which use the techniques of data mining to predict heart diseases. However, limited scientific papers have addressed the four cross-validation techniques of splitting the data set that plays an important role in selecting the best technique for predicting heart disease. It is important to choose the optimal combination between the cross-validation techniques and the data mining, classification techniques that can enhance the performance of the prediction models. This paper aims to apply the four-cross-validation techniques (holdout, k-fold cross-validation, stratified k fold cross-validation, and repeated random) with the eight data mining, classification techniques (Linear Discriminant Analysis, Logistic regression, Support Vector Model, KNN, Decision Tree, Naïve Bayes, Random Forest, and Neural Network) to improve the accuracy of heart disease prediction and select the best prediction models. It analyzes these techniques on a small and large dataset collected from different data sources like Kaggle and the UCI machine-learning repository. The evaluation metrics like accuracy, precision, recall, and F-measure were used to measure the performance of prediction models. Experimentation is performed on two datasets, and the results show that when the dataset is colossal (70000 records), the optimal combination that achieves the highest accuracy is holdout cross-validation with the neural network with an accuracy of 71.82%. At the same time, Repeated Random with Random Forest considers the optimal combination in a small dataset (303 records) with an accuracy of 89.01%. The best models will be recommended to the physicians in business organizations to help them predicting heart disease in employees into one of two categories, cardiac and non-cardiac, at an early stage. The early detection of heart diseases in employees will improve productivity in the business organization.
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47

Apituley, Arnoud, Michel Van Roozendael, Andreas Richter, Thomas Wagner, Udo Friess, Francois Hendrick, Karin Kreher, and Jan-Lukas Tirpitz. "Aerosol profiling during the large scale field campaign CINDI-2." EPJ Web of Conferences 176 (2018): 10005. http://dx.doi.org/10.1051/epjconf/201817610005.

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For the validation of space borne observations of NO2 and other trace gases from hyperspectral imagers, ground based instruments based on the MAXDOAS technique are an excellent choice, since they rely on similar retrieval techniques as the observations from orbit. To ensure proper traceability of the MAXDOAS observations, a thorough validation and intercomparison is mandatory. Advanced MAXDOAS observation and retrieval techniques enable inferring vertical structure of trace gases and aerosols. These techniques and their results need validation by e.g. lidar techniques. For the proper understanding of the results from passive remote sensing techniques, independent observations are needed that include parameters needed to understand the light paths, i.e. in-situ aerosol observations of optical and microphysical properties, and essential are in particular the vertical profiles of aerosol optical properties by (Raman) lidar. The approach used in the CINDI-2 campaign held in Cabauw in 2016 is presented in this paper and the results will be discussed in the presentation at the conference.
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48

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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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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50

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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