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Journal articles on the topic 'Technique selection'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Sharaff, Aakanksha, Naresh Kumar Nagwani, and Kunal Swami. "Impact of Feature Selection Technique on Email Classification." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 59–63. http://dx.doi.org/10.7763/ijke.2015.v1.10.

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2

EKER, İPEK, AYFER ERGİN, and GÜLER ALKAN. "SELECTION OF CONTAINER PORT WITH FAHP-TOPSIS TECHNIQUE." Beykoz Akademi Dergisi 1, no. 1 (June 1, 2013): 43–58. http://dx.doi.org/10.14514/byk.m.21478082.2013.1/1.43-58.

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3

Vilchez, Ramcis N. "Modified Selection Sort Algorithm Employing Boolean and Distinct Function in a Bidirectional Enhanced Selection Technique." International Journal of Machine Learning and Computing 10, no. 1 (January 2020): 93–98. http://dx.doi.org/10.18178/ijmlc.2020.10.1.904.

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4

Marcucci, Bruce. "A shade selection technique." Journal of Prosthetic Dentistry 89, no. 5 (May 2003): 518–21. http://dx.doi.org/10.1016/s0022-3913(03)00076-3.

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5

Pizzamiglio, Elio. "A color selection technique." Journal of Prosthetic Dentistry 66, no. 5 (November 1991): 592–96. http://dx.doi.org/10.1016/0022-3913(91)90435-y.

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6

Peruzzi, L., G. Melioli, L. B. De Monte, M. Alessio, M. Zaccolo, S. Roggero, G. Bellone, A. Funaro, and F. Malavasi. "Microplate selection technique (MPST)." Journal of Immunological Methods 123, no. 1 (September 1989): 113–21. http://dx.doi.org/10.1016/0022-1759(89)90035-5.

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7

Grunt, Paweł. "Structured Analytic Techniques: Taxonomy and Technique Selection for Information and Intelligence Analysis Practitioners." Journal of Management and Financial Sciences, no. 30 (July 29, 2019): 115–36. http://dx.doi.org/10.33119/jmfs.2017.30.7.

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The purpose of this article is to propose a new taxonomy and selection of structured analytictechniques for information and intelligence analysis practitioners. The presented taxonomyand selection of structured analytic techniques are based on the author’s experience in informationand intelligence analysis as well as in training analysts in the use of those techniques.The presented classification and selection of techniques have resulted from the author’sattempts to improve teaching and learning process of the analysts who were novice to structuredanalytic techniques. From the author’s experience, a well-constructed taxonomy aidsnovice analysts in understanding a purpose of used techniques and selecting the best-suitedtechnique for a given analytic problem. In the article the author will first offer definitions ofanalysis, analytic process and analytic spectrum. This will be followed by a review of the fourgeneral categories of analytic methods used in intelligence analysis. The author will furtherexplain the origins, concepts and characteristics of both structured analysis and structuredanalytic techniques. Next, structured analytic techniques will be discussed in the context ofthe Intelligence Cycle. Following this, the author will review several existing taxonomies ofstructured analytic techniques. The article will conclude with a taxonomy and a selection oftechniques for the information and intelligence analysis practitioners, based on a review ofthe literature augmented by the author’s professional experience.

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Hagedorn, Judith C., and Bryan B. Voelzke. "Patient Selection for Urethroplasty Technique." Urologic Clinics of North America 44, no. 1 (February 2017): 27–37. http://dx.doi.org/10.1016/j.ucl.2016.08.007.

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9

Carrizo, Dante, Oscar Dieste, and Natalia Juristo. "Systematizing requirements elicitation technique selection." Information and Software Technology 56, no. 6 (June 2014): 644–69. http://dx.doi.org/10.1016/j.infsof.2014.01.009.

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10

Chang, Kuei-Hu. "A Novel Contractor Selection Technique Using the Extended PROMETHEE II Method." Mathematical Problems in Engineering 2021 (November 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/3664709.

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Selecting suitable contractors directly influences product quality, corporate profits, and even sustainable development. The selection problem of contractors is, therefore, a critical issue for the sustainable development of an enterprise. However, traditional contractor selection techniques are unable to handle information regarding the relative importance of criteria or handle nonexistent or missing data in the assessment process of contractor selection. In order to effectively address this problem, this study proposes a new contractor selection technique that integrates the concept of soft set and the PROMETHEE II method to select suitable contractors. Three numerical examples are applied to prove the correctness and effectiveness of the proposed technique. This study also compares the simulation results achieved using the proposed method with those achieved using the traditional weighted arithmetic averaging method and the data envelopment analysis (DEA) technique. The simulation results show that the proposed method is a more general contractor selection technique for handling incomplete information than the traditional weighted arithmetic averaging method and the DEA method.

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11

Agrawal, Anil, Mohd Husain, Raj Gaurang Tiwari, and Subodh Kumar. "A Novel Technique for Database Selection and Document Selection." International Journal of Computer Applications 17, no. 8 (March 31, 2011): 22–26. http://dx.doi.org/10.5120/2241-2865.

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12

Friedman, Shimon, Adam Stabholz, and Aviad Tamse. "Endodontic retreatment—Case selection and technique. Part 3. Retreatment techniques." Journal of Endodontics 16, no. 11 (November 1990): 543–49. http://dx.doi.org/10.1016/s0099-2399(07)80219-6.

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13

Abed Aljasim Muhisn, Zahraa, Mazni Omar, Mazida Ahmad, and Sinan Adnan Muhisn. "Team Leader Selection by Using an Analytic Hierarchy Process (AHP) Technique." Journal of Software 10, no. 10 (October 2015): 1216–27. http://dx.doi.org/10.17706//jsw.10.10.1216-1227.

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14

Zaki, Amira I., Mai Abdelgelil, Said E. El-Khamy, and Waleed K. Badawi. "MIMO Self-Heterodyne OFDM Using Band Selection Technique." Entropy 23, no. 1 (December 28, 2020): 32. http://dx.doi.org/10.3390/e23010032.

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The 5G technology is a promising technology to cope with the increasing demand for higher data rate and quality of service. In this paper, two proposed techniques are implemented for multiple input multiple output (MIMO) self-heterodyne OFDM system to enhance data rate and minimize the bit error rate (BER). In both of the two proposed techniques, Band Selection (BS) approach is used, once with Space Time Block Coded (STBC) for the first proposed technique (BS- STBC), and once again with Frequency Space Time Block Coded (FSTBC) for the second proposed technique (BS-FSTBC). The use of the BS in the proposed techniques helps to choose the sub-band with better subchannels gains for sending the information and consequently, minimize the BER. Moreover, the use of the FSTBC instead of STBC helps to use the spectral efficiently and hence increase data rate. The simulation results show that the proposed techniques BS-STBC and BS-FSTBC, for the MIMO self-heterodyne OFDM system, provide a great enhancement in the BER performance when compared to the conventional techniques. Moreover, the simulation results show that the first proposed technique BS-FSTBC outperform the second propose technique BS-STBC in term of the BER performance.

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15

Song, Zhigang, and Laura Safran. "Diagnostic Technique Selection for SRAM Logic Type Failures." EDFA Technical Articles 20, no. 2 (May 1, 2018): 18–24. http://dx.doi.org/10.31399/asm.edfa.2018-2.p018.

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Abstract Selecting a fault isolation technique for a particular type of SRAM logic failure requires an understanding of available methods. In this article, the authors review common fault isolation techniques and present several case studies, explaining how they determined which technique to use.

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16

Nguyen, Lien Bich, and Lam Tra Pham. "Accounting software selection: The relationship between selection technique and outcome." Science and Technology Development Journal 19, no. 3 (September 30, 2016): 5–17. http://dx.doi.org/10.32508/stdj.v19i3.489.

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The demand for accounting software keeps increasing over the time. However, choosing suitable and satisfactory accounting software that meets requirements of the company is by no means easy. Four factors that companies take into consideration in choosing accounting software are criteria, method, technique, and software features (e.g. support tools). This paper focused on testing the relationship between accounting software selection technique and its outcome as well as the company characteristics. A variety of methodologies were carried out, i.e. ANOVA and chi squared test for quantitative study and face-to-face interview for qualitative study, in which the latter was conducted after the former to confirm and explain the former. Results showed that in Vietnam as a transition market, the accounting software selection technique does not directly affect the successful choice of accounting software, i.e. suitable and satisfactory software. Size and line of business at large have no impact on the accounting software selection technique that the company is adopting.

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17

Phogat, Manu, and Dharmender Kumar. "Disease Single Nucleotide Polymorphism Selection using Hybrid Feature Selection Technique." Journal of Physics: Conference Series 1950, no. 1 (August 1, 2021): 012079. http://dx.doi.org/10.1088/1742-6596/1950/1/012079.

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18

Fernando, Hiran C. "Endoscopic fundoplication: patient selection and technique." Journal of Visualized Surgery 3 (September 15, 2017): 121. http://dx.doi.org/10.21037/jovs.2017.08.03.

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19

Deshmukh, Dr Tejashree. "“Graphology: A Technique for Employee Selection”." GIS Business 14, no. 4 (August 12, 2019): 209–13. http://dx.doi.org/10.26643/gis.v14i4.6879.

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Importance of Employee Selection is discussed widely by many authors till date. If we believe that the organizational success or failure is dependent on the talent pool of the employees, then we admit that Employee Selection is one of the most important areas of Human Resource Management. Thomas Stone defined Selection as "a process of differentiating between applicants in order to identify (and hire) those with a greater likelihood of success in a job".

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20

Anderson, Neil. "Repertory Grid Technique in Employee Selection." Personnel Review 19, no. 3 (March 1990): 9–15. http://dx.doi.org/10.1108/00483489010143032.

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21

Lyu, Yunxiao, Ting Li, Bin Wang, Yunxiao Cheng, and Sicong Zhao. "Selection of pancreaticojejunostomy technique after pancreaticoduodenectomy." Medicine 97, no. 40 (October 2018): e12621. http://dx.doi.org/10.1097/md.0000000000012621.

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22

Schellhas, Kurt P. "Discography: Patient Selection, Technique and Interpretation." Journal of Vascular and Interventional Radiology 11, no. 2 (February 2000): 46–47. http://dx.doi.org/10.1016/s1051-0443(00)70026-1.

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23

Rankovic, Vladimir, Zora Arsovski, Slavko Arsovski, Zoran Kalinic, Igor Milanovic, and Dragana Rejman-Petrovic. "Supplier Selection using NSGA-II Technique." International Journal of Web Portals 4, no. 4 (October 2012): 35–47. http://dx.doi.org/10.4018/jwp.2012100103.

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In modern manufacturing industries, supplier selection is increasingly recognized as a critical decision in supply chain management. Supplier selection problem is a typical multiple criteria decision making problem involving a number of different and usually conflicting objectives. Because of that, modern supplier selection techniques imply solving of multi-objective optimization problems. In this paper supplier selection using evolutionary algorithm (NSGA-II method) is presented. Recent investigations show that quality and costs are among the most important selection factors. In this paper the authors used variance of quality and total costs as criteria for selection optimization. Results show that described methodology can be applicable for the practical purposes.

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24

Penrose, L. L., S. Jabara, and S. D. Prien. "A preparatory technique for semen selection." Fertility and Sterility 90 (September 2008): S416—S417. http://dx.doi.org/10.1016/j.fertnstert.2008.07.1362.

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25

Mohamed, Rozlini, Munirah Mohd Yusof, Noorhaniza Wahid, Norhanifah Murli, and Muhaini Othman. "Bat algorithm and k-means techniques for classification performance improvement." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (September 1, 2019): 1411. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1411-1418.

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This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and feature selection technique.

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Srinivasan A, Srinivasan A., Arunachalam RM Arunachalam RM, Ramesh S. Ramesh. S, and Senthilkumaar J. S. Senthilkumaar J. S. "Selection of optimal machining parameters for metal matrix composites using Taguchi Technique." Indian Journal of Applied Research 1, no. 2 (October 1, 2011): 36–38. http://dx.doi.org/10.15373/2249555x/nov2011/12.

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27

D., Arulanantham. "MIMO based Channel Selection Technique for Automation Broadband Networks with IoT Applications." International Journal of Psychosocial Rehabilitation 23, no. 3 (September 30, 2019): 1002–11. http://dx.doi.org/10.37200/ijpr/v23i3/pr190500.

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28

Jain, Rahi, and Wei Xu. "HDSI: High dimensional selection with interactions algorithm on feature selection and testing." PLOS ONE 16, no. 2 (February 16, 2021): e0246159. http://dx.doi.org/10.1371/journal.pone.0246159.

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Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO.

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Cancian, Glen, Wayne Pullan, and Gary Chai. "A Review of PMS Treatment Selection Techniques with Comparative Analysis with QTMR Technique." Advanced Materials Research 723 (August 2013): 769–81. http://dx.doi.org/10.4028/www.scientific.net/amr.723.769.

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Treatment selection techniques used in Pavement Management Systems often rely on predefined priorities outlined by state road agency which are subject to priority and engineering judgement. These techniques can be implemented over multi-period planning horizons however doesnt necessarily provide the best possible works program. This paper presents an overview of Pavement Management Systems, then describes and analyses the four main categories of treatment selection techniques employed within these systems. An overview of the Pavement Management System implemented by the Queensland Department of Transport and Main Roads and in particular its process of treatment selection is outlined. In addition a comparative analysis is then undertaken with each of these treatment selection categories.

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30

LAWANNA, Adtha. "HTTCS: Hybridization Technique for Test Case Selection." Walailak Journal of Science and Technology (WJST) 16, no. 2 (July 22, 2018): 95–105. http://dx.doi.org/10.48048/wjst.2019.1301.

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One problem found within the process of software maintenance is that the size of the selected test cases is large. This causes the ability of the whole process of software-development life cycle to drop. Particularly, it may be time consuming and cause delays, and the cost may be expensive. The selection of test cases for software maintenance depends more on the criticality of fixing bugs than the criticality of avoiding programming errors. Therefore, selection methods are proposed, such as test-all, random, and regression selection. This includes Technique for Test Case Selection (TTCS) and the improvement of Test Case Selection (TCS). These techniques can provide better results, in particular, giving smaller sizes, reduction rates, and % problem-solving than traditional techniques. However, this paper proposes a new model, which is a combination of using the process of determining an appropriate number of selected test cases regarding TTCS, and TCS with testing-based selection, named the Hybridization Technique for Test Case Selection (HTTCS). Obviously, HTTCS can reduce the size of the selected test cases by about 96.86 - 98.83 %, which is better than TTCS and TCS, by about 0.29 - 16.51 %. Additionally, using HTTCS can increase the % problem-solving by up to 99.98 %, is which higher than others about at most 0.66 %.

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31

Abubakar, Shamsuddeen Muhammad, and Zahraddeen Sufyanu. "Comparisons of Filter, Wrapper and Embedded-Based Feature Selection Techniques for Consistency of Software Metrics Analysis." SLU Journal of Science and Technology 4, no. 1&2 (July 20, 2022): 188–204. http://dx.doi.org/10.56471/slujst.v4i.238.

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Identifying and selecting the most consistent subset of metrics which improves the performance of software defect prediction model is paramount but challenging problem as it receives little attention in literature. The current research aimed at investigating the consistency of subsets of metrics that are produced by embedded feature selection techniques. Ten (10) feature selection techniques used from the families of filter and wrapper-based feature selection techniques commonly used in the defect prediction domain. Ten (10) publicly available defect datasets were studied which span both proprietary and open source domains. SVM-RFE-RF presented 42-93% consistent metrics across datasets. While the prior study on non-Embedded produced 56.5% consistent metrics at median. SVM-RFE-LF approach of Embedded Feature Selection Technique produced 54-80% consistent metrics across datasets and 42.5% at median. To state the purpose of tittle has been achieved Embedded based Feature Selection Techniques produced most efficient consistent subset selection across the entire datasets and amongst the feature selection techniques as compared with counterpart filter and wrapper-based feature selection techniques

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32

Luo, Lai Jun, and Hai Ping Ren. "Wireless Sensor Network Link Selection Algorithm with Bayesian Technique." Advanced Materials Research 1078 (December 2014): 329–32. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.329.

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In wireless sensor networks, traditional link selection algorithm needs lots of data packages as testing samples, but the nodes of WSN are battery-powered, so the energy is extremely limited. To overcome this shortcoming, the aim of this paper is to propose three new link selection algorithms based the concept of Bayesian approach. Simulation results demonstrate that the three algorithms based on Bayesian approach have a higher success rate than empirical-algorithm by about 10 percent in selecting the highest quality link with the case of small samples. Among them, BSLA-EB has a good adaptability and it can get better experimental results.

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33

Robitaille, Nicolas, and Simon Duchesne. "Label Fusion Strategy Selection." International Journal of Biomedical Imaging 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/431095.

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Label fusion is used in medical image segmentation to combine several different labels of the same entity into a single discrete label, potentially more accurate, with respect to the exact, sought segmentation, than the best input element. Using simulated data, we compared three existing label fusion techniques—STAPLE, Voting, and Shape-Based Averaging (SBA)—and observed that none could be considered superior depending on the dissimilarity between the input elements. We thus developed an empirical, hybrid technique called SVS, which selects the most appropriate technique to apply based on this dissimilarity. We evaluated the label fusion strategies on two- and three-dimensional simulated data and showed that SVS is superior to any of the three existing methods examined. On real data, we used SVS to perform fusions of 10 segmentations of the hippocampus and amygdala in 78 subjects from the ICBM dataset. SVS selected SBA in almost all cases, which was the most appropriate method overall.

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34

Kelley, J. F., and J. Ukelson. "Coas: Combined Object-Action Selection: A Human Factors Experiment." Proceedings of the Human Factors Society Annual Meeting 36, no. 4 (October 1992): 316–20. http://dx.doi.org/10.1177/154193129203600412.

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12 participants with a high level of domain experience used two different, mouse-based, interaction techniques to carry out three workstation file management tasks of varying complexity. One technique followed a standard Object-Action model; the other was a newly developed technique called COAS (Combined Object-Action Selection). There was little difference in performance on a simple task; performance for participants using the new technique was 38% faster on a moderately complex task and was 21% faster on a complex task. The file management application, interaction techniques and experiment were implemented in an OS/2 Presentation Manager style using ITS (Interactive Transaction System).

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35

Wiradinata, Trianggoro, and Adi Suryaputra Paramita. "Clustering and Feature Selection Technique for Improving Internet Traffic Classification Using K-NN." Journal of Advances in Computer Networks 4, no. 1 (2016): 24–27. http://dx.doi.org/10.18178/jacn.2016.4.1.198.

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36

Kim, Jeong-Ho, Seok-Hwan Bae, Ki-Jin Kim, and Se-Jong Yoo. "Selection of radiation treatment plan technique at breast cancer operating technique." Journal of the Korea Safety Management and Science 17, no. 1 (March 31, 2015): 125–30. http://dx.doi.org/10.12812/ksms.2015.17.1.125.

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37

Laine, M., and J. Tamminen. "Aerosol model selection and uncertainty modelling by adaptive MCMC technique." Atmospheric Chemistry and Physics 8, no. 24 (December 19, 2008): 7697–707. http://dx.doi.org/10.5194/acp-8-7697-2008.

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Abstract. We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way. The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ). It uses Markov chain Monte Carlo (MCMC) technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use. We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.

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38

Erdin, Ceren, and Halil Emre Akbaş. "A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey." Sustainability 11, no. 14 (July 14, 2019): 3837. http://dx.doi.org/10.3390/su11143837.

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Multi-criteria decision-making (MCDM) techniques are increasingly being used for the problem of location selection, which directly affects the long-term success of a company. Besides these techniques, with the advantage of handling both spatial and non-spatial data, geographic information systems (GIS) also represent a useful method for selecting the appropriate location for different kinds of facilities and sites. In this respect, this study aims to compare the results of a MCDM technique, fuzzy technique for order preference by similarity to ideal solution (TOPSIS), and GIS for the location selection of shopping malls in Turkey. According to the results of both fuzzy TOPSIS and GIS, the Marmara region was determined as the best alternative for shopping malls in Turkey.

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39

Iwasokun, Gabriel Babatunde, Ayowole Oluwatayo Idowu, and Bamidele Moses Kuboye. "Fuzzification Technique for Candidate Rating and Selection." International Journal of Decision Support System Technology 14, no. 1 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/ijdsst.303944.

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The traditional ways of candidate selection and recruitment are prone to subjectivity, imprecision and vagueness. With a view to achieving objective and precise selection and recruitment while keeping up with technological improvement and changes, this paper discusses a fuzzification-based technique for candidate rating and selection. The technique comprises a fuzzy logic component that is an extension of Boolean logic and used for establishing accurate selection process and precise solutions to multi-variable problems. There is a knowledge base component which forms the database of multi-level information and rule base which composes a set of if-then statements for decision making. Its inference engine applies a pre-defined procedure on input from the rule base and fuzzy logic interfaces for final recommendations. The proposed methodology performs pre-defined procedures that are based on some input sets which stores multi-level information derived from several pre-specified scores. Results from the implementation of the proposed technique established its practical function.

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40

Malhotra, Ruchika, Arvinder Kaur, and Yogesh Singh. "A Regression Test Selection and Prioritization Technique." Journal of Information Processing Systems 6, no. 2 (June 30, 2010): 235–52. http://dx.doi.org/10.3745/jips.2010.6.2.235.

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41

Kim, Han Seong, Tae Howan Hong, and Yong Soo Cho. "A Cell Selection Technique Considering MIMO Precoding." Journal of Korean Institute of Communications and Information Sciences 37A, no. 12 (December 28, 2012): 1076–84. http://dx.doi.org/10.7840/kics.2012.37a.12.1076.

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42

Seetha, Hari, M. Narasimha Murty, and R. Saravanan. "Effective feature selection technique for text classification." International Journal of Data Mining, Modelling and Management 7, no. 3 (2015): 165. http://dx.doi.org/10.1504/ijdmmm.2015.071451.

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43

Sarkar, Chandrima, Sarah Cooley, and Jaideep Srivastava. "Robust Feature Selection Technique Using Rank Aggregation." Applied Artificial Intelligence 28, no. 3 (March 14, 2014): 243–57. http://dx.doi.org/10.1080/08839514.2014.883903.

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44

DeBacker, S., P. Kempeneers, W. Debruyn, and P. Scheunders. "A Band Selection Technique for Spectral Classification." IEEE Geoscience and Remote Sensing Letters 2, no. 3 (July 2005): 319–23. http://dx.doi.org/10.1109/lgrs.2005.848511.

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45

Mohamed, A., and G. B. Jasmon. "Voltage contingency selection technique for security assessment." IEE Proceedings C Generation, Transmission and Distribution 136, no. 1 (1989): 24. http://dx.doi.org/10.1049/ip-c.1989.0004.

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46

Miura, Motoki, and Kenji Saisho. "A Text Selection Technique Using Word Snapping." Procedia Computer Science 35 (2014): 1644–51. http://dx.doi.org/10.1016/j.procs.2014.08.257.

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Ahmad, Amir, and Lipika Dey. "A feature selection technique for classificatory analysis." Pattern Recognition Letters 26, no. 1 (January 2005): 43–56. http://dx.doi.org/10.1016/j.patrec.2004.08.015.

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Rothermel, Gregg, and Mary Jean Harrold. "A safe, efficient regression test selection technique." ACM Transactions on Software Engineering and Methodology 6, no. 2 (April 1997): 173–210. http://dx.doi.org/10.1145/248233.248262.

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Tin-Loi, F. "A constraint selection technique in limit analysis." Applied Mathematical Modelling 13, no. 7 (July 1989): 442–46. http://dx.doi.org/10.1016/0307-904x(89)90157-1.

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Almohamad, Tarik A., M. F. M. Salleh, and M. N. Mahmud. "Partner Selection Technique for Wireless Cooperative Communication." Wireless Personal Communications 77, no. 1 (November 10, 2013): 255–67. http://dx.doi.org/10.1007/s11277-013-1504-2.

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