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Journal articles on the topic 'Classification methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ghimire, Santosh. "On the Image Pixels Classification Methods." Journal of the Institute of Engineering 15, no. 2 (2019): 202–9. http://dx.doi.org/10.3126/jie.v15i2.27667.

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In this article, we first discuss about the images and image pixels classifications. Then we briefly discuss the importance of classification of images and finally focus on various methods of classification which can be implemented to classify image pixels.

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2

Crouch, R. J., and R. J. Blong. "Gully sidewall classification: methods and applications." Zeitschrift für Geomorphologie 33, no. 3 (1989): 291–305. http://dx.doi.org/10.1127/zfg/33/1989/291.

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3

Kubade, Harshad M. "The Overview of Bayes Classification Methods." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 2801–2. http://dx.doi.org/10.31142/ijtsrd15750.

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4

Hrytsiuk, Petro, Tetiana Babych, and Bohdan Krasko. "CLASSIFICATION METHODS OF THE YIELD FORECASTING." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (2022): 209–16. http://dx.doi.org/10.31891/2307-5732-2022-309-3-209-210.

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Analysis of wheat yields over the past 20 years shows that wheat yield are growing in all regions of Ukraine. This is a result of the growing investment attractiveness of the grain industry and the significant investments made in the industry. However, the upward trend is accompanied by significant fluctuations in wheat yields, which are mostly caused by weather and climatic factors. The most noticeable is the climatic factors influence for the steppe regions of Ukraine. The paper proposes a classification approach to forecasting wheat yield taking into account weather and climatic factors and

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Rasheed, Nishana, and Shreeja R. "Image Classification Methods." International Journal of Engineering Trends and Technology 8, no. 8 (2014): 461–64. http://dx.doi.org/10.14445/22315381/ijett-v8p279.

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6

Othman, Osama M., and Christopher H. Bryant. "Pruning Classification Rules with Instance Reduction Methods." International Journal of Machine Learning and Computing 5, no. 3 (2015): 187–91. http://dx.doi.org/10.7763/ijmlc.2015.v5.505.

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7

Pekhtasheva, Elena, Anatoly Neverov, Stefan Kubica, and Gennady Zaikov. "Classification of Biodamages, Evaluation and Protection Methods." Chemistry & Chemical Technology 6, no. 4 (2012): 459–72. http://dx.doi.org/10.23939/chcht06.04.459.

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8

Babaeva, Vasila. "Error Classification And Methods Of Their Correction." American Journal of Social Science and Education Innovations 02, no. 08 (2020): 474–77. http://dx.doi.org/10.37547/tajssei/volume02issue08-76.

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9

Perepelitsa, V. A., I. V. Kozin, and S. V. Kurapov. "Methods of classification and algorithms of graph coloring." Researches in Mathematics 16 (February 7, 2021): 135. http://dx.doi.org/10.15421/240816.

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We study the connection between classifications on finite set and the problem of graph coloring. We consider the optimality criterion for classification of special type: h-classifications, which are built on the base of proximity measure. It is shown that the problem of finding the optimal h-classification can be reduced to the problem of coloring of non-adjacency graph vertices by the smallest possible number of colors. We consider algorithms of proper coloring of graph vertices.

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10

WATADA, Junzo. "Methods for Fuzzy Classification." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 1 (1992): 61–73. http://dx.doi.org/10.3156/jfuzzy.4.1_61.

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11

Marini, Federico. "Classification Methods in Chemometrics." Current Analytical Chemistry 6, no. 1 (2010): 72–79. http://dx.doi.org/10.2174/157341110790069592.

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12

Benediktsson, J. A., and P. H. Swain. "Consensus theoretic classification methods." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 4 (1992): 688–704. http://dx.doi.org/10.1109/21.156582.

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13

Ekin, Oya, Peter L. Hammer, Alexander Kogan, and Pawel Winter. "Distance-Based Classification Methods." INFOR: Information Systems and Operational Research 37, no. 3 (1999): 337–52. http://dx.doi.org/10.1080/03155986.1999.11732388.

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14

Bischl, Bernd, Julia Schiffner, and Claus Weihs. "Benchmarking local classification methods." Computational Statistics 28, no. 6 (2013): 2599–619. http://dx.doi.org/10.1007/s00180-013-0420-y.

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15

Vrbanec, Tedo, and Ana Meštrović. "Taxonomy of academic plagiarism methods." Zbornik Veleučilišta u Rijeci 9, no. 1 (2021): 283–300. http://dx.doi.org/10.31784/zvr.9.1.17.

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The article gives an overview of the plagiarism domain, with focus on academic plagiarism. The article defines plagiarism, explains the origin of the term, as well as plagiarism related terms. It identifies the extent of the plagiarism domain and then focuses on the plagiarism subdomain of text documents, for which it gives an overview of current classifications and taxonomies and then proposes a more comprehensive classification according to several criteria: their origin and purpose, technical implementation, consequence, complexity of detection and according to the number of linguistic sour

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16

Селютін, Євгеній, and Ігор Козін. "FEATURES OF METAHEURISTIC METHODS." Молодий вчений, no. 2 (90) (February 26, 2021): 109–12. http://dx.doi.org/10.32839/2304-5809/2021-2-90-21.

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The essence of tasks and classification, as well as the necessity of fulfillment of conditions and relations on the set are analyzed. The properties of the equivalence relation are specified. As elementary fragments are analyzed all edges of the graph. The conditions of attachment of an edge are the next: this edge is a ray of an existing star or has no common vertices with already constructed stars of coverage. Emphasis is placed on the lack of optimality of such a solution. The aim of this work is to construct metaheuristics for finding a suboptimal classification defined by a tolerance rela

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17

Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprs-archives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recen

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18

Han, Seokwan, and Jinsoo Hwang. "Network Classification of P2P Traffic with Various Classification Methods." Korean Journal of Applied Statistics 28, no. 1 (2015): 1–8. http://dx.doi.org/10.5351/kjas.2015.28.1.001.

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19

Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprsarchives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recen

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20

Researcher. "EVALUATING ENSEMBLING METHODS FOR EYE STATE CLASSIFICATION." International Journal of Artificial Intelligence and Machine Learning (IJAIML) 3, no. 2 (2024): 204–10. https://doi.org/10.5281/zenodo.14010606.

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Eye state tracking is one of the well-researched problems. EEG is a frequently used sensor modality to capture the state of human cognition. This is also a widely studied problem in the field of Computer Vision. Tracking eye movements with the help of a camera may be obtrusive (camera placement may only sometimes be conducive in each environment). In such a scenario, approaches like EEG measurement are preferred to tracking eye movements using a camera. Since this is a non-intrusive approach (and painless), this is preferable. In this work, we explore a supervised classification approach to id

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21

H T, Bhavana, and Srikanth H T. "Pattern Classification Methods: A Survey." International Journal of Scientific and Research Publications (IJSRP) 9, no. 8 (2019): p9299. http://dx.doi.org/10.29322/ijsrp.9.08.2019.p9299.

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22

Moskvin, Leonid N. "A classification of separation methods." Vestnik of Saint Petersburg University. Physics. Chemistry 4(62), no. 2 (2017): 163–214. http://dx.doi.org/10.21638/11701/spbu04.2017.206.

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23

Gubarev, V. V., A. A. Gorshenkov, Yu N. Klikushin, and V. Yu Kobenko. "Classification measurements: Methods and implementation." Optoelectronics, Instrumentation and Data Processing 49, no. 2 (2013): 171–77. http://dx.doi.org/10.3103/s875669901302009x.

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24

Mohammed, Somia B., Ahmed Khalid, and SaifeEldin F. Osman. "A Survey of Classification Methods." International Journal of Advanced Engineering Research and Science 3, no. 10 (2016): 148–52. http://dx.doi.org/10.22161/ijaers/310.24.

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25

Petricas, A. Zh, D. V. Medvedev, and E. B. Olkhovskaya. "Classification of local anesthesia methods." Stomatologiya 95, no. 4 (2016): 4. http://dx.doi.org/10.17116/stomat20169544-9.

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26

Hu, Ruo, and Zan Fu Xie. "Classification of Knowledge Discovery Methods." Applied Mechanics and Materials 63-64 (June 2011): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.859.

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Knowledge Discovery, the science and technology of exploring knowledge in order to discover previously unknown patterns, is a part of the overall process of getting information in databases. In today’s computer-driven world, these databases contain a lot of information. The significant value of this information makes knowledge discovery a matter of considerable importance and necessity. A decision tree is a predictive model which can be used to represent both classifiers and regression models. When a decision tree is used for classification tasks, it is more appropriately referred to as a clas

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27

Khokar, N. "A Classification of Shedding Methods." Journal of the Textile Institute 90, no. 4 (1999): 570–79. http://dx.doi.org/10.1080/00405000.1999.10750054.

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28

Ştefan, Raluca-Mariana, Măriuţa Şerban, Iulian-Ion Hurloiu, and Bianca-Florentina Rusu. "Kernel Methods for Data Classification." International conference KNOWLEDGE-BASED ORGANIZATION 22, no. 3 (2016): 572–75. http://dx.doi.org/10.1515/kbo-2016-0098.

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Abstract In the past decades, the exponential evolution of data collection for macroeconomic databases in digital format caused a huge increase in their volume. As a consequence, the automatic organization and the classification of macroeconomic data show a significant practical value. Various techniques for categorizing data are used to classify numerous macroeconomic data according to the classes they belong to. Since the manual construction of some of the classifiers is difficult and time consuming, are preferred classifiers that learn from action examples, a process which forms the supervi

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29

FUKAGATA, Koji. "Classification of Flow Control Methods." Journal of the Society of Mechanical Engineers 115, no. 1127 (2012): 686–87. http://dx.doi.org/10.1299/jsmemag.115.1127_686.

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30

Moskvin, Leonid N. "A Classification of Separation Methods." Separation & Purification Reviews 45, no. 1 (2014): 1–27. http://dx.doi.org/10.1080/15422119.2014.897235.

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31

Bourel, M., and A. M. Segura. "Multiclass classification methods in ecology." Ecological Indicators 85 (February 2018): 1012–21. http://dx.doi.org/10.1016/j.ecolind.2017.11.031.

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32

Hartmann, Jochen, Juliana Huppertz, Christina Schamp, and Mark Heitmann. "Comparing automated text classification methods." International Journal of Research in Marketing 36, no. 1 (2019): 20–38. http://dx.doi.org/10.1016/j.ijresmar.2018.09.009.

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33

Borisova, Svetlana P., and Marina E. Тalikina. "Classification possibilities of testing methods." Vestnik of Samara University. Economics and Management 13, no. 3 (2022): 131–37. http://dx.doi.org/10.18287/2542-0461-2022-13-3-131-138.

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When carrying out entrepreneurial activities, it is especially important to assess the ability of counterparties to fulfill their contractual obligations. While the counterparty carries out its activities, there is always the possibility of settling emerging disagreements, but this possibility practically disappears in the event of bankruptcy of the counterparty. Despite a fairly large number of methods for determining the propensity of an entrepreneur to bankruptcy, they do not always give accurate results. In this regard, the issue of trust in these methods is important. To solve this proble

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34

Carvalho, Jhonnata Bezerra de, and Getúlio José Amorim do Amaral. "Classification methods for planar shapes." Expert Systems with Applications 151 (August 2020): 113320. http://dx.doi.org/10.1016/j.eswa.2020.113320.

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35

Gao, Yiting, Jiayi Li, and Xianghui Meng. "Methods for DDoS attacks classification." Applied and Computational Engineering 5, no. 1 (2023): 736–43. http://dx.doi.org/10.54254/2755-2721/5/20230686.

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At present, many people and companies are attacked by DDoS every day. DDoS attacks will make many large websites inoperable, which will not only affect people's normal use but also cause substantial economic losses. So far, many researchers have found different methods to prevent DDoS attacks, but there is no unified research on the classification of different types of DDoS attacks. If people are familiar with all common types of DDoS attacks and can successfully classify various DDoS attacks, they can take corresponding and effective precautionary measures to reduce risks and offer more prote

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36

Martyniuk, T. B., and B. I. Krukivskyi. "Classification Analysis of Sorting Methods." Visnyk of Vinnytsia Politechnical Institute 168, no. 3 (2023): 77–83. http://dx.doi.org/10.31649/1997-9266-2023-168-3-77-83.

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37

Barlow, Roger. "Event classification using weighting methods." Journal of Computational Physics 72, no. 1 (1987): 202–19. http://dx.doi.org/10.1016/0021-9991(87)90078-7.

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38

Panđa, Lovre, Dorijan Radočaj, and Rina Milošević. "Methods of Land Cover Classification Using Worldview-3 Satellite Images in Land Management." Tehnički glasnik 18, no. 1 (2024): 142–47. http://dx.doi.org/10.31803/tg-20221006135311.

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Abstract: Modern geoinformation technologies, such as remote sensing satellite missions and classification methods, are becoming increasingly prominent in land cover classification. Due to the emergence of high spatial resolution missions with improved temporal and spectral resolutions, such as Worldview-3, this approach enabled new possibilities in land management. To provide an in-depth analysis of such possibilities, this study reviews methods of land cover classification using WorldView-3 satellite imagery. With 29 different spectral channels and a spatial resolution of 1.2 m, Worldview-3

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39

Sydorenko Romanenko, V. D. "Classification of methods for detecting voids in rock massive." Jornal of Kryvyi Rih National University, no. 58 (2024): 142–48. http://dx.doi.org/10.31721/2306-5451-2024-1-58-142-148.

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40

Hutsul, Taras, Ivan Zhezhera, and Vladyslav Tkach. "FEATURES OF UAV CLASSIFICATION AND SELECTION METHODS." Technical Sciences and Technologies, no. 4(30) (2022): 201–12. http://dx.doi.org/10.25140/2411-5363-2022-4(30)-201-212.

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Unmanned aerialvehicles (UAVs) are being actively adopted in various areas of human activity, demonstrating excellent results. Their usage allows to minimize the risks of people being in high-risk conditions, and integration with various systems expands the range of functions that previously seemed impossible.There are currently over 1,500 different official UAV designs, and the number is growing rapidly. The effectiveness of choosing a specific model and its subsequent application for the specific tasks requires a clear classification. Domestic classification is mainly inherent in the militar

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41

Ahmad, Absar, Nafis Faizi, and Nazneen Aiman Siddiqui. "Revised Prasad and Kuppuswamy socioeconomic scales for India in 2024: Methods and Updates." Indian Journal of Community Health 36, no. 4 (2024): 598–603. http://dx.doi.org/10.47203/ijch.2024.v36i04.015.

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Socioeconomic scales are vital to public health research to measure associations and impacts on health. BG Prasad classification and the Kuppuswamy scales are two commonly used socio-economic scales for India as they are cost-effective and efficient. While BG Prasad classification is based on per capita family income, Kuppuswamy also measures education and occupation of the family head in addition to monthly family income. Since income is a dynamic entity affected by economic changes such as inflation and cost of consumption, there is a need to update the Prasad and Kuppuswamy classification.

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42

Chechnev, V. B. "Analysis and classification of multi-criteria decision-making methods." Ontology of designing 14, no. 4 (2024): 607–24. http://dx.doi.org/10.18287/2223-9537-2024-14-4-607-624.

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Decision-making requires in-depth analysis through the evaluation and comparison of various alternative solutions. These tasks are characterized by multiple criteria used for comparison. The variability of parameters and conditions in decision-making has led to the development of numerous methods aimed at facilitating this process effectively. Currently, several hundred multi-criteria decision-making methods are known, making it challenging to select the most appropriate one for practical use. Therefore, research on the analysis, classification, and systematization of multi-criteria decision-m

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43

SalerovaD, K., та A. SarsembayevA. "Қолданыстағы бейнелерді жіктеу әдістерін зерттеу". INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, № 6(6) (14 березня 2022): 100–104. http://dx.doi.org/10.54309/ijict.2022.2.6.012.

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Image classification is a topical issue in data science, which is applied in many areas. It is the process of getting classes of information from a multichannel bitmap using specific rules. There are three approaches to image classification - supervised, unsupervised and object- based. Support Vector Machines, Artificial Neural Network, Decision Tree, Convolutional Neural Network are the solution methods used for image classification. Choice of the classification approach depends on the way an analyst interacts with the computer. The whole process boils down to image stack acquisition, preproc

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44

Cerri, Ricardo, André Carlos P. L. F. de Carvalho, and Alex A. Freitas. "Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification." Intelligent Data Analysis 15, no. 6 (2011): 861–87. http://dx.doi.org/10.3233/ida-2011-0500.

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45

Xie, Shengkun, and Sridhar Krishnan. "Signal classification via multi-scale PCA and empirical classification methods." International Journal of Mechatronics and Automation 1, no. 3/4 (2011): 213. http://dx.doi.org/10.1504/ijma.2011.045253.

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46

Pushpa, M., and S. Karpagavalli. "Multi-label Classification: Problem Transformation methods in Tamil Phoneme classification." Procedia Computer Science 115 (2017): 572–79. http://dx.doi.org/10.1016/j.procs.2017.09.116.

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47

Agus, Eko Minarno, Septya Maulani Ayu, Kurniawardhani Arrie, Bimantoro Fitri, and Suciati Nanik. "Comparison of Methods for Batik Classification Using Multi Texton Histogram." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 1358–66. https://doi.org/10.12928/TELKOMNIKA.v16i3.7376.

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Batik is a symbol reflecting Indonesian culture which has been acknowledged by UNESCO since 2009. Batik has various motifs or patterns. Because most regions in Indonesia have their own characteristic of batik motifs, people find difficulties to recognize the variety of Batik. This study attempts to develop a system that can help people to classify Batik motifs using Multi Texton Histogram (MTH) for feature extraction. Meanwhile, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithm were employed for classification. The performance of those classifications is then compared to see

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48

Arkok, Bassam, and Akram M. Zeki. "Classification of Qur'anic topics based on imbalanced classification." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 678–87. https://doi.org/10.11591/ijeecs.v22.i2.pp678-687.

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Imbalanced classification techniques have been applied widely in the field of data mining. It is used to classify the imbalanced classes that are not equal in the number of samples. The problem of imbalanced classes is that the classification performance tends to the class with more samples while the class with few samples will obtain poor performance. This problem can be occurred in the Qur’anic classification due to the different in the number of verses. Many studies classified Qur’anic verses, which depended on the traditional classification. However, no study classified Qur&rsq

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49

D.E., Narbekov. "On Some Methods Of Economic Risk Management: Essence And Classifications." American Journal of Management and Economics Innovations 3, no. 06 (2021): 146–56. http://dx.doi.org/10.37547/tajmei/volume03issue06-22.

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The article notes that economic risk is one of the most multifaceted phenomena, the nature and components of which are not subject to a simple and unambiguous interpretation. But it should be noted that the classification of risks of this type, taking into account the many factors containing its signs is a rather voluminous problem due to the lack of a generally accepted systemic classification. The author argues that the above classification of methods for managing economic risks makes it possible to determine the characteristics used in their classification in the management process as a who

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50

Ferreira, Ana Alice Salmito Noleto de Campos, Leilane Rocha Barros Dourado, Daniel Biagiotti, Natanael Pereira da Silva Santos, Daphinne Cardoso Nagib Nascimento, and Katiene Regia Silva Sousa. "Methods for classifying coefficients of variation in experimentation with poultrys." Comunicata Scientiae 9, no. 4 (2019): 565–74. http://dx.doi.org/10.14295/cs.v9i4.2619.

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The aim of this work was to evaluate the coefficient of variation (CV) and to establish classification ranges for the main variables evaluated in experimentation with poultrys through different methods. The CV data of different response variables observed in poultry articles (broilers, laying hens, roosters and quails) published in five national journals were tabulated in a Microsoft Excel 2010® worksheet. Afterwards, they were subjected to normality evaluation through the Shapiro-Wilk method, and later the following data of the descriptive statistics were utilized: maximum value, minimum valu

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