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38 results on '"Eiamkanitchat, Narissara"'

1. A Comparative Study on Scrum Simulations

Author

Ramingwong, Sakgasit, Ramingwong, Lachana, Cosh, Kenneth, Eiamkanitchat, Narissara, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Unger, Herwig, editor, Sodsee, Sunantha, editor, and Meesad, Phayung, editor

Published
2019
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23. Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic.

Author

Srijiranon, Krittakom, Eiamkanitchat, Narissara, Ramingwong, Sakgasit, Cosh, Kenneth, and Ramingwong, Lachana

Subjects
*DATA mining, *ENVIRONMENTAL sciences, *PARTICULATE matter, *AIR pollution, *COMPUTER science
Abstract

Coarse particulate matter (PM10), the inhalable particles with an aerodynamic diameter smaller than 10 micrometers are one of the major air pollutions that affect human health. Over the previous decade, a number of researchers applied various data mining techniques to create a temporal prediction model. This study reviews and discusses 100 research articles in computer science and environmental science coming from the Scopus database. The three processes of data mining techniques, including data preparation, model creation, and model evaluation for prediction PM10 are highlighted. A summary of the overall process directions of data mining as well as their output are revealed. Additionally, recommendations for future research are identified. This article is categorized under:Application Areas > Science and TechnologyTechnologies > Machine LearningTechnologies > Prediction [ABSTRACT FROM AUTHOR]

Published
2021
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28. On Feature Selection and Rule Extraction for High Dimensional Data: A Case of Diffuse Large B-Cell Lymphomas Microarrays Classification

Author

Eiamkanitchat, Narissara, Theera-Umpon, Nipon, and Auephanwiriyakul, Sansanee

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Article Subject
Abstract

Neurofuzzy methods capable of selecting a handful of useful features are very useful in analysis of high dimensional datasets. A neurofuzzy classification scheme that can create proper linguistic features and simultaneously select informative features for a high dimensional dataset is presented and applied to the diffuse large B-cell lymphomas (DLBCL) microarray classification problem. The classification scheme is the combination of embedded linguistic feature creation and tuning algorithm, feature selection, and rule-based classification in one neural network framework. The adjustable linguistic features are embedded in the network structure via fuzzy membership functions. The network performs the classification task on the high dimensional DLBCL microarray dataset either by the direct calculation or by the rule-based approach. The 10-fold cross validation is applied to ensure the validity of the results. Very good results from both direct calculation and logical rules are achieved. The results show that the network can select a small set of informative features in this high dimensional dataset. By a comparison to other previously proposed methods, our method yields better classification performance.

Published
2015
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