Praisan Padungweang
Orcid: 0000-0002-4358-7927
According to our database1,
Praisan Padungweang
authored at least 14 papers
between 2012 and 2021.
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Bibliography
2021
Proceedings of the 12th IEEE Annual Ubiquitous Computing, 2021
2019
J. Biomed. Informatics, 2019
2018
Logistic Principle Component Analysis (L-PCA) for Feature Selection in Classification.
Proceedings of the 14th International Conference on Natural Computation, 2018
Proceedings of the Tenth International Conference on Advanced Computational Intelligence, 2018
Stock Selection Using Association Rules on Cash Flow and Accrual Financial Indicators.
Proceedings of the 10th International Conference on Advances in Information Technology, 2018
2017
Proceedings of the 10th International Symposium on Visual Information Communication and Interaction, 2017
Automatic discovering success factor relationship entities in articles using named entity recognition.
Proceedings of the 9th International Conference on Knowledge and Smart Technology, 2017
Dog cough sound classification using artificial neural network and the selected relevant features from discrete wavelet transform.
Proceedings of the 9th International Conference on Knowledge and Smart Technology, 2017
Proceedings of the 14th International Joint Conference on Computer Science and Software Engineering, 2017
2016
Proceedings of the Neural Information Processing - 23rd International Conference, 2016
Data mining approach for automatic discovering success factors relationship statements in full text articles.
Proceedings of the Eighth International Conference on Advanced Computational Intelligence, 2016
Semi-automatic construction of thyroid cancer intervention corpus from biomedical abstracts.
Proceedings of the Eighth International Conference on Advanced Computational Intelligence, 2016
2015
TNorm: An Unsupervised Batch Effects Correction Method for Gene Expression Data Classification.
Proceedings of the Neural Information Processing - 22nd International Conference, 2015
2012
A Discrimination Analysis for Unsupervised Feature Selection via Optic Diffraction Principle.
IEEE Trans. Neural Networks Learn. Syst., 2012