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Supaporn Kiattisin
Researcher at Mahidol University
Publications - 85
Citations - 399
Supaporn Kiattisin is an academic researcher from Mahidol University. The author has contributed to research in topics: Computer science & Enterprise architecture. The author has an hindex of 8, co-authored 73 publications receiving 269 citations. Previous affiliations of Supaporn Kiattisin include University of the Thai Chamber of Commerce & King Mongkut's University of Technology Thonburi.
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Journal ArticleDOI
A Comparison of Traditional and Neural Networks Forecasting Techniques for Container Throughput at Bangkok Port
TL;DR: This research provides the best application of forecasting technique which is Neural Network — Multilayer Perceptron technique for predicting container throughput at Bangkok Port.
Proceedings ArticleDOI
Stroke risk prediction model based on demographic data
TL;DR: Three classification algorithms: Decision Tree, Naive Bayes and Neural Network are used for predicting stroke which are model-based, superior to general statistics, and got a proper model for identification.
Journal ArticleDOI
Fishing Vessels Behavior Identification for Combating IUU Fishing: Enable Traceability at Sea
TL;DR: The novel concept of a combination of global and local features of trajectory data for fishing vessel behavior identification and enabling seafood transparency could solve the IUU Fishing and enable traceability at sea, including monitoring, maritime, and marine resources conservation systems.
Proceedings ArticleDOI
A match of X-ray teeth films using image processing based on special features of teeth
TL;DR: A match of X-ray teeth films using image processing based on special features of teeth will help the dental doctors to match simply a pair of teeth using thespecial features of the teeth films.
An Application of Neural Networks for Forecasting Container Throughput at Bangkok Port
TL;DR: In this article, the authors explore the use of neural network for predicting future container throughput at Bangkok Port, and the results are measured by root mean squared error (RMSE) and mean absolute error (MAE).