https://ph05.tci-thaijo.org/index.php/JIIST/issue/feedJournal of Intelligent Informatics and Smart Technology2024-11-09T09:43:16+07:00Chutima Beokhaimookchutima@rsu.ac.thOpen Journal Systems<p>We sincerely apologize for the incompleteness of the website's content. We are in the process of transferring information from the old website to this website. However, you can submit articles for publication with us on this website system as usual. Thank you for your understanding.</p> <p> </p> <p>Journal of Intelligent Informatics and Smart Technology aims to be a publisher of a wide range of high quality academic journals such as original articles, review article, short communication, and case report in the field of artificial intelligence including Natural Language Processing, Speech Recognition, Machine Learning, Image Processing, Robotics, Semantic Web, Intelligent Computer-Aided Instruction (ICAI), Expert Systems, and other related-fields.</p> <p>The articles are all published in English.</p>https://ph05.tci-thaijo.org/index.php/JIIST/article/view/95Myanmar Spelling Error Classification: An Empirical Study of Tsetlin Machine Techniques2024-05-28T09:29:50+07:00Ei Thandar Phyueiphyuycc@gmail.comYe Kyaw Thuyktnlp@gmail.comThazin Myint Ooqueenofthazin@gmail.comHutchatai Chanlekhafenghtc@ku.ac.thThepchai Supnithithepchai.supnithi@nectec.or.th<p>Accurate spelling and grammar checking is fundamental to the development of language tools for Myanmar language. Classifying spelling error types is crucial in spell checkers and other language processing tools because it enables more accurate and context-aware error corrections. This process categorizes spelling errors in written text into distinct types or categories. To address the lack of such resources for Myanmar language, we have developed a spelling corpus containing misspelled words alongside their corrected forms in a parallel structure, paired with a corpus categorizing types of spelling errors. This paper focuses on an observational study of the Tsetlin Machine for Myanmar spelling error type classification, involving comprehensive parameter tuning and a performance comparison with fastText, a state-of-the-art natural language processing model. Our studies indicate that while the Tsetlin Machine achieves comparable results to fastText specifically in the domain of phonetic error classification, it demonstrates lower efficacy in other error classes.</p>2024-11-09T00:00:00+07:00Copyright (c) 2024 Journal of Intelligent Informatics and Smart Technology