https://ph05.tci-thaijo.org/index.php/JIIST/issue/feed 2024-11-09T09:43:16+07:00 Asst.Prof.Dr.Mahasak Ketcham maoquee@hotmail.com Open Journal Systems <p class="ng-star-inserted"><span class="ng-star-inserted">The <strong>Journal of Intelligent Informatics and Smart Technology (JIIST) (ISSN: 2586-9167)</strong> operates under the association of <strong>the</strong> <strong>Artificial Intelligence Association of Thailand (AIAT) and </strong><strong>the IEEE SMC Thailand Chapter</strong><strong>.</strong> It aims to be a publisher of a wide range of high-quality academic journals.</span></p> <p class="ng-star-inserted"><span class="ng-star-inserted">The journal welcomes original articles, review articles, short communications, and case reports in the field of artificial intelligence. This includes topics such as Natural Language Processing, Speech Recognition, Machine Learning, Image Processing, Robotics, Semantic Web, Intelligent Computer-Aided Instruction (ICAI), and Expert Systems, among other related fields. All articles are published in English.</span></p> https://ph05.tci-thaijo.org/index.php/JIIST/article/view/95 2024-05-28T09:29:50+07:00 Ei Thandar Phyu eiphyuycc@gmail.com Ye Kyaw Thu yktnlp@gmail.com Thazin Myint Oo queenofthazin@gmail.com Hutchatai Chanlekha fenghtc@ku.ac.th Thepchai Supnithi thepchai.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>

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