研究者業績
  1. 研究者リスト
  2. タッサネーチャロエンポーン タッサネー

タッサネーチャロエンポーン タッサネー

Thatsanee Charoenporn

基本情報

所属
武蔵野大学 データサイエンス学部 データサイエンス学科 教授
学位
Ph.D.(Sirindhorn International Institute of Technology, Thailand)
通称等の別名
Kaeng-SOM
J-GLOBAL ID
201901012821607047
researchmap会員ID
B000350703
In the age that information blows into us anywhere and anytime, it enables people to access across various sciences easily. The combination of science, arts and humanity can occur serenely. I believe that science and mind are compatible. My deep passion is to apply various academic aspects including truth together for the benefit and highest happiness of mankind. I therefore became a part of many important multidisciplinary project such as the Multi-lingual Machine Translation Project (1989-1995), Language Resource Development for Computer Science (ORCHID), E-Learning for Minor Languages, Information Literacy, Application of IOT for the elderly and Well-Being, Digital Cultural Information Development (Digitized Thailand), Digital Thinking, Social Entrepreneur, University Entrepreneur, Start up Promotion Support and so on.

I have an M.A. and B.A. in Arts and Linguistics and Ph.D. in Information Technology. All degrees and experiences from the real implemented projects granted me as part of a member of the National Electronics and Computer Technology Center, Thailand (1994-2014), Faculty of Informatics, Burapha University of Thailand (2014-2019). Currently, I am a member of the Faculty of Data Science, and Asia AI Institute of Musashino University, Japan.

My research interests include Language Resource Development, Semantics, Syntactic, Morphological Analysis, Machine Translation, Language Intermediate Representation, Natural Language Processing, Social Innovation, Business Model and Inspiration, Social Understanding,  Standardization on Heritage Information, and Cultural and Historic Digitization.

研究キーワード

 11

研究分野

 3

経歴

 10
もっとみる

学歴

 3

論文

 44
  • Takahiro Kubo, Virach Sornlertlamvanich, Thatsanee Charoenporn
    Frontiers in Artificial Intelligence and Applications 2025年3月17日  
    Nurses play a crucial role in healthcare, directly influencing the quality of patient care. Facing a global nursing shortage, there is an urgent need for strategies to enhance nursing efficiency and care quality. This foundational study explores an NLP-based approach to determine NANDA nursing diagnoses, leveraging both subjective and objective patient data recorded by nurses. Employing text data similarity analysis and a prototype predictive model, our research aims to refine the nursing assessment process and pave the way for the potential automation of nursing diagnoses. This work highlights the potential of AI to support nursing practices and sets a platform for future research to fully realize AI’s benefits in addressing the challenges posed by the nursing shortage.
  • Virach Sornlertlamvanich, Ryusei Doi, Thatsanee Charoenporn
    Frontiers in Artificial Intelligence and Applications 2025年3月17日  
    Challenged by data-driven AI limitations in reasoning and knowledge depth, this work presents a novel approach for enhanced conversational understanding. We leverage advanced text analysis to strategically extract key information from FAQs, then utilize AI-generated questions and robust semantic similarity metrics to significantly improve user query matching precision. Through the strategic integration of important sentence extraction in knowledge preparation, coupled with question generation and the application of semantic textual similarity measures, our model achieves a substantial improvement in user query matching precision. We propose a dual-system architecture—augmenting System 1 with additional knowledge akin to System 2 in human cognition. The methodology is exemplified through chatbot correction using FAQs, demonstrating the potential for human-like mind processing. Results showcase improved semantic understanding and reasoning, offering a promising path for advancing AI capabilities in conversational contexts.
  • Waranrach Viriyavit, Somrudee Deepaisarn, Thatsanee Charoenporn, Virach Sornlertlamvanich
    Frontiers in Artificial Intelligence and Applications 2025年3月17日  
    The increasing elderly population necessitates increased geriatric care. However, a shortage of caregivers leads to a risk of falls and bedsores in the elderly, both of which result in severe injuries. Whilst wearable devices, and vision sensors have been adopted for monitoring. However, these sensors come with limitations, impacting comfort and privacy for the elderly. To address these challenges, non-intrusive sensing devices integrated into the environment offer promising value for continuous elderly activity monitoring. This study uses a panel sensor embedded with four sensors, consisting of two piezoelectric sensors and two pressure sensors. It is placed beneath the mattress. The position classification encompasses five distinct positions: off-bed, sitting, lying in the center, lying on the left side, and lying on the right side. To find the best position for placing the panel, the positions of the panel and the combination of panel sensors positions are evaluated for five-bed positions classification. As a result, the best position for a sensor panel was in the middle of the bed (position No. 3), with an accuracy of 97.12%. This suggests the panel sensor should be placed at 123.5 cm, measured from the top of the bed. Moreover, in the case of placing two-panel sensors, the most effective arrangement comprises placing one-panel sensor placed at the the bed-top (position No. 1) and the other in the middle of the bed (position No. 3), yielding accuracy 99.93%.
  • Yuichi Tanigawa, Virach Sornlertlamvanich, Thatsanee Charoenporn
    2025年2月  査読有り
  • Kubo Takahiro, Virach Sornlertlamvanich, Thatsanee Charoenporn
    ECTI Transactions on Computer and Information Technology (ECTI-CIT) 19(1) 65-74 2024年11月30日  
    Nurses play a crucial role in healthcare, directly influencing patient care quality. With a global nursing shortage, enhancing nursing efficiency and care quality is urgently needed. This foundational study explores the advantages of text and data processing techniques to determine NANDA-I nursing diagnoses using both subjective and objective patient data recorded by nurses. By employing text data similarity analysis and a prototype of the predictive model, our research aims to rene the nursing assessment process and facilitate the automation of nursing diagnoses. This work highlights the accuracy of BERT-based assessment pattern matching to support nursing practices and sets a platform for future research to address the nursing shortage effectively.
もっとみる

MISC

 1

講演・口頭発表等

 21
もっとみる

担当経験のある科目(授業)

 12
もっとみる
一覧へ戻る