Curriculum Vitaes

Virach Sornlertlamvanich

  (ソンラートラムワニッチ ウィラット)

Profile Information

Affiliation
Professor (Professor), Department of Data Science, Musashino University
Department of Engineering, Thammasat University
Degree
Ph.D.(Sep, 1998, Tokyo Institute of Technology)

Contact information
virachgmail.com
ORCID ID
 https://orcid.org/0000-0002-6918-8713
J-GLOBAL ID
201901006918149809
researchmap Member ID
B000354731

External link

In 2003, he achieved the “National Distinguished Researcher Award” in Information Technology and Communication from the National Research Council of Thailand, following by the “ASEAN Outstanding Engineering Achievement Award” from ASEAN Federation of Engineering Organizations (AFEO) in 2011. He was also esteemed “The Researcher of the Year 2001” by the Nation Newspaper in 2001. He started his research career in the field of Knowledge Engineering and Artificial Intelligence during his study in Kyoto University in 1980-1986. He started his research in Natural Language Processing by participating in the Multi-lingual Machine Translation project during 1988-1995, and received his Ph.D. from Tokyo Institute of Technology in 1998. Some of his long-running research contributions can be seen in the initiative in the development of Thai POS tagged corpus (ORCHID, 1997), the first corpus based Thai-English dictionary (LEXiTRON, 1997), and the first English-Thai online machine translation web service (ParSit, 2000) based on the Inter-lingual approach. His recent efforts are on the research and development of the technologies for digital content creation and understanding. He proposed the Digitized Thailand project in 2009 to establish an intelligent service platform for being a fundamental framework for digital content sharing and application mashup. Some of the achievements have already been publicized in culture and local wisdom digitization and the applications on the digital content services for tourism, product design and education. His research interest includes Natural Language Processing, Human Language Technology, Information Retrieval, Data Mining, Artificial Intelligence, Machine Learning, Deep Learning, Social Media Analytics and the related fields.

Education

 3

Papers

 134
  • Midhu Jean Joseph, Virach Sornlertlamvanich, Thatsanee Charoenporn
    Proceedings of the 16th International Conference on Knowledge and Smart Technology (KST2024), Feb, 2024  Peer-reviewedLast author
  • Naoki Tomizawa, Virach Sornlertlamvanich, Thatsanee Charoenporn
    Proceedings of the 16th International Conference on Knowledge and Smart Technology (KST2024), 173-177, Feb, 2024  Peer-reviewedLast author
  • Takafumi Nakanishi, Ayako Minematsu, Ryotaro Okada, Osamu Hasegawa, Virach Sornlertlamvanich
    Frontiers in Artificial Intelligence and Applications, Information Modelling and Knowledge Bases, XXXV 227-238, Jan 16, 2024  Peer-reviewed
  • Virach Sornlertlamvanich, Thatsanee Charoenporn, Somrudee Deepaisarn
    Frontiers in Artificial Intelligence and Applications, 380 179-193, Jan 16, 2024  Peer-reviewedLead authorCorresponding author
    The Thammasat AI City distributed platform is a proposed AI platform designed to enhance city intelligent management. It addresses the limitations of current smart city architecture by incorporating cross-domain data connectivity and machine learning to support comprehensive data collection. In this study, we delve into two main areas, that is, monitoring and visualization of city ambient lighting, and indoor human physical distance tracking. The smart street light monitoring system provides real-time visualization of street lighting status, energy consumption, and maintenance requirement, which helps to optimize energy consumption and maintenance reduction. The indoor camera-based system for human physical distance tracking can be used in public spaces to monitor social distancing and ensure public safety. The overall goal of the platform is to improve the quality of life in urban areas and align with sustainable urban development concepts.
  • Somrudee Deepaisarn, Paphana Yiwsiw, Chanon Tantiwattanapaibul, Suphachok Buaruk, Virach Sornlertlamvanich
    Journal of information and communication convergence engineering, 1-5, Sep 30, 2023  Peer-reviewedLast author
  • Somrudee Deepaisarn, Paphana Yiwsiw, Chanon Tantiwattanapaibul, Suphachok Buaruk, Virach Sornlertlamvanich
    JICCE, 21(3), Sep, 2023  Peer-reviewedLast authorCorresponding author
  • Somrudee Deepaisarn, Sirawit Chokphantavee, Sorawit Chokphantavee, Phuriphan Prathipasen, Suphachok Buaruk, Virach Sornlertlamvanich
    Scientific Reports, 13(13228), Aug, 2023  Peer-reviewedLast authorCorresponding author
  • Virach SORNLERTLAMVANICH, Thatsanee CHAROENPORN, Somrudee DEEPAISARN
    International Conference on Information Modelling and Knowledge Bases (EJC2023), 239-258, Jun, 2023  Peer-reviewedLead authorCorresponding author
  • Virach Sornlertlamvanich, Sunisa Wittayapanyanon (Saito
    International Conference on Business and Industrial Research (ICBIR2023), 620-626, May, 2023  Peer-reviewedLead authorCorresponding author
  • Somrudee Deepaisarn, Paphana Yiwsiw, Sirada Chaisawat, Thanakit Lerttomolsakul, Leeyakorn Cheewakriengkrai, Chanon Tantiwattanapaibul, Suphachok Buaruk, Virach Sornlertlamvanich
    Sensors, 23(4) 1853-1853, Feb 7, 2023  Peer-reviewedLast authorCorresponding author
    The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.
  • Hiroo Iwata, Shiori Sasaki, Naoki Ishibashi, Virach Sornlertlamvanich, Yuki Enzaki, Yasushi Kiyoki
    Frontiers in Artificial Intelligence and Applications, Jan 23, 2023  
    This paper describes about project “Data Sensorium” launched at the Asia AI Institute of Musashino University. Data Sensoriumis a conceptual framework of systems providing physical experience of content stored in database. Spatial immersive display is a key technology of Data Sensorium. This paper introduces prototype implementation of the concept and its application to environmental and architectural dataset.
  • Takafumi Nakanishi, Ayako Minematsu, Ryotaro Okada, Osamu Hasegawa, Virach Sornlertlamvanich
    Frontiers in Artificial Intelligence and Applications, Jan 23, 2023  
    Through technology, it is essential to seamlessly bridge the divide between diverse speaking communities (including the signer (the sign language speaker) community). In order to realize communication that successfully conveys emotions, it is necessary to recognize not only verbal information but also non-verbal information. In the case of signers, there are two main types of behavior: verbal behavior and emotional behavior. This paper presents a sign language recognition method by similarity measure with emotional expression specific to signers. We focus on recognizing the sign language conveying verbal information itself and on recognizing emotional expression. Our method recognizes sign language by time-series similarity measure on a small amount of model data, and at the same time, recognizes emotion expression specific to signers. Our method extracts time-series features of the body, arms, and hands from sign language videos and recognizes them by measuring the similarity of the time-series features. In addition, it recognizes the emotional expressions specific to signers from the time-series features of their faces.
  • Virach Sornlertlamvanich, Pawinee Iamtrakul, Teerayuth Horanont, Narit Hnoohom, Konlakorn Wongpatikaseree, Sumeth Yuenyong, Jantima Angkapanichkit, Suthasinee Piyapasuntra, Prittipoen Lopkerd, Santirak Prasertsuk, Chawee Busayarat, I-soon Raungratanaamporn, Somrudee Deepaisarn, Thatsanee Charoenporn
    Frontiers in Artificial Intelligence and Applications, 364 92-109, Jan 23, 2023  Peer-reviewedLead authorCorresponding author
    This research proposes an AI platform for data sharing across multiple domains. Since the data in the smart city concept are domain-specific processed, the existing smart city architecture is suffered from cross-domain data interpretation. To go beyond the digital transformation efforts in smart city development, the AI city is created on the architecture of cross-domain data connectivity and transform learning in the machine learning paradigm. In this research, the health and human behavioral data are targeted on human traceability and contactless technologies. To measure the inhabitants quality of life (QoL), the primary emotion expression study is conducted to interpret the emotional states and the mental health of people in the urbanized city. The results of information augmentation draw attention to the immersive visualization of the Thammasat model.
  • Virach Sornlertlamvanich, Thatsanee Charoenporn, Shiori Sasaki, Yasushi Kiyoki
    IIAI-AAI-Winter, 225-229, Dec, 2022  Peer-reviewedLead authorCorresponding author
  • Somrudee Deepaisarn, Angkoon Angkoonsawaengsuk, Charn Arunkit, Chayud Srisumarnk, Krongkan Nimmanwatthana, Nanmanas Linphrachaya, Nattapol Chiewnawintawat, Rinrada Tanthanathewin, Sivakorn Seinglek, Suphachok Buaruk, Virach Sornlertlamvanich
    APSIPA Annual Summit and Conference, Nov 7, 2022  Peer-reviewedLast author
  • Somrudee Deepaisarn, Suphachok Buaruk, Sirawit Chokphantavee, Sorawit Chokphantavee, Phuriphan Prathipasen, Virach Sornlertlamvanich
    2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 1-5, Nov 5, 2022  Peer-reviewedLast author
  • Sakada Sao, Virach Sornlertlamvanich
    Journal of Science & Technology Asia (STA), 27(3), Sep, 2022  Peer-reviewedLast authorCorresponding author
  • Taiki Kimura, Thatsanee Charoenporn, Virach Sornlertlamvanich
    2022 International Electronics Symposium (IES), 162-165, Aug 9, 2022  Peer-reviewedLast author
  • Virach Sornlertlamvanich, Kitiya Suriyachay, Thatsanee Charoenporn
    Human Language Technology. Challenges for Computer Science and Linguistics, 13212 143-160, Jun, 2022  Peer-reviewedLead authorCorresponding author
  • Sumeth Yuenyong, Virach Sornlertlamvanich
    Journal of Songklanakarin Journal of Science and Technology (SJST), Jun, 2022  Peer-reviewedLast author
  • Virach Sornlertlamvanich, Sumeth Yuenyong
    IEEE Access, 10 53043-53052, May 16, 2022  Peer-reviewedLead authorCorresponding author
  • Virach Sornlertlamvanich, Hiroki Nomoto, Sunisa Wittayapanyanon (Saito, Atsushi Kasuga, Kenji Okano, Wataru Okubo, Yunjin Nam, Yoshimi Miyake, Thuzar Hlaing, Ryuko Taniguchi, Sri Budi Lestari
    Proceedings of the 7th International Conference on Business and Industrial Research (ICBIR2022), May, 2022  Peer-reviewedLead authorCorresponding author
  • Ryusei Doi, Thatsanee Charoenporn, Virach Sornlertlamvanich
    Proceedings of the 7th International Conference on Business and Industrial Research (ICBIR2022), May, 2022  Peer-reviewedCorresponding author
  • Ryosuke Yamano, Thatsanee Charoenporn, Virach Sornlertlamvanich
    Proceedings of the 7th International Conference on Business and Industrial Research (ICBIR2022), May, 2022  Peer-reviewedCorresponding author
  • I-soon Raungratanaamporn, Jirawan Klaylee, Sararad Chayaphong, Virach Sornlertlamvanich
    Proceedings of the 7th International Conference on Business and Industrial Research (ICBIR2022), May, 2022  Peer-reviewedLast author
  • Jirawan Klaylee, Pawinee Iamtrakul, Virach Sornlertlamvanich
    Proceedings of the 2nd International Civil Engineering and Architecture Conference (CEAC2022), Mar, 2022  Peer-reviewedLast author
  • Takayuki Take, Thatsanee Charoenporn, Virach Sornlertlamvanich
    DEIM2022, Feb, 2022  Last author
  • Takuma Nitta, Shinpei Hagimoto, Ari Yanase, Ryotaro Okada, Virach Sornlertlamvanich, Takafumi Nakanishi
    International Journal of Smart Computing and Artificial Intelligence, 6(1) 1-1, 2022  
  • Narit Hnoohom, Pitchaya Chotivatunyu, Nagorn Maitrichit, Virach Sornlertlamvanich, Anuchit Jitpattanakul, Sakorn Mekruksavanich
    Proceedings of the 25th International Computer Science and Engineering Conference (ICSEC), Nov, 2021  Peer-reviewed
  • Narit Hnoohom, Nagorn Maitrichit, Pitchaya Chotivatunyu, Virach Sornlertlamvanich, Anuchit Jitpattanakul, Sakorn Mekruksavanich
    Proceedings of the 25th International Computer Science and Engineering Conference (ICSEC), Nov, 2021  Peer-reviewed
  • Xuanzhou Yang, Virach Sornlertlamvanich
    International Electronics Symposium (IES), Sep, 2021  Peer-reviewedLast author
  • Goragod Pongthanisorn, Waranrach Viriyavit, Thatsanee Charoenporn, Virach Sornlertlamvanich
    International Conference on Information Modelling and Knowledge Bases (EJC2021), Sep, 2021  Peer-reviewedCorresponding author
  • Kitiya Suriyachay, Thatsanee Charoenporn, Virach Sornlertlamvanich, Natsuda Kaothanthong
    Journal of Science & Technology Asia (STA), 26(2) 61-78, Jul, 2021  Peer-reviewedCorresponding author
  • Shinpei Hagimoto, Takuma Nitta, Ari Yanase, Takafumi Nakanishi, Ryotaro Okada, Virach Sornlertlamvanich
    Proceedings of 19th IADIS International Conference e-Society 2021, Mar, 2021  Peer-reviewedLast author
  • Takuma Nitta, Shinpei Hagimoto, Ari Yanase, Takafumi Nakanishi, Ryotaro Okada, Virach Sornlertlamvanich
    IEEE/IIAI INTERNATIONAL CONGRESS ON APPLIED INFORMATION TECHNOLOGY (IIAI AIT 2020), Dec, 2020  Peer-reviewedLast author
  • Rintaro Nakahodo, Virach Sornlertlamvanich
    The 5th International Conference on Information Technology (InCIT 2020), 156-161, Oct, 2020  Peer-reviewedLast author
  • G. Pongthanisorn, W. Viriyavit, T. Prakayapan, S. Deepaisarn, V. Sornlertlamvanich
    International Electronics Symposium (IES), Sep, 2020  Peer-reviewedLast authorCorresponding author
  • Waranrach Viriyavit, Virach Sornlertlamvanich
    Journal of Sensors, 2020 1-14, Jan 31, 2020  Peer-reviewedCorresponding author
  • Waranrach Viriyavit, Virach Sornlertlamvanich
    Information Modelling and Knowledge Bases XXXI, 222-237, 2020  Peer-reviewedCorresponding author
  • Ari Yanase, Virach Sornlertlamvanich, Thatsanee Charoenporn
    International Conference Language Technologies for All (LT4All), Dec, 2019  Peer-reviewedLast author
  • Thatsanee Charoenporn, Virach Sornlertlamvanich
    International Conference Language Technologies for All (LT4All), Dec, 2019  Peer-reviewedLast author
  • Virach Sornlertlamvanich, Nannam Aksorn, Thatsanee Charoenporn
    International Conference Language Technologies for All (LT4All), Dec, 2019  Peer-reviewedLead authorCorresponding author
  • Sakada Sao, Virach Sornlertlamvanich
    Proceedings of 2019 4th International Conference on Information Technology: Encompassing Intelligent Technology and Innovation Towards the New Era of Human Life, InCIT 2019, 28-31, Oct, 2019  Peer-reviewed
    © 2019 IEEE. This paper describes an approach for bed position classification by using 2 stacked layers of Long Short-Term Memory approach. The data is collected from the sensor panel which consists of 2 types of sensors, i.e. piezoelectric and pressure sensors. The raw data has been classified into 5 classes. It also has to go through the min-max scaling normalization on a fixed range between 0 and 1. The data is assembled to fit a one-second interval of the 30Hz sensor sampling rate. The model has been experimented by changing the number of hidden nodes of the model in 128, 80 and 50 nodes. The result is 91.70% of accuracy which is good enough comparing to the previous works.
  • Virach Sornlertlamvanich, Panuwat Assawinjaipetch, Kiyoaki Shirai, Sanparith Marukatat
    Sep, 2019  Peer-reviewedCorresponding author
  • Phat Jotikabukkana, Virach Sornlertlamvanich
    Information Modelling and Knowledge Bases XXXI - Proceedings of the 29th International Conference on Information Modelling and Knowledge Bases(EJC), 363-380, 2019  Peer-reviewed
  • Kitiya Suriyachay, Virach Sornlertlamvanich
    ICAICTA 2018 - 5th International Conference on Advanced Informatics: Concepts Theory and Applications, 30-35, Nov 20, 2018  Peer-reviewedCorresponding author
    © 2018 IEEE. In the Thai language, named entity can be used with or without a prefix or an indication of word. This may cause confusion between named entity and other types of noun. However, a named entity is likely to be used in adjacent to verbs or prepositions. This means that the adjacent verbs or prepositions to a noun can be as a good feature to determine the type of named entity. There are some studies on named entity recognition (NER) task in other languages such as Indonesian showing that combination of word embedding and part-of-speech (POS) tag can improve the performance of the NER model. In this paper, we investigate the Thai Named Entity Recognition task using Bi-LSTM model with word embedding and POS embedding for dealing with the relatively small and disjointedly labeled corpus. We compare our model with the one without POS tag, and the baseline model of CRF with the similar set of feature. The experiment results show that our proposed model outperforms the other two in all F1-score measures. Especially, in the case of location file, the F1-score is increased by 14 percent.
  • Tran Sy Bang, Virach Sornlertlamvanich
    IEICE Transactions on Information and Systems, E101D(4) 909-916, Apr, 2018  Peer-reviewedLast author
    Copyright © 2018 The Institute of Electronics, Information and Communication Engineers. This paper presents a supervised method to classify a document at the sub-sentence level. Traditionally, sentiment analysis often classifies sentence polarity based on word features, syllable features, or Ngram features. A sentence, as a whole, may contain several phrases and words which carry their own specific sentiment. However, classifying a sentence based on phrases and words can sometimes be incoherent because they are ungrammatically formed. In order to overcome this problem, we need to arrange words and phrase in a dependency form to capture their semantic scope of sentiment. Thus, we transform a sentence into a dependency tree structure. A dependency tree is composed of subtrees, and each subtree allocates words and syllables in a grammatical order. Moreover, a sentence dependency tree structure can mitigate word sense ambiguity or solve the inherent polysemy of words by determining their word sense. In our experiment, we provide the details of the proposed subtree polarity classification for sub-opinion analysis. To conclude our discussion, we also elaborate on the effectiveness of the analysis result.
  • Waranrach Viriyavit, Virach Sornlertlamvanich, Waree Kongprawechnon, Panita Pongpaibool
    Information Modelling and Knowledge Bases XXIX, 301 383-394, 2018  Peer-reviewedCorresponding author
    © 2018 The authors and IOS Press. All rights reserved. This study proposes bed posture classification using a Neural Network and a Bayesian Network for elderly care. The data are collected in a hospital. The on-bed postures are analyzed into five types, those are, out of bed, sitting, lying down, lying left, and lying right, by using signals from a sensor panel (composed of piezoelectric sensors and pressure sensors). The sensor panel is placed under a mattress in the thoracic area. To eliminate the effect of weight and the bias between different types of sensors, the sensing data are normalized into a range of 0 to 1 by the unity-based normalization (or feature scaling) method. In addition, a Bayesian Network is adopted to estimate the likelihood of consecutive postures. The results from both a Neural Network and Bayesian Network estimation are combined by the weighted arithmetic mean. The experimental results yield the maximum accuracy of posture classification when the coefficient of Bayesian probability and a Neural Network are set to 0.7 and 0.3 respectively.
  • Htet Htet Htun, Virach Sornlertlamvanich
    Information Modelling and Knowledge Bases XXIX, 301 373-382, 2018  Peer-reviewedLast author
    © 2018 The authors and IOS Press. All rights reserved. In the last few years, Concept Similarity Measures (CSMs) become important for the biomedical ontologies in order to find adaptable treatments from the conceptually similar diseases. For the ontology primitive concepts, they are not fully defined in the ontology so taxonomical path-based similarity measure cannot give the correct similarity for primitive concepts. In this paper, we propose a new primitive concept name similarity measure based on natural language processing to get a better result in concept similarity measure in terms of noun phrase construction analysis. We conduct experiments on the standard clinical ontology SNOMED CT and make comparison between taxonomical path-based measure and our proposed similarity measure against human expert results in order to prove our proposed similarity measure can outperform the existing approaches for primitive concept similarity.
  • Virach Sornlertlamvanich, Petchporn Chawakitchareon, Aran Hansuebsai, Chawan Koopipat, Bernhard Thalheim, Yasushi Kiyoki, Hannu Jaakkola, Naofumi Yoshida
    Information Modelling and Knowledge Bases XXIX, 27th International Conference on Information Modelling and Knowledge Bases (EJC 2017), Krabi, Thailand, June 5-9, 2017., 301, 2018  Peer-reviewedLead authorCorresponding author

Misc.

 11

Books and Other Publications

 5

Research Projects

 6

教育内容・方法の工夫

 4
  • Subject
    Information Retrieval
    Date(From)
    2015/01/01
    Date(To)
    2019/03/31
    Summary
    Basic and advanced techniques for text-based information systems: efficient text indexing; Boolean and vector space retrieval models; evaluation and interface issues; Web search including crawling, link-based algorithms, and Web metadata; text/Web clustering, classification; text mining.
  • Subject
    Management Information System
    Date(From)
    2014/08/01
    Date(To)
    2018/08/31
    Summary
    Management Information System explores the use of information systems in today's organizations. This is an exciting field because of the degree of change occurring in technology and how that translates into new opportunities for management and business process. Knowledge about information systems is essential for creating successful, competitive firms, for managing global corporations, for adding business value, and for providing useful products and services to customers. Throughout the course, case studies are provided to illustrate how organizations use IT to manage their businesses. The main topics covered in the course include
    l organizations,management,andthenetworkedenterprise
    l informationtechnology,infrastructure,platforms,andtelecommunications l systemsdevelopmentandmanagement,managingglobalsystems
    l applicationsforthedigitalfirm,includinge-businessande-commerce.
  • Subject
    Big Data Analytics
    Date(From)
    2018/01/01
    Date(To)
    2018/05/31
    Summary
    The recent explosion of social media, IOT technology, and the computerization of every aspect of economic activity resulted in the creation of big data. It is estimated that 80% and more of the data is in the unstructured form, in parallel with the development of computer which has kept getting ever more powerful and storage ever cheaper. Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits. This course brings together several key information technologies in Big Data, AI, Machine Learning and Deep Learning to use in manipulating, storing, and analyzing big data.
  • Subject
    Creative Thinking
    Date(From)
    2013/06/01
    Date(To)
    2018/06/30
    Summary
    Creative thinking and designing tools. The ways to innovation and problem solving.