Name: Songpol Eiamsam-ang
Position: Scientist
Department: Medical Entomology
Charoenpanyakul, R., Kittichai, V., Eiamsamang, S. et al. Enhancing mosquito classification through self-supervised learning. Sci Rep 14, 27123 (2024). https://doi.org/10.1038/s41598-024-78260-2
Eiamsamang S, Chuwongin S, Promma P, Samung Y, Saeung A, Chaisiri K, Chareonviriyaphap T, Sriwichai P. Deep learning technology for field-based mosquito vector identification. JITMM Proc. 2024;12:36-50.
Myat Su Yin, Gunatilaka D, Assawavinijkulchai K, Thampakorn T, Heesawat W, Haddawy P, Eiamsamang S, Sriwichai P, Weber M. MosquitoSongSense: IoT-based mosquito wingbeat data collection system. In: Proceedings of the 2023 ACM Conference on Information Technology for Social Good (GoodIT '23). New York, NY: Association for Computing Machinery; 2023. p. 285–90. doi:10.1145/3582515.3609546.
Eiamsamang S, Chuwongin S, Promma P, Samung Y, Saeuang A, Chaisiri K, Sriwichai P. Optimization and improving stability of deep learning technology for field-based mosquito identification contributing to vector surveillance monitoring. Presented at: The 5th International Symposium of Benthological Society of Asia; 2022; Chiang Mai University, Thailand.
>Eiamsamang S, Chuwongin S, Promma P, Samung Y, Saeuang A, Chaisiri K, Sriwichai P. Optimization and improving stability of deep learning technology for field-based mosquito identification contributing to vector surveillance monitoring. Presented at: The 5th International Symposium of Benthological Society of Asia; 2022; Chiang Mai University, Thailand.