Zobrazeno 1 - 10 of 34 pro vyhledávání: '"Panrasee Ritthipravat"'
1
Akademický článek
Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement
Autor: Potjanee Kanchanapiboon, Pitipat Tunksook, Prinya Tunksook, Panrasee Ritthipravat, Supatchai Boonpratham, Yodhathai Satravaha, Chaiyapol Chaweewannakorn, Supakit Peanchitlertkajorn
Publikováno v: Progress in Orthodontics, Vol 25, Iss 1, Pp 1-14 (2024)
Abstract Objectives This study aimed to assess the accuracy of machine learning (ML) models with feature selection technique in classifying cervical vertebral maturation stages (CVMS). Consensus-based datasets were used for models training and evalua
Externí odkaz: https://doaj.org/article/ddab0ebf532d4f9fb31f6558cb787852
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2
Akademický článek
Speed meets accuracy: Advanced deep learning for efficient Orientia tsutsugamushi bacteria assessment in RNAi screening
Autor: Potjanee Kanchanapiboon, Chuenchat Songsaksuppachok, Porncheera Chusorn, Panrasee Ritthipravat
Publikováno v: Intelligent Systems with Applications, Vol 22, Iss , Pp 200356- (2024)
This study investigates the use of advanced computer vision techniques for assessing the severity of Orientia tsutsugamushi bacterial infectivity. It uses fluorescent scrub typhus images obtained from molecular screening, and addresses challenges pos
Externí odkaz: https://doaj.org/article/23a90c30a1a94b6c8eeb0650e1a72835
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3
Akademický článek
Efficient and precise cell counting for RNAi screening of Orientia tsutsugamushi infection using deep learning techniques
Autor: Potjanee Kanchanapiboon, Chuenchat Songsaksuppachok, Porncheera Chusorn, Panrasee Ritthipravat
Publikováno v: Intelligent Systems with Applications, Vol 21, Iss , Pp 200304- (2024)
Acquiring fluorescent scrub typhus images obtained through RNA interference screening for the analysis of 60 different human genes and 18 control genes poses challenges due to nonuniform or clumped cells and variations in image quality, rendering ima
Externí odkaz: https://doaj.org/article/d6d790f4f78448769c0d7443506d2c8e
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4
Akademický článek
Development of a novel robot-assisted vocabulary learning system using pure synthetic data
Autor: Zaw Htet Aung, Chuenchat Songsaksuppachok, Potjanee Kanchanapiboon, Panrasee Ritthipravat
Publikováno v: Intelligent Systems with Applications, Vol 20, Iss , Pp 200295- (2023)
This study investigates the application of deep learning methods trained on synthetic data for the robust detection of vocabulary flashcards, an essential aspect of robot-assisted language learning (RALL). Despite its importance, flashcard detection
Externí odkaz: https://doaj.org/article/b2b83416294c4b4db9c8dcd6d4c43e25
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5
Akademický článek
Blended Engineering Design Process Learning Activities for Secondary School Students during COVID-19 Epidemic: Students’ Learning Activities and Perception
Autor: Monamorn Precharattana, Soonthareeya Sanium, Khemmawadee Pongsanon, Panrasee Ritthipravat, Suparat Chuechote, Worapan Kusakunniran
Publikováno v: Education Sciences, Vol 13, Iss 2, p 159 (2023)
This study aims to present the teaching and learning activities of Engineering Design Processes (EDP) to secondary school students. The proposed teaching technique used was blended learning, which integrated group activities based on online learning
Externí odkaz: https://doaj.org/article/3f4650e142e9488693bca2ede7787ef3
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9
A lightweight deep learning approach to mouth segmentation in color images
Autor: Kittisak Chotikkakamthorn, Panrasee Ritthipravat, Worapan Kusakunniran, Pimchanok Tuakta, Paitoon Benjapornlert
Publikováno v: Applied Computing and Informatics.
PurposeMouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation probl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::062210174c3260d782ab5358186c3851
https://doi.org/10.1108/aci-08-2022-0225
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Plný text Recenzováno Digitální knihovna AV ČR
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