Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Marukatat, Sanparith"'
Autor:
Kiratiratanapruk, Kantip, Temniranrat, Pitchayagan, Sinthupinyo, Wasin, Marukatat, Sanparith, Patarapuwadol, Sujin
Fast, accurate and affordable rice disease detection method is required to assist rice farmers tackling equipment and expertise shortages problems. In this paper, we focused on the solution using computer vision technique to detect rice diseases from
Externí odkaz:
http://arxiv.org/abs/2206.07344
Autor:
Chumpu, Romrawin, Chu, Chun-Lin, Treeratanaphitak, Tanyakarn, Marukatat, Sanparith, Hsu, Shu-Han
Publikováno v:
In Engineering Applications of Artificial Intelligence November 2023 126 Part B
Akademický článek
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Publikováno v:
2018 11th Biomedical Engineering International Conference (BMEiCON)
Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide more medical information than no
Externí odkaz:
http://arxiv.org/abs/1808.10858
This short paper provides further details of the Sloth Search System, which was developed by the NECTEC team for the Video Browser Showdown (VBS) 2018.
Comment: Final note paper about the Sloth Search System at the VBS 2018 competition
Comment: Final note paper about the Sloth Search System at the VBS 2018 competition
Externí odkaz:
http://arxiv.org/abs/1806.01081
Counterfactual inference has become a ubiquitous tool in online advertisement, recommendation systems, medical diagnosis, and econometrics. Accurate modeling of outcome distributions associated with different interventions -- known as counterfactual
Externí odkaz:
http://arxiv.org/abs/1805.08845
Autor:
Siriwong, Nanicha, Sukaram, Thanikan, Tansawat, Rossarin, Apiparakoon, Terapap, Tiyarattanachai, Thodsawit, Marukatat, Sanparith, Rerknimitr, Rungsun, Chaiteerakij, Roongruedee
Publikováno v:
In Liver Research September 2022 6(3):191-197
Publikováno v:
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 5, September 2014
The paper presents a novel technique called "Structural Crossing-Over" to synthesize qualified data for training machine learning-based handwriting recognition. The proposed technique can provide a greater variety of patterns of training data than th
Externí odkaz:
http://arxiv.org/abs/1412.6018
Autor:
Inkeaw, Papangkorn, Bootkrajang, Jakramate, Marukatat, Sanparith, Gonçalves, Teresa, Chaijaruwanich, Jeerayut
Publikováno v:
In Expert Systems With Applications 15 November 2019 134:120-137
Akademický článek
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