Zobrazeno 1 - 7 of 7 pro vyhledávání: '"Seoksu Hong"'
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Akademický článek
Modulation of Synaptic Plasticity Mimicked in Al Nanoparticle‐Embedded IGZO Synaptic Transistor
Autor: Jeehoon Kim, Younghun Kim, Ojun Kwon, Taehyeon Kim, Seyoung Oh, Soeun Jin, Woojin Park, Jung‐Dae Kwon, Seung‐Woo Hong, Chang‐Sik Lee, Ho‐Yong Ryu, Seoksu Hong, Jaehoon Kim, Tae‐Young Heo, Byungjin Cho
Publikováno v: Advanced Electronic Materials, Vol 6, Iss 4, Pp n/a-n/a (2020)
Abstract Diverse artificial synapse structures and materials are widely proposed for neuromorphic hardware systems beyond von Neumann architecture owing to their capability to mimic complex information processing tasks such as image recognition, natu
Externí odkaz: https://doaj.org/article/832ce76830994b4db0dc3f3e8de7ec56
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2
Akademický článek
Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data
Autor: Bomi Jeong, Hyunjeong Cho, Jieun Kim, Soon Kil Kwon, SeungWoo Hong, ChangSik Lee, TaeYeon Kim, Man Sik Park, Seoksu Hong, Tae-Young Heo
Publikováno v: Diagnostics, Vol 10, Iss 6, p 415 (2020)
This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with vario
Externí odkaz: https://doaj.org/article/1a2e02442dc44771919a6325fe0de7cb
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4
Prediction of Chlorophyll-a Concentrations in the Nakdong River Using Machine Learning Methods
Autor: TaeYeon Kim, Seungwoo Hong, Eunji Lee, Jungsu Park, Man Sik Park, Seoksu Hong, Tae-Young Heo, Seulbi Lee, Yuna Shin, Taekgeun Kim, ChangSik Lee
Publikováno v: Water, Vol 12, Iss 1822, p 1822 (2020)
Water
Volume 12
Issue 6
Many studies have attempted to predict chlorophyll-a concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Rando
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::129981a2f0f095eeda3521e07e5d2e56
https://www.mdpi.com/2073-4441/12/6/1822
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5
Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data
Autor: Seung-Woo Hong, Tae-Young Heo, Soon Kil Kwon, ChangSik Lee, Hyunjeong Cho, Man Sik Park, Ji Eun Kim, TaeYeon Kim, Bomi Jeong, Seoksu Hong
Publikováno v: Diagnostics
Diagnostics, Vol 10, Iss 415, p 415 (2020)
Volume 10
Issue 6
This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with vario
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4554d7a4438fbd188dabe7f282c661dd
https://pubmed.ncbi.nlm.nih.gov/32570782
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6
The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods
Autor: Heesuk Lee, Seoksu Hong, Young-Joo Lee, Bomi Jeong, Yuna Shin, Jae-Kyeong Lee, Taekgeun Kim, Tae-Young Heo, Jaehoon Kim, Dae Keun Seo
Publikováno v: Mathematical Problems in Engineering, Vol 2019 (2019)
This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging,
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3b6200d8ff7c0d3a35a11a30b53e9f5
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Plný text Recenzováno Digitální knihovna AV ČR
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