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Document Title Author Full Name Author Short Name Index Corresponding Address ResearcherID ResearcherID Author Name ORCID ORCID Author Name Related Email
Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment Kim, Jonghong Kim, J 6 Keimyung Univ, Dongsan Med Ctr, Dept Neurol, Daegu, South Korea cylee87@knu.ac.kr; jeongsm00@gmail.com;
Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment Chung, Ho-Young Chung, HY 7 Kyungpook Natl Univ, Sch Med, Dept Med Informat, Daegu, South Korea cylee87@knu.ac.kr; jeongsm00@gmail.com;
Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment Lee, Chung-Young Lee, CY 8 교신저자 Kyungpook Natl Univ, Sch Med, Dept Microbiol, Daegu, South Korea cylee87@knu.ac.kr; jeongsm00@gmail.com;
Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment Lee, Chung-Young Lee, CY 8 교신저자 Kyungpook Natl Univ, Untreatable Infect Dis Inst, Daegu, South Korea cylee87@knu.ac.kr; jeongsm00@gmail.com;
Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment Jeong, Sungmoon Jeong, S 9 교신저자 Kyungpook Natl Univ, Sch Med, Dept Med Informat, Daegu, South Korea cylee87@knu.ac.kr; jeongsm00@gmail.com;
Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment Jeong, Sungmoon Jeong, S 9 교신저자 Kyungpook Natl Univ Hosp, Res Ctr Artificial Intelligence Med, Daegu, South Korea cylee87@knu.ac.kr; jeongsm00@gmail.com;
Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size Byun, Yong-Hoon Byun, YH 1 Kyungpook Natl Univ, Dept Agr Civil Engn, 80 Daehak Ro, Daegu 41566, South Korea JKI-8441-2023 Byun, Yong-Hoon jwon@unist.ac.kr;
Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size Son, Juik Son, J 2 Kyungpook Natl Univ, Dept Agr Civil Engn, 80 Daehak Ro, Daegu 41566, South Korea jwon@unist.ac.kr;
Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size Yun, Jungmin Yun, J 3 Univ Ulsan, Dept Civil & Environm Engn, Daehak Ro 93, Ulsan 680749, South Korea jwon@unist.ac.kr;
Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size Choo, Hyunwook Choo, H 4 Hanyang Univ, Dept Civil & Environm Engn, Seoul 04763, South Korea jwon@unist.ac.kr;
Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size Won, Jongmuk Won, J 5 교신저자 Ulsan Natl Inst Sci & Technol UNIST, Dept Civil Earth & Environm Engn, UNIST Gil 50, Ulsan 44919, South Korea jwon@unist.ac.kr;
Machine Learning-based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts Kim, Taewan Kim, T 1 Seoul Natl Univ, 1 Gwanak Ro, Seoul, South Korea 0009-0001-3758-9440 Kim, Taewan hhwang@astro.snu.ac.kr;
Machine Learning-based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts Sohn, Jubee Sohn, J 2 Seoul Natl Univ, 1 Gwanak Ro, Seoul, South Korea 0000-0002-9254-144X Sohn, Jubee hhwang@astro.snu.ac.kr;
Machine Learning-based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts Hwang, Ho Seong Hwang, HS 3 교신저자 Seoul Natl Univ, 1 Gwanak Ro, Seoul, South Korea AAS-6010-2020 HWANG, Ho hhwang@astro.snu.ac.kr;
Machine Learning-based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts Hwang, Ho Seong Hwang, HS 3 교신저자 Macquarie Univ, Australian Astron Opt, Sydney, 2113, Australia AAS-6010-2020 HWANG, Ho hhwang@astro.snu.ac.kr;
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