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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 Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Kim, I. H. Kim, IH 4 Seoul Natl Univ Hosp, Dept Radiat Oncol, Seoul, South Korea F-4594-2014 Kim, Hee
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Lim, D. H. Lim, DH 5 Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Seoul, South Korea
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Park, S. H. Park, SH 6 Kyungpook Natl Univ, Med Ctr, Daegu, South Korea
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Lee, J. H. Lee, JH 7 Pusan Natl Univ Hosp, Dept Radiat Oncol, Busan, South Korea J-2154-2015 Lee, Hye
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Chang, J. H. Chang, JH 8 Seoul Natl Univ, Boramae Med Ctr, Seoul Metropolitan Govt, Dept Radiat Oncol, Seoul, South Korea
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Cho, K. H. Cho, KH 9 Natl Canc Ctr, Proton Therapy Ctr, Goyang Si, Gyeonggi Do, South Korea AFA-1420-2022 Cho, Hwa Jin
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Kim, J. H. Kim, JH 10 Keimyung Univ, Sch Med, Dongsan Med Ctr, Dept Radiat Oncol, Daegu, South Korea
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Sunwoo, L. Sunwoo, L 11 Seoul Natl Univ, Bundang Hosp, Dept Radiol, Seongnam, South Korea
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Choi, S. H. Choi, SH 12 Seoul Natl Univ, Coll Med, Dept Radiol, Seoul, South Korea AAE-2350-2021 Choi, Sung-Hwan
Machine Learning Model to Predict Pseudoprogression versus Progression in Glioblastoma: A Multi-institutional Study (KROG 18-07) Kim, I. A. Kim, IA 13 Seoul Natl Univ, Seoul, South Korea
Machine Learning-Based Code Auto-Completion Implementation for Firmware Developers Kim, Junghyun Kim, J 1 Georgia Inst Technol, Sch Computat Sci & Engn, Atlanta, GA 30332 USA 0000-0002-8762-6991 Kim, Junghyun andy.kim@gatech.edu;klee400@knu.ac.kr;sh518.choi@samsung.com;
Machine Learning-Based Code Auto-Completion Implementation for Firmware Developers Lee, Kyuman Lee, K 2 교신저자 Kyungpook Natl Univ, Dept Robot & Smart Syst Engn, Daegu 41566, South Korea AAM-6979-2020 lee, kyuman 0000-0003-0755-9635 Lee, Kyuman andy.kim@gatech.edu;klee400@knu.ac.kr;sh518.choi@samsung.com;
Machine Learning-Based Code Auto-Completion Implementation for Firmware Developers Choi, Sanghyun Choi, S 3 Samsung Elect, Memory SW Dev Team, Hwasung 18448, South Korea andy.kim@gatech.edu;klee400@knu.ac.kr;sh518.choi@samsung.com;
Machine learning-based prediction models for formation energies of interstitial atoms in HCP crystals You, Daegun You, D 1 Sungkyunkwan Univ, Sch Mech Engn, Suwon, Gyeonggi Do, South Korea 0000-0002-9435-0452 you, daegun wy.shin@yonsei.ac.kr;dongwoolee@skku.edu;
Machine learning-based prediction models for formation energies of interstitial atoms in HCP crystals Ganorkar, Shraddha Ganorkar, S 2 Sungkyunkwan Univ, Sch Mech Engn, Suwon, Gyeonggi Do, South Korea 0000-0001-7933-3796 Ganorkar, Shraddha wy.shin@yonsei.ac.kr;dongwoolee@skku.edu;
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