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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
Predicting energy consumption of building clusters at the design stage using machine learning models Kavgic, Miroslava Kavgic, M 5 Univ Ottawa, Civil Engn, 161 Louis Pasteur, Ottawa, ON K1N 6N5, Canada owolabiabdulhameed@gmail.com; yahaya@knu.ac.kr; mohammad.amir@liverpool.ac.uk; dongjuhsuh@knu.ac.kr; mkavgic@uottawa.ca; dongjunsuh@knu.ac.kr;
Predicting energy consumption of building clusters at the design stage using machine learning models Suh, Dongjun Suh, D 6 교신저자 Kyungpook Natl Univ, Dept Convergence & Fus Syst Engn, Sangju 37224, South Korea owolabiabdulhameed@gmail.com; yahaya@knu.ac.kr; mohammad.amir@liverpool.ac.uk; dongjuhsuh@knu.ac.kr; mkavgic@uottawa.ca; dongjunsuh@knu.ac.kr;
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Lee, Jeeyeon Elizabeth Lee, JE 1
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Park, Sung Joon Park, SJ 2
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Kim, Won Hwa Kim, WH 3
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Kim, Jaeil Kim, J 4
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Kang, Byeongju Kang, B 5
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Park, Ho Yong Park, HY 6
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Kim, Hye Jung Kim, HJ 7
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Chae, Yee Soo Chae, YS 8
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Lee, Soo Jung Lee, SJ 9
Predicting high-risk group according to Oncotype DX recurrence score using dynamic contrast-enhanced breast MR with temporal radiomic features. Lee, In Hee Lee, IH 10
Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models Kim, In-Hwan Kim, IH 1 Univ Ulsan, Asan Med Inst Convergence Sci & Technol, Asan Med Ctr, Dept Biomed Engn,Coll Med, Seoul, South Korea jeuspark@gmail.com; namkugkim@gmail.com;
Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models Jeong, Jiheon Jeong, J 2 Univ Ulsan, Asan Med Inst Convergence Sci & Technol, Asan Med Ctr, Dept Biomed Engn,Coll Med, Seoul, South Korea jeuspark@gmail.com; namkugkim@gmail.com;
Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models Jeong, Jiheon Jeong, J 2 SK Telecom, Seoul 04539, South Korea jeuspark@gmail.com; namkugkim@gmail.com;
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