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| Machine learning applications in genome-scale metabolic modeling Kim, Yeji; Kim, Gi Bae; Lee, Sang Yup, Current Opinion in Systems Biology, v.25, pp.42 - 49, 2021-03 |
| Machine learning applications in systems metabolic engineering Kim, Gi Bae; Kim, Won Jun; Kim, Hyun Uk; Lee, Sang Yup, CURRENT OPINION IN BIOTECHNOLOGY, v.64, pp.1 - 9, 2020-08 |
| Machine learning assisted non-destructive beam profile monitoring Omarov, Zhanibek; Haciomeroglu, Selcuk, NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, v.1026, 2022-03 |
| Machine learning assisted synthesis of lithium-ion batteries cathode materials Liow, Chi Hao; Kang, Hyeonmuk; Kim, Seunggu; Na, Moony; Lee, Yongju; Baucour, Arthur; Bang, Kihoon; et al, NANO ENERGY, v.98, 2022-07 |
| Machine learning based english-to-Korean transliteration using grapheme and phoneme information Oh, JH; Choi, Key-Sun, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E88D, no.7, pp.1737 - 1748, 2005-07 |
| Machine learning based evolutionary algorithms and optimization for transportation and logistics Preface Cheng, John; Yang, Bin; Gen, Mitsuo; Jang, Young Jae; Liang, Cheng-Ji, COMPUTERS & INDUSTRIAL ENGINEERING, v.143, 2020-05 |
| Machine learning based photovoltaic energy prediction scheme by augmentation of on-site IoT data Park, Jaeeun; Kim, Jangkyum; Lee, Sanghyun; Choi, Jun Kyun, FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, v.134, pp.1 - 12, 2022-09 |
| Machine Learning Driven Aid Classification for Sustainable Development Lee, Junho; Song, Hyeonho; Lee, Dongjoon; Kim, Sundong; Sim, Jisoo; Cha, Meeyoung; Park, Kyung-Ryul, Proceedings of the International Joint Conference on Artificial Intelligence, 2023-08 |
| Machine learning filters out efficient electrocatalysts in the massive ternary alloy space for fuel cells Park, Youngtae; Hwang, Chang-Kyu; Bang, Kihoon; Hong, Doosun; Nam, Hyobin; Kwon, Soonho; Yeo, Byung Chul; et al, APPLIED CATALYSIS B-ENVIRONMENTAL, v.339, 2023-12 |
| Machine Learning for Advanced Wireless Sensor Networks: A Review Kim, Taeyoung; Vecchietti, Luiz Felipe; Choi, Kyujin; Lee, Sangkeum; Har, Dongsoo, IEEE SENSORS JOURNAL, v.21, no.11, pp.12379 - 12397, 2021-06 |
| Machine Learning for Object Recognition in Manufacturing Applications Yun, Huitaek; Kim, Eunseob; Kim, Dong Min; Park, Hyung Wook; Jun, Martin Byung-Guk, INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, v.24, no.4, pp.683 - 712, 2023-04 |
Machine Learning for Practical Localization System Using Multiview CSI Kim, Minseuk; Han, Dongsoo; Rhee, June-Koo Kevin, IEEE ACCESS, v.8, pp.184575 - 184584, 2020-10 |
| Machine learning for renewable energy materials Gu, Geun Ho; Noh, Juhwan; Kim, Inkyung; Jung, Yousung, JOURNAL OF MATERIALS CHEMISTRY A, v.7, no.29, pp.17096 - 17117, 2019-08 |
| Machine learning identification of symmetrized base states of Rydberg atoms Chong, Daryl Ryan; Kim, Minhyuk; Ahn, Jaewook; Jeong, Heejeong, FRONTIERS OF PHYSICS, v.17, no.1, 2022-02 |
| Machine learning modeling to forecast uncertainty between capital sudden stop and boom Park, sangjin; Yang, Jae-Suk, EXPERT SYSTEMS WITH APPLICATIONS, v.237, 2024-03 |
| Machine learning models based on bubble analysis for Bitcoin market crash prediction Park, sangjin; Yang, Jae-Suk, Engineering Applications of Artificial Intelligence, v.135, 2024-09 |
| Machine learning powered sketch aided design via topology optimization Zhang, Weisheng; Wang, Yue; Youn, Sung-Kie; Guo, Xu, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, v.419, 2024-02 |
| Machine learning-assisted laser-induced breakdown spectroscopy for monitoring molten salt compositions of small modular reactor fuel under varying laser focus positions Lee, Yunu; Foster, Richard I.; Kim, Hyeongbin; Choi, Sungyeol, ANALYTICA CHIMICA ACTA, v.1241, 2023-02 |
| Machine learning-assisted optimization of TBBPA-bis-(2,3-dibromopropyl ether) extraction process from ABS polymer Wan, Yan; Zeng, Qiang; Shi, Pujiang; Yoon, Yong-Jin; Tay, Chor Yong; Lee, Jong-Min, CHEMOSPHERE, v.287, 2022-01 |
| Machine learning-based atom contribution method for the prediction of surface charge density profiles and solvent design Liu, Qilei; Zhang, Lei; Tang, Kun; Liu, Linlin; Du, Jian; Meng, Qingwei; Gani, Rafiqul, AICHE JOURNAL, v.67, no.2, pp.e17110, 2021-02 |
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