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浏览/检索结果: 共5条，第1-5条 帮助 限定条件 专题：合肥物质科学研究院    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 A matter of the beehive sound: Can honey bees alert the pollution out of their hives? 期刊论文 ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022 作者:  Yu, Baizhong;  Huang, Xinqiu;  Sharif, Muhammad Zahid;  Jiang, Xueli;  Di, Nayan 收藏  |  浏览/下载：16/0  |  提交时间：2022/12/22 Honey bees  Apis cerana  Beehive sound  Environmental contaminants  Machine Learning   Machine learning prediction of prostate cancer from transrectal ultrasound video clips 期刊论文 FRONTIERS IN ONCOLOGY, 2022, 卷号: 12 作者:  Wang, Kai;  Chen, Peizhe;  Feng, Bojian;  Tu, Jing;  Hu, Zhengbiao 收藏  |  浏览/下载：15/0  |  提交时间：2022/12/22 artificial intelligence  prostate cancer  ultrasound  machine learning  support vector machine   Impact of cumulative cisplatin dose in childhood nasopharyngeal carcinoma based on neoadjuvant chemotherapy response in the intensity-modulated radiotherapy era: a real-world study 期刊论文 CANCER CELL INTERNATIONAL, 2021, 卷号: 21 作者:  Jin, Ya-Nan;  Qiang, Meng-Yun;  Liu, Meng-Meng;  Cheng, Zhi-Bin;  Zhang, Wang-Jian 收藏  |  浏览/下载：41/0  |  提交时间：2022/01/10 Nasopharyngeal carcinoma  Cumulative cisplatin dose  Children and adolescents  Tumor response  Neoadjuvant chemotherapy   Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches 期刊论文 IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2021 作者:  Zhu, Kunpeng;  Fuh, Jerry Ying Hsi;  Lin, Xin 收藏  |  浏览/下载：21/0  |  提交时间：2022/01/25 Feature extraction  Process monitoring  Three-dimensional printing  Powders  Condition monitoring  Buildings  Metals  Condition monitoring  machine learning  metal-based additive manufacturing (MAM)   Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A 期刊论文 Plasma Physics and Controlled Fusion, 2021, 卷号: 63 作者:  Zhong,Y;  Zheng,W;  Chen,Z Y;  Xia,F;  Yu,L M 收藏  |  浏览/下载：21/0  |  提交时间：2021/06/21 disruption prediction  machine learning  LightGBM  feature importance