Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women by Peng Wang, Qiang Yin, Kangzhi Ding, Huaichang Zhong, Qundi Jia, Zhasang Xiao, Hai Xiong

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Using a machine learning algorithm and clinical data to predict the risk factors of disease recurrence after adjuvant treatment of advanced-stage oral cavity cancer by Sheng-Yao Huang, Ren-Jun Hsu, Dai-Wei Liu, Wen-Lin Hsu

Subjects: “…extreme gradient boosting…”
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Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model by Hang Qu, Qiqi Ban, LiangXue Zhou, HaiHan Duan, Wei Wang, AiJun Peng

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Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study by Xin Guan, Bo Zhang, Ming Fu, Mengying Li, Xu Yuan, Yaowu Zhu, Jing Peng, Huan Guo, Yanjun Lu

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Assessing groundwater level variability in response to climate change: A case study of large plain areas by Haidi Wu, Xueyan Ye, Xinqiang Du, Wuxiang Wang, Hongyan Li, Weihong Dong

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Extreme gradient boosting: A machine learning technique for daily global solar radiation forecasting on tilted surfaces by Mbah O. M., Madueke C. I., Umunakwe R., Agba M. N.

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A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management by Sheng-Wei Wang, Yen-Yu Chen, Shu-Han Hsu, Yu-Hsuan Kao, Masaomi Kimura, Li-chiu Chang, Tzi-Wen Pan, Chuen-Fa Ni

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Variance-based sensitivity analysis of climate variability impact on crop yield using machine learning: A case study in Jordan by Yingqiang Xu, Abeer Albalawneh, Maysoon Al-Zoubi, Hiba Baroud

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Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

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Machine learning modeling for thermochemical biohydrogen production from biomass by Yingju Chang, Wei Wang, Jo-Shu Chang, Duu-Jong Lee

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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

Subjects: “…Extreme Gradient Boosting (XGBoost + FIS)…”
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Peramalan Penjualan Produk Menggunakan Extreme Gradient Boosting (XGBoost) dan Kerangka Kerja CRISP-DM untuk Pengoptimalan Manajemen Persediaan (Studi Kasus: UB Mart) by Raihan Winurputra, Dian Eka Ratnawati

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Disaggregating IMERG satellite precipitation over Czech Republic: an innovative approach using hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Cluster... by Ujjwal Singh, Sadaf Nasreen, Gaurav Tripathi, Pragya Mehrishi, Rajani Kumar Pradhan, Poppová Bestakova, Vivek Vikram Singh, K C Gouda, Laxmi Kant Sharma, Kiran Jalem, Petr Maca, Rama Rao Nidamanuri, Akhilesh Singh Raghubanshi, Yannis Markonis, Rakovec Oldřich, Martin Hanel

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Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador by Esteban Bravo-López, Tomás Fernández, Chester Sellers, Jorge Delgado-García

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Applicability of remote sensing and machine learning for predicting bulk soil electrical conductivity under different forest types in central Japan by Kyaw Win, Tamotsu Sato, Takuya Hiroshima

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Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models by Mohammed Hilal Mukhsaf, Weiqin Li, Ghassan Husham Jani

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Utilizing Machine Learning and Multi-Station Observations to Investigate the Visibility of Sea Fog in the Beibu Gulf by Qin Huang, Peng Zeng, Xiaowei Guo, Jingjing Lyu

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Machine learning analysis of greenhouse gas sources impacting Africa’s food security nexus by Adusei Bofa, Temesgen Zewotir

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Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models by Baljeet Kaur, Andrew Binns, Edward McBean, Dan Sandink, Karen Castro, Bahram Gharabaghi

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Data‐Driven Estimation of Groundwater Level Time‐Series at Unmonitored Sites Using Comparative Regional Analysis by E. Haaf, M. Giese, T. Reimann, R. Barthel

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