Showing 5,781 - 5,800 results of 5,817 for search '"forester"', query time: 0.08s Refine Results
  1. 5781

    Construction and validation of a risk prediction model for high altitude de-acclimatization syndrome by DING Yu, DING Yu, WANG Zejun, WANG Zejun, XIE Jiaxin, XIE Jiaxin

    Published 2025-01-01
    “…Risk prediction models for high-altitude adaptation symptoms were built based on 8 machine learning methods, including multiple factor logistic regression (LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGB), support vector machine (SVM), K-nearest neighbor (KNN), light gradient boosting (LGB) and naïve bayes (NB). …”
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  2. 5782

    AI-based tumor-infiltrating lymphocyte scoring system for assessing HCC prognosis in patients undergoing liver resection by Zhiyang Chen, Tingting Xie, Shuting Chen, Zhenhui Li, Su Yao, Xuanjun Lu, Wenfeng He, Chao Tang, Dacheng Yang, Shaohua Li, Feng Shi, Huan Lin, Zipei Li, Anant Madabhushi, Xiangtian Zhao, Zaiyi Liu, Cheng Lu

    Published 2025-02-01
    “…We trained a deep neural network and a random forest model to segment tumor regions and locate CD8+ TILs in H&E and CD8-stained whole-slide images. …”
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  3. 5783

    Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosisResearch in context by Caihong Ning, Hui Ouyang, Jie Xiao, Di Wu, Zefang Sun, Baiqi Liu, Dingcheng Shen, Xiaoyue Hong, Chiayan Lin, Jiarong Li, Lu Chen, Shuai Zhu, Xinying Li, Fada Xia, Gengwen Huang

    Published 2025-02-01
    “…Findings: Random survival forest (RSF) model showed the best predictive performance than other 9 ML models (internal validation, C-index = 0.863 [95% CI: 0.854–0.875]; external validation, C-index = 0.857 [95% CI: 0.850–0.865]). …”
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  4. 5784
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  9. 5789
  10. 5790

    Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT, 18F-FDG PET/CT, DNA mutation, and CA19... by Liang Qi, Xiang Li, Jiayao Ni, Yali Du, Qing Gu, Baorui Liu, Jian He, Juan Du

    Published 2025-01-01
    “…In constructing the short-term treatment efficacy prediction model, ensemble learning methods such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and RandomForest performed the best. However, in terms of model interpretability, decision tree methods provide the most intuitive display of the predictive details of the model. …”
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  11. 5791
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  14. 5794

    Étude de trois grands tonneaux mis au jour à Reims/Durocortorum (Marne) : le savoir-faire des tonneliers antiques by Pierre Mille, Philippe Rollet

    Published 2020-12-01
    “…In order to meet the orders of the cooper’s workshops the hoop makers –another forest-related activity– had to obtain their supplies from the cord-makers, among other peoples. …”
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  15. 5795

    Development, validation, and clinical application of a machine learning model for risk stratification and management of cervical cancer screening based on full-genotyping hrHPV tes... by Binhua Dong, Zhen Lu, Tianjie Yang, Junfeng Wang, Yan Zhang, Xunyuan Tuo, Juntao Wang, Shaomei Lin, Hongning Cai, Huan Cheng, Xiaoli Cao, Xinxin Huang, Zheng Zheng, Chong Miao, Yue Wang, Huifeng Xue, Shuxia Xu, Xianhua Liu, Huachun Zou, Pengming Sun

    Published 2025-02-01
    “…Methods: We developed, compared and validated four machine learning models (eXtreme gradient boosting [XGBoost], support vector machine [SVM], random forest [RF], and naïve bayes [NB]) for cervical cancer prediction, using data from a national cervical cancer screening project conducted in 267 healthcare centers in China. …”
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  16. 5796
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  18. 5798

    A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 by Z. Wu, Z. Wu, W. Lu, A. Roobaert, L. Song, X.-H. Yan, W.-J. Cai

    Published 2025-01-01
    “…This study developed a regional reconstructed <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> product for the NAACOM (Reconstructed Coastal Acidification Database-<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>, or ReCAD-NAACOM-<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>) using a two-step approach combining random forest regression and linear regression. The product provides monthly <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> data at 0.25° spatial resolution from 1993 to 2021, enabling investigation of regional spatial differences, seasonal cycles, and decadal changes in <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>. …”
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  19. 5799
  20. 5800

    Modern theoretical-methodological and economic-legal problems of implementing the concept of sustainable development of tourism and recreation in Ukraine by Ion Dubovich, Alla Anishchenko, Nadia Yurkiv, Yuliia Volkovska, Rostyslav Matsko, Oleh Rozhkovych, Mariana-Nadiia Dubovich, Oksana Pylypiak

    Published 2024-10-01
    “…It is emphasized that the main problems that are currently hindering the implementation of the concept of sustainable development of tourism and recreation in Ukraine today are, first of all, the fact that hundreds of hectares of the territory of Ukraine are mined, the territories are contaminated with ammunition, large areas of the forest fund have been destroyed, the infrastructure has been destroyed, etc.It is noted that the Methodology for determining damage and losses, approved by the Ministry of Environmental Protection and Natural Resources of Ukraine, takes into account only a part of economic losses, while other significant losses that directly relate to the tourism and recreation industry (loss of life and health of the population of Ukraine, separation Ukrainian families, destroyed living conditions of Ukrainian citizens, damaged or destroyed residential buildings, moral, aesthetic and many other damages) are not taken into account.It is substantiated that for an objective assessment of damage and the amount of damage, it is also necessary to take into account environmental damage and destroyed natural resources, including mined and polluted hundreds of hectares of the territory of Ukraine, etc. …”
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