Barriers Preventing Liver Transplantation in Canadians with HIV Infection – Perceptions of HIV Specialists by Curtis L Cooper, Joanne DeForest, John Gill, Richard Lalonde, on behalf of the Canadian HIV Trials Network (CTN) Co-Infection Core Group

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Fast Ways to Detect Outliers by Emad Obaid Merza, Nashaat Jasim Mohammed

“…On the other hand, the presence of outliers ​​may be of great benefit, for example knowledge of geological activities that precede natural disasters such as (earthquakes, forest fires, floods ... etc.). Therefore, detection of outliers is of great importance in various fields. …”
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Enhancing breast cancer prediction through stacking ensemble and deep learning integration by Fatih Gurcan

“…To achieve this, the efficacy of ensemble methods such as Random Forest, XGBoost, LightGBM, ExtraTrees, HistGradientBoosting, AdaBoost, GradientBoosting, and CatBoost in modeling breast cancer diagnosis was comprehensively evaluated. …”
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Identification of Fake Comments in E-Commerce Based on Triplet Convolutional Twin Network and CatBoost Model by Juanjuan Peng

“…The benchmark experimental results show that the proposed TriCNN-CatBoost model significantly outperforms traditional Naive Bayes, Support Vector Machines, and Random Forest models in terms of accuracy, recall, and F1 score, demonstrating stronger false comment recognition ability and generalization performance. …”
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Immunocompromised Children with Severe Adenoviral Respiratory Infection by Joanna C. Tylka, Michael C. McCrory, Shira J. Gertz, Jason W. Custer, Michael C. Spaeder

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Construction of a health literacy prediction model for diabetic patients: A multicenter study by Zepeng Wang, Junyi Shi, Fangyuan Jiang, Kui Jiang, Yalan Chen

“…Predictive models were established and compared using logistic regression (LR), random forest (RF), and support vector machine (SVM). Calibration curves, decomposition plots, and partial dependence plots were drawn to evaluate and interpret the models. …”
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Declining extent of fen peat deposits over the past 50 years in Ogre Municipality, central Latvia by Normunds Stivrins, Davis Matskins, Ilze Ozola

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Assessment of source material for malting barley breeding by O. A. Yusova, P. N. Nikolaev, M. A. Kuzmich, L. S. Kuzmich

“…The studies were carried out from 2017 to 2020. in the southern forest-steppe of Western Siberia. The target material included 13 lines: Sasha × Getman (2 lines), Sasha × Margret, Podarok Sibiri × Getman (3 lines), Omsky 95 × Beatrice (3 lines), Omsky 95 × Despina, Omsky 95 × Viva, Omsky 100 × Margret, and Omsky 90 × Margret. …”
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Shoot complexes on the apical part of the crown of generative <i>Fraxinus excelsior</i> L. trees by I. S. Antonova, M. S. Televinova

“…Fraxinus excelsior L. is a common forest species in the Central Russian Upland, used to produce valuable lumber and for landscaping. …”
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Numerical simulation and experimental validation of the oleogel formation from grape seed oil and beeswax by Zohreh Baratian Ghorghi, Ali Faezian, Samira Yeganehzad, Mohammad Ali Hesarinejad, Alireza Sheikhi Darani, Marcello Fidaleo, Hashem Ahmadi Tighchi

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Control y Biología del Helecho Trepador Japonés (Lygodium japonicum) by Elsa D. Chevasco, Patrick J. Minogue, Kimberly K. Bohn, Francisco Escobedo

“…Bohn, and Francisco Escobedo, and published by the UF Department of School of Forest Resources and Conservation, November 2016. …”
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Ecological infrastructure and its role in sustainable urban development: analysis and perspectives by Ie. P. Tertytskyi

“…The article examines key components of EI such as green infrastructure (parks, gardens, forests) and blue infrastructure (water bodies), emphasizing their importance in providing ecosystem services and improving residents' quality of life. …”
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Spatiotemporal analysis of urban expansion and its impact on farmlands in the central Ethiopia metropolitan area by Kalid Hassen Yasin, Anteneh Derribew Iguala, Tadele Bedo Gelete

“…The supervised random forest (RF) classification in the Google Earth Engine platform was used to prepare land use and land cover (LULC) for 1990, 2000, 2010, and 2023. …”
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Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub by Thirunavukarasu Balasubramaniam, Wathsala Anupama Mohotti, Kenneth Sabir, Richi Nayak

“…Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. …”
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Evolution of SARS-CoV-2 in white-tailed deer in Pennsylvania 2021-2024. by Andrew D Marques, Matthew Hogenauer, Natalie Bauer, Michelle Gibison, Beatrice DeMarco, Scott Sherrill-Mix, Carter Merenstein, Ronald G Collman, Roderick B Gagne, Frederic D Bushman

“…Prevalence was higher in WTD in regions with crop coverage rather than forest, suggesting an association with proximity to humans. …”
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An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia. by Junying Wei, Heshan Cao, Mingling Peng, Yinzhou Zhang, Sibei Li, Wuhua Ma, Yuhui Li

“…<h4>Results</h4>A total of 1,894 VAP patients were included, with 12 features ultimately selected for model construction: 24-hour urine output, blood urea nitrogen, age, diastolic blood pressure, platelet count, anion gap, body temperature, bicarbonate level, sodium level, body mass index, and whether combined with congestive heart failure and cerebrovascular disease. The random forest (RF) model showed the best performance, achieving an AUC of 0.780 in internal validation and 0.724 in external testing, outperforming other ML models and common clinical scoring systems.…”
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Unlocking Vellore's water future: Integrated hydrogeochemical research aligns with SDGs 6, 12, and 13 by Sakshi Dange, Kumaraguru Arumugam, Sai Saraswathi Vijayaraghavalu

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Les bois de construction du boulevard Dr Henri-Henrot à Reims/Durocortorum by Willy Tegel

“…The exceptional number of wooden elements recovered on site not only provides information on the state of the forest at that time, but also on its management, both of which may be related to changing environmental conditions and socio-economic processes. …”
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Classification of mangrove habitat for  development of soka crab (Scylla serrata) silvofishery in the north coast of Rembang Regency by Erny Poedjirahajoe

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Automated potato tuber mass estimation and grading with multiangle 2D images by Ayush K. Sharma, Lincoln Zotarelli, Alina Zare, Lakesh K. Sharma

“…In the second step, a random forest classification model was developed to grade the potato tubers based on image-extracted tuber width dimensions from the top, side, and both angles. …”
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