Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning by Zeynep Kucukakcali, Ipek Balikci Cicek

“…The study uses Random Forest (RF) machine learning to identify characteristics that affect WHR, an indication of CVD. …”
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Comparative use of different AI methods for the prediction of concrete compressive strength by Mouhamadou Amar

“…The most accurate model was found to be a gradient-boosted tree followed by deep learning and random forest. Forecasts were validated with high accuracy by comparing experimental results to numerical data.…”
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L’abeille mellifère (Apis mellifera unicolor) à Mayotte by Samuel Perichon

“…The reason why collecting honey in the forest remains a marker of identity in Mahoran culture is undoubtedly because of this. …”
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Chemical Differentiation and Quantitative Analysis of Different Types of Panax Genus Stem-Leaf Based on a UPLC-Q-Exactive Orbitrap/MS Combined with Multivariate Statistical Analysi... by Lele Li, Yang Wang, Yang Xiu, Shuying Liu

“…To further explore the differences between cultivated ginseng stem-leaf and forest ginseng stem-leaf, the partial least squares-discriminant analysis (PLS-DA) model was built based on −ESI full scan data. …”
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RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization by Pengfei Tong, Xuerong Yang, Xuanzhi Peng, Longfei Wang

“…In challenging environments such as disaster aid or forest rescue, unmanned aerial vehicles (UAVs) have been hampered by inconsistent or even denied global navigation satellite system (GNSS) signals, resulting in UAVs becoming incapable of operating normally. …”
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Goal and shot prediction in ball possessions in FIFA Women’s World Cup 2023: a machine learning approach by Iyán Iván-Baragaño, Antonio Ardá, José L. Losada, Rubén Maneiro

“…The predictive capacity was tested using Random Forest and XGBoost and finally and SHAP values were calculated and visualized to understand the influence of the predictors.ResultsRandom Forest technique showed greater efficacy, with recall and sensitivity above 93% in the resampled dataset. …”
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Analysis of Debris Flow Reduction Effect of Check Dam Types considering the Mountain Stream Shape: A Case Study of 2016 Debris Flow Hazard in Ulleung-do Island, South Korea by Man-Il Kim, Namgyun Kim

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Testing the Validity of Environmental Kuznets Curve for Carbon Emission: A Cross-Section Analysis by Punam Chanda, Pintu Majhi and Salina Akther

“…The study investigates the validity of the EKC hypothesis for carbon emission with an analysis of 158 countries in the world, with population, urbanization, forest cover, and tourist inflow as the control variables. …”
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An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers. by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Twum Frimpong, Abdul-Salaam Gaddafi

“…Prediction accuracy score of area under the curve obtained show Gradient Boosting classifier achieved 91% accuracy, Logistic Regression 93% and Random Forest 91%. Balanced accuracy score obtained for these techniques were; Gradient Boosting 82.73%, Logistic Regression 84.62% and Random Forest 83.02%. …”
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Vanilla lindmaniana and V. palmarum (Orchidaceae) are distinct allopatric species by Emerson R. Pansarin

“…While studying the diversity of Brazilian Vanilla, differences between plants from the Amazonia/Cerrado/Pantanal and Caatinga/Atlantic Forest Biomes were recorded. Material and methods – Detailed descriptions based on herbaria specimens and living material of V. lindmaniana and V. palmarum are provided and a morphological comparison is given. …”
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Simulation of the Hydrological Components of Dez River Basin by the classification of Land Use Categories Using SUFI-2 Algorithm by Elaheh Asgari, Mohammad Baaghideh, Majid Hosseini, Alireza Entezari, Asghar Kamyar

“…The results of the model showed that the areas with forest use share the highest contribution to aquifer nutrition, and the barren lands have the highest surface runoff. …”
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Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach by Soheil Hashtarkhani, Yiwang Zhou, Fekede Asefa Kumsa, Shelley White-Means, David L Schwartz, Arash Shaban-Nejad

“…To evaluate the influence of these social determinants, we implemented a random forest model, with the aim of comparing its performance to linear regression and support vector machine models. …”
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Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…Our analysis indicates that the Random Forest and Deep Belief Network models outperform all other competing models. …”
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Automated Classification of Circulating Tumor Cells and the Impact of Interobsever Variability on Classifier Training and Performance by Carl-Magnus Svensson, Ron Hübler, Marc Thilo Figge

“…The random forest classifier turned out to be resilient to uncertainty in the training data while the support vector machine’s performance is highly dependent on the amount of uncertainty in the training data. …”
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Stapled End-To-Side Ileocolic Anastomosis in Crohn’s Disease: Old Dog, Reliable Tricks? A Retrospective Two-Center Cohort Study by Volkan Doğru, MD, Jean H. Ashburn, MD, Umut Akova, BS, Alton G. Sutter, MD, Eren Esen, MD, Emily M. Gardner, MD, Andre da Luz Moreira, MD, Arman Erkan, MD, John Kirat, MD, Michael J. Grieco, MD, Feza H. Remzi, MD

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Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospectiv... by Shingo Fukuma, Yukio Yuzawa, Daijo Inaguma, Hiroki Hayashi, Ryosuke Yanagiya, Akira Koseki, Toshiya Iwamori, Michiharu Kudo

“…The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics.Conclusions The random forest model could be useful in identifying patients with extremely rapid eGFR decline.Trial registration UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.…”
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Effects of milk extracellular vesicles from Holstein Friesian and Brown Swiss heat-stressed dairy cows on bovine mammary epithelial cells by S. Castellani, L. Basiricò, A. Maggiolino, C. Lecchi, P. De Palo, U. Bernabucci

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Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 by Jenberu Mekurianew Kelkay, Deje Sendek Anteneh, Henok Dessie Wubneh, Abraham Dessie Gessesse, Gebeyehu Fassil Gebeyehu, Kalkidan Kassahun Aweke, Mikiyas Birhanu Ejigu, Mathias Amare Sendeku, Kirubel Adrissie Barkneh, Hasset Girma Demissie, Wubshet D. Negash, Birku Getie Mihret

“…Region, residency, age of women, sex of child, respondent education, distance health facility, husband education and religion were top predicting factors of short birth interval among women in Ethiopia. Conclusion Random forest was best predictive models with improved performance. …”
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Ownership Succession: Plan Now for the Future of Your Land by Chris Demers

“…Published by the UF School of Forest Resources and Conservation, March 2009. FOR 212/FR274: Ownership Succession: Plan Now for the Future of Your Land (ufl.edu) …”
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Integrated Timber, Forage and Livestock Production - Benefits of Silvopasture by Jarek Nowak, Ann Blount, Sarah Workman

“… This document is Circular 1430, one of a series of the School of Forest Resources and Conservation, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. …”
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