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  1. 1521

    Machine Learning with Voting Committee for Frost Prediction by Vinícius Albuquerque de Almeida, Juliana Aparecida Anochi, José Roberto Rozante, Haroldo Fraga de Campos Velho

    Published 2025-02-01
    “…A machine learning (ML)-based methodology for predicting frosts was applied to the southern and southeastern regions of Brazil, as well as to other countries including Uruguay, Paraguay, northern Argentina, and southeastern Bolivia. …”
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  2. 1522

    Predicting surgical risk in morbidly obese patients by K. A. Anisimova, D. I. Vasilevsky, S. G. Balandov, E. T. Berulava, A. V. Zinchenko, N. V. Markov, I. G. Buhankov, E. V. Blinov, G. V. Semikova

    Published 2024-10-01
    “…The results obtained during the study made it possible to integrate the developed tactics of preoperative examination and preparation for surgical intervention in morbidly obese patients into a practical algorithm. Application of the developed tools for predicting the risk of complications in bariatric surgeries allowed to reduce the complication rate from 12.2 % to 2.0 %, and the mortality rate from 2.0 % to 0 %.CONCLUSION. …”
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  3. 1523

    Machine Learning for Health Insurance Prediction in Nigeria by Victor Enemona Ochigbo, Oluwasogo Adekunle Okunade, Emmanuel Gbenga Dada, Oluyemi Mikail Olaniyi, Oluwatoyosi Victoria Oyewande

    Published 2024-12-01
    “…This paper focused on predicting the likelihood of medical insurance coverage among individuals in Nigeria by employing four prominent Machine learning techniques: Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers. …”
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  4. 1524

    Population FBA predicts metabolic phenotypes in yeast. by Piyush Labhsetwar, Marcelo C R Melo, John A Cole, Zaida Luthey-Schulten

    Published 2017-09-01
    “…We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. …”
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  5. 1525

    STEP-BY-STEP PREDICTION OF LATE SPONTANEOUS MISCARRIAGE by Юлия Алексеевна Дударева, Татьяна Викторовна Раченкова, Сергей Вадимович Дронов

    Published 2025-05-01
    “…The aim of the study was to develop a step-by-step system for predicting late spontaneous miscarriages, taking into account clinical, anamnestic, echographic data, and the cytokine status of pregnant women. …”
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  6. 1526

    AI model for predicting asthma prognosis in children by Elham Sagheb, MS, Chung-Il Wi, MD, Katherine S. King, MS, Bhavani Singh Agnikula Kshatriya, MS, Euijung Ryu, PhD, Hongfang Liu, PhD, Miguel A. Park, MD, Hee Yun Seol, MD, Shauna M. Overgaard, PhD, Deepak K. Sharma, PhD, Young J. Juhn, MD, Sunghwan Sohn, PhD

    Published 2025-05-01
    “…Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans. …”
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  7. 1527

    Prediction models for COVID-19 disease outcomes by Cynthia Y. Tang, Cheng Gao, Kritika Prasai, Tao Li, Shreya Dash, Jane A. McElroy, Jun Hang, Xiu-Feng Wan

    Published 2024-12-01
    “…Using the Virus-Human Outcomes Prediction (ViHOP) algorithm, we aim to utilize the individual’s clinical characteristics, the individual’s location, and the infecting SARS-CoV-2 virus characteristics obtained by whole genome sequencing to determine their likelihood of admission to the hospital, admission to the intensive care unit (ICU), or experiencing long COVID. …”
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  8. 1528

    Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

    Published 2025-06-01
    “…The use of deep learning and neural network algorithms enhances the accuracy of these predictions by capturing complex data trends over time. …”
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  9. 1529
  10. 1530

    Prediction of 1-year post-operative mortality in elderly patients with fragility hip fractures in China: evaluation of risk prediction models by Qiyuan Lu, Mengmeng Chen, Houfu Ling

    Published 2025-06-01
    “…This investigation conducts a comparative evaluation of seven prognostic models—the Sernbo Score, Jiang et al. model, Nottingham Hip Fracture Score (NHFS), Holt et al. algorithm, HEMA, ASAgeCoGeCC Score, and SHiPS—HiPSe, and SHire, and SHim, HEMA, ham Hip Fracture Score (mortality risk prediction in elderly fragility hip fracture patientsMethodsIn this retrospective cohort analysis, all consecutive patients aged isk prediction in elderly fragility hip fracture between January 2018 and October 2022 were enrolled. …”
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  11. 1531

    An Adaptive Maximum Power Output Sustaining System for a Photovoltaic Power Plant Based on a Robust Predictive Control Approach by I. Elzein, Yu. N. Petrenko

    Published 2020-10-01
    “…This paper makes an emphasis on model predictive controller as a control method for controlling the maximum power point tracking through the utilization of the well-known algorithm namely the Perturb and Observe technique. …”
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  12. 1532

    Decision Tree Methodology (C4.5) for Predicting Students' Reading Interest in the Library SMK Negeri 1 Kota Cirebon by Muhammad Erwanto, Kosim Kosim, Nur Bambang Riyanto, Sukmo Banyu Jogo

    Published 2025-03-01
    “…This step is done by designing a system model that uses the C4.5 algorithm to form a decision tree to produce a rule for predicting student reading interest. …”
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  13. 1533

    Application of log-based specific surface area prediction for permeability modeling in a highly heterogeneous carbonate reservoir in the middle east by Mojtaba Homaie, Ida Lykke Fabricius, Morten Leth Hjuler, Asadollah Mahboubi, Ali Kadkhodaie, Reza Moussavi Harami

    Published 2025-08-01
    “…This study evaluates two log-based methodologies for specific surface modeling and their role in predicting permeability. The first method utilizes density and gamma-ray logs, as previously validated in North Sea chalks, while the second method innovatively integrates deep resistivity and porosity data using a K-nearest neighbor machine learning algorithm. …”
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  14. 1534
  15. 1535

    Cutting-edge approaches to specific energy prediction in TBM disc cutters: Integrating COSSA-RF model with three interpretative techniques by Jian Zhou, Zijian Liu, Chuanqi Li, Kun Du, Haiqing Yang

    Published 2025-06-01
    “…Therefore, in this paper, the sparrow search algorithm (SSA), combined with six chaotic mapping strategies, is utilized to optimize the random forest (RF) model for predicting SE, referred to as the COSSA-RF prediction models. …”
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  16. 1536
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  20. 1540

    Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model by Dan Li, Jiwei Qu, Delan Zhu, Zheyu Qin

    Published 2024-11-01
    “…The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict solar radiation. …”
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