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  1. 1941
  2. 1942
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  4. 1944

    THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE DEVELOPMENT OF PREDICTIVE COMPETENCE IN MODERN SPECIALISTS by Viacheslav Osadchyi, Maksym Pavlenko, Liliia Pavlenko, Oleksii Sysoiev, Vladyslav Kruglyk

    Published 2025-06-01
    “…These changes significantly influence the formation of predictive competence – a crucial human capacity for reasoned forecasting and decision-making under uncertainty. …”
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    Article
  5. 1945

    A Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

    Published 2025-06-01
    “…Our results highlight the long short-term memory (LSTM) algorithm’s robustness achieving the highest accuracy of 94% and an F1-score of 0.87 along with a support vector machine (SVM), indicating high efficacy in predicting student success at the onset of learning coding. …”
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    Article
  6. 1946
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  10. 1950

    Predictive modeling and optimization of noise emissions in a palm oil methyl ester-fueled diesel engine using response surface methodology and artificial neural network integrated with genetic algorithm by J.M. Zikri, M.S.M. Sani, M.F.F.A. Rashid, J. Muriban, G.S. Prayogo

    Published 2025-03-01
    “…This research examines the predictive performance of two modeling techniques—Response Surface Methodology (RSM) and an Artificial Neural Network enhanced by a Genetic Algorithm (ANN-GA)—in relation to noise emission levels from a single-cylinder diesel engine running on palm oil methyl ester (POME). …”
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    Article
  11. 1951
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  13. 1953

    Prediction after a Horizon of Predictability: Nonpredictable Points and Partial Multistep Prediction for Chaotic Time Series by Vasilii A. Gromov, Philip S. Baranov

    Published 2023-01-01
    “…The resulting strategy demonstrates accurate results for both benchmark and real-world time series, with the number of predicted steps exceeding that of any other published algorithm.…”
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    Article
  14. 1954
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  16. 1956
  17. 1957

    Predicting equilibrium scour depth around non-circular bridge piers with shallow foundations using hybrid explainable machine learning methods by Nasrin Eini, Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, Essam Heggy, Marek Kirs

    Published 2024-12-01
    “…This study combines two metaheuristic optimization techniques—Siberian tiger optimization (STO) and brown-bear optimization algorithms (BOA)—with artificial neural networks (ANNs) to enhance deq prediction accuracy for both round- and sharp-nosed piers using both field and laboratory data. …”
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    Article
  18. 1958
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  20. 1960