Prediction Model for Soybean Productivity by Ion GANEA

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Models for predicting juvenile hypertension by Vytautas Janilionis, Edita Janavičiūtė, Jūratė Klumbienė, Giedrė Sakalauskienė

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Modelling complexity : The limits to prediction by Michael Batty, Paul M. Torrens

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Atmospheric Modeling for Wildfire Prediction by Fathima Nuzla Ismail, Brendon J. Woodford, Sherlock A. Licorish

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Study on predictive models for COVID-19 in Cuba by Juan Felipe Medina Mendieta, Manuel Eduardo Cortés Cortés, Manuel Cortés Iglesias, Annia del Carmen Pérez Fernández, Marianelis Manzano Cabrera

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Predictive model for necrotising fasciitis (NF) outcome by Sushmitha Kalakeri, Utsav Balar, Gehad Abdelkhalek

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On the Relevance and Limitations of Predictive Models in Clinical Psychology by Emanuele Maria Merlo

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DEVELOPING PREDICTIVE MODELS OF INTERNET SERVICE STRATEGIES by Maxim Yu. Khramov

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Fuzzy Model Predictive Control for Unmanned Helicopter by Łukasz Kiciński, Sebastian Topczewski

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Research on Simplified Design of Model Predictive Control by Qing Zhang, Chi Zhang, Qi Wang, Shiyun Dong, Aoqi Xiao

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A Predictive Model of the HIV Infection Course by E. I. Veselova, G. D. Kaminskiy, K. A. Kocharyan, A. B. Peregudova, O. V. Lovacheva, A. G. Samoylova

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Risk-Stratified Predictive Models of Pregnancy Loss by O. V. Shirai, B. I. Aslanov, S. V. Rishchuk, S. E. Melnikova

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BiDGCNLLM: A Graph–Language Model for Drone State Forecasting and Separation in Urban Air Mobility Using Digital Twin-Augmented Remote ID Data by Zhang Wen, Junjie Zhao, An Zhang, Wenhao Bi, Boyu Kuang, Yu Su, Ruixin Wang

“…Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> by 11.59%. …”
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Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics by Md Abu Sufian, Lujain Alsadder, Wahiba Hamzi, Sadia Zaman, A. S. M. Sharifuzzaman Sagar, Boumediene Hamzi

“…<b>Background/Objectives</b>: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. …”
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Model development to improve the predictive maintenance reliability of medical devices by Khalid Musallam Alahmadi, Essam Rabea Ibrahim Mahmoud, Fitrian Imaduddin

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Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

“…For optical imagery, the Bayesian-optimized XGBoost model achieves the best performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>=</mo><mn>0.9870</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RMSE</mi><mo>=</mo><mi>$</mi><mn>3.44</mn><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>NSE</mi><mo>=</mo><mn>0.9651</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>KGE</mi><mo>=</mo><mn>0.8950</mn></mrow></semantics></math></inline-formula>), followed closely by CatBoost (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>=</mo><mn>0.9826</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RMSE</mi><mo>=</mo><mi>$</mi><mn>3.83</mn><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>). …”
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Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices by Gavriela Asiminari, Lefteris Benos, Dimitrios Kateris, Patrizia Busato, Charisios Achillas, Claus Grøn Sørensen, Simon Pearson, Dionysis Bochtis

“…The gradient-boosting regression-based model was the most effective, achieving a high mean <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> value of 0.931 in predicting field efficiency, by taking into account only basic geometric field indices. …”
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Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon by Nicolás Amigo

“…The MD simulations provided a detailed dataset that captured the atomic-level behavior of the a-Si, which enabled exploration of how thermodynamic factors, such as the cooling rate, temperature, and pressure, affect the material’s density, internal energy, and enthalpy. Machine learning models were trained on this dataset and demonstrated exceptional predictive accuracy with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> values that exceeded 0.95 and minimal root-mean-square errors. …”
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An epidemiological model of monkeypox: model prediction and control application by Min Qian, Danyang Li, Zihan Hao, Shujuan Hu, Weide Li

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Heuristic Model of Forecasting of Operating State of Semiconductor Devices by V. O. Kaziuchyts, S. M. Borovikov, E. N. Shneiderov

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