Development of a Mathematical Model for Personalized Estimation of Life Expectancy in Ukraine by Vladyslav Malanin, Illya Chaykovsky

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Methodological Aspects of Predictive Mineragenic Studies Using Earth Remote Sensing Data by Petrov Vladislav, Ustinov Stepan, Minaev Vasilii

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Mathematical procedure for prediction of dental anxiety in children with inherited bleeding disorders by Vujkov Sanja, Cveticanin Livija, Bajkin Branislav, Blagojević Duška, Milekić Bojana, Vučinić Predrag

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Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data by Jiannan He, Yongbin Zhang, Mingyue Liu, Lin Chen, Weidong Man, Hua Fang, Xiang Li, Xuan Yin, Jianping Liang, Wenke Bai, Fuping Li

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A GNN-Based QSPR Model for Surfactant Properties by Seokgyun Ham, Xin Wang, Hongwei Zhang, Brian Lattimer, Rui Qiao

“…However, the relationship between surfactant structure and these properties is complex and difficult to predict theoretically. Here, we develop a graph neural network (GNN)-based quantitative structure–property relationship (QSPR) model to predict the CMC, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>γ</mi></mrow><mrow><mi>c</mi><mi>m</mi><mi>c</mi></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>Γ</mi></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula>. …”
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IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network by Yuexu Jiang, Manish Sridhar Immadi, Duolin Wang, Shuai Zeng, Yen On Chan, Jing Zhou, Dong Xu, Trupti Joshi

“…Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. …”
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Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model by Chinedu E. Ekuma

“…THICK2D is disseminated as an open-source utility, accessible on GitHub at https://github.com/gmp007/THICK2D, and archived on Zenodo at https://10.5281/zenodo.11216648.…”
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Wave Run-Up Distance Prediction Combined Data-Driven Method and Physical Experiments by Peng Qin, Hangwei Zhu, Fan Jin, Wangtao Lu, Zhenzhu Meng, Chunmei Ding, Xian Liu, Chunmei Cheng

“…Results demonstrate that the GMM-GBR combined model achieves a coefficient of determination <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> greater than 0.91, outperforming a conventional, non-clustered GBR model. …”
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Predicting Sugarcane Yield Through Temporal Analysis of Satellite Imagery During the Growth Phase by Julio Cezar Souza Vasconcelos, Caio Simplicio Arantes, Eduardo Antonio Speranza, João Francisco Gonçalves Antunes, Luiz Antonio Falaguasta Barbosa, Geraldo Magela de Almeida Cançado

“…This study aims to evaluate the effectiveness of various modeling approaches, including a heteroskedastic gamma regression model, Random Forest, and Artificial Neural Networks, in predicting sugarcane yield based on satellite-derived vegetation indices and environmental variables. …”
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Comparative analysis of the performance of mixing rules for density prediction of simple chemical mixtures by Babalola F. U., Akanji I. O., Oyegoke T.

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Temperature-Dependent Kinetic Modeling of Nitrogen-Limited Batch Fermentation by Yeast Species by Artai R. Moimenta, Romain Minebois, David Henriques, Amparo Querol, Eva Balsa-Canto

“…Validated across five industrial <i>S. cerevisiae</i> strains in an illustrative example related to wine fermentation, the model exhibited strong predictive performance (NRMSE <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo><</mo><mn>10.5</mn><mo>%</mo></mrow></semantics></math></inline-formula>, median R²<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>></mo><mn>0.95</mn></mrow></semantics></math></inline-formula>) and enabled simulation-based process optimization, including nitrogen-supplementation strategies and strain selection for improved fermentation outcomes. …”
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Modeling and Optimization of Concrete Mixtures Using Machine Learning Estimators and Genetic Algorithms by Ana I. Oviedo, Jorge M. Londoño, John F. Vargas, Carolina Zuluaga, Ana Gómez

“…This study presents a methodology to optimize concrete mixtures by integrating machine learning (ML) and genetic algorithms. ML models are used to predict compressive strength, while genetic algorithms optimize the mixture cost under quality constraints. …”
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An IoT-enabled AI system for real-time crop prediction using soil and weather data in precision agriculture by MD Shaifullah Sharafat, Nilavro Das Kabya, Rahimul Islam Emu, Mehrab Uddin Ahmed, Jakaria Chowdhury Onik, Mohammad Aminul Islam, Riasat Khan

“…However, integrating AI models with IoT devices for instantaneous crop prediction remains a challenge due to computational constraints and the need for model interpretability. …”
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i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus by Sakshi Gautam, Anamika Thakur, Manoj Kumar

“…The i-DENV web server is freely accessible at http://bioinfo.imtech.res.in/manojk/idenv/, offering a structure-specific drug prediction platform for DENV research and antiviral drug discovery.…”
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A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study by Yanan Liu, Lei Fan, Wencai Wang, Hongxuan Song, Zhenhua Zhang, Qian Liu, Zhongji Meng, Shibo Li, Hua Wang, Shijun Zhou, Wanjun Liu, Guomei Xia, Jianping Duan, Chunxia Guo, Lu Wang, Ling Xu, Tong Wang, Hanxin Li, Xinyue Zhang, Tiandan Xiang, Di Liu, Zujiang Yu, Yuliang Liu, Junzhong Wang, Xin Zheng

“…We developed a user-friendly web-based calculator for clinical use, available at http://175.178.66.58/english/. By utilizing the UNION-SFTS model, clinicians can promptly predict and monitor the disease severity and mortality risk of SFTS patients, enabling early intervention in severe cases and ultimately reduces patient mortality.…”
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Ionospheric Prediction With High Temporal Resolution Using a Local Data Ingestion Technique Over the Chinese Region by Junchen Xue, Peng Guo, Weihua Wang

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Specifics of predicting the profitability of individual bank products based on machine learning by Inna Strelchenko, Dmytro Stognii, Anatolii Strelchenko

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Uncertainty Quantification in Shear Wave Velocity Predictions: Integrating Explainable Machine Learning and Bayesian Inference by Ayele Tesema Chala, Richard Ray

“…The results highlight the unique advantages of each model. The XGBoost model demonstrates good predictive performance, achieving high coefficient of determination (<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>), index of agreement (IA), Kling–Gupta efficiency (KGE) values, and low error values while effectively explaining the impact of input parameters on Vs. …”
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A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media by Yousef Heider, Fadi Aldakheel, Wolfgang Ehlers

“…The approach utilizes binarized CT images of porous microstructures to predict the permeability tensor, a crucial parameter in continuum porous media flow modeling. …”
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Early-Stage State-of-Health Prediction of Lithium Batteries for Wireless Sensor Networks Using LSTM and a Single Exponential Degradation Model by Lorenzo Ciani, Cristian Garzon-Alfonso, Francesco Grasso, Gabriele Patrizi

“…Various architectures and hyperparameters were explored to optimize the models’ performance. The key finding is that training one of the models with only 50 records (equivalent to 30% of battery usage) enables accurate SOH prediction, achieving a Mean Squared Error (MSE) of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.68</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></semantics></math></inline-formula> and Root Mean Squared Error (RMSE) of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.30</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>. …”
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