Towards ML Models’ Recommendations by Lara Kallab, Elio Mansour, Richard Chbeir

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Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model by V. Sai Gowtam, Hyunju Connor, Bharat S. R. Kunduri, Joachim Raeder, Karl M. Laundal, S. Tulasi Ram, Dogacan S. Ozturk, Donald Hampton, Shibaji Chakraborty, Charles Owolabi, Amy Keesee

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DEVELOPMENT OF DATA MESH DATA PLATFORM WITH ML DOMAIN OF DATA ANALYSIS by M. Fostyak, L. Demkiv

“…The layer of data analysis includes the ML model domain. In this domain, data classification is implemented using various classifiers. …”
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Predicting largest expected aftershock ground motions using automated machine learning (AutoML)-based scheme by Xiaohui Yu, Meng Wang, Chaolie Ning, Kun Ji

“…Subsequently, we employ a wavelet-based technique to generate synthetic aftershock accelerograms that align with the spectrum of the mainshock, using the mainshock ground motion as a reference input. …”
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A Novel Dataset for Early Cardiovascular Risk Detection in School Children Using Machine Learning by Rafael Alejandro Olivera Solís, Emilio Francisco González Rodríguez, Roberto Castañeda Sheissa, Juan Valentín Lorenzo-Ginori, José García

“…Key contributors to classification performance included hypertension, hyperreactivity, body mass index (BMI), uric acid, cholesterol, parental hypertension, and sibling dyslipidemia. These findings align with established clinical knowledge and reinforce the potential of ML models for pediatric cardiovascular risk assessment. …”
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Explainability of Subfield Level Crop Yield Prediction Using Remote Sensing by Hiba Najjar, Miro Miranda, Marlon Nuske, Ribana Roscher, Andreas Dengel

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An Efficient Methodology for the Categorization of Software Requirements Using Natural Language Processing and Similarity Analysis by Rahat Izhar, Kenneth Cosh, Lachana Ramingwon, Sakgasit Ramingwong, Shahid N. Bhatti

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Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis by Mahreen Kiran, Ying Xie, Nasreen Anjum, Graham Ball, Barbara Pierscionek, Duncan Russell

“…Key thematic clusters include foundational ML techniques, epidemiological forecasting, predictive modelling, and clinical applications. …”
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Durability prediction of sustainable marine concrete under freeze-thaw cycles using multi-objective machine learning models by Aïssa Rezzoug, Ali H. AlAteah, Sadiq Alinsaif, Sahar A. Mostafa

“…All models demonstrated strong predictive accuracy (validation R² > 0.90), aligning well with observed experimental trends. …”
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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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Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values by Gabriela Bastos Porsani, Juan Bautista Echeverría Trueba, Carlos Fernández Bandera

“…Model calibration refines design-stage inputs to align with real-world building performance. …”
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Chemometric and computational modeling of polysaccharide coated drugs for colonic drug delivery by Ahmad Khaleel AlOmari, Khaled Almansour

“…The Raman method was used for collection of spectral data which were then used as inputs to the ML models for estimation of drug release. …”
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Modeling the compressive strength behavior of concrete reinforced with basalt fiber by Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh, Viroon Kamchoom, Paul Awoyera, Siva Avudaiappan

“…Abstract This research investigates the compressive strength behavior of basalt fiber-reinforced concrete (BFRC) using machine learning models to optimize predictions and enhance its practical applications. …”
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Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany by S. Kunz, A. Schulz, M. Wetzel, M. Nölscher, T. Chiaburu, F. Biessmann, S. Broda

“…Global ML architectures enable predictions across numerous monitoring wells concurrently using a single model, allowing predictions over a broad range of hydrogeological and meteorological conditions and simplifying model management. …”
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Integrating groundwater pumping data with regression-enhanced random forest models to improve groundwater monitoring and management in a coastal region by Jamie Kim, Yueling Ma, Yueling Ma, Reed M. Maxwell, Reed M. Maxwell, Reed M. Maxwell

“…We then inverted the RERF model to predict GWP using WTD anomalies, land cover, and a cross metric as additional inputs. …”
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Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil by Tzuri Eilat, Alison McQuillan, Amichai Mitelman

“…Accurate prediction of tunneling-induced settlements in shallow tunnels in weak soil is challenging, as advanced constitutive models, such as the small-strain hardening soil model (SS-HSM) require several input parameters. …”
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Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Ahmed M. Ebid, Shadi Hanandeh, Susana Monserrat Zurita Polo, Vilma Fernanda Noboa Silva, Rodney Orlando Santillán Murillo, Rolando Fabián Zabala Vizuete, Paul Awoyera, Siva Avudaiappan

“…All models were created using 2024 “Weka Data Mining” software version 3.8.6. …”
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Use of Machine-Learning Techniques to Estimate Long-Term Wave Power at a Target Site Where Short-Term Data Are Available by María José Pérez-Molina, José A. Carta

“…The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. …”
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Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality by Avisek Kundu, Seeboli Ghosh Kundu, Santosh Kumar Sahu, Nitesh Dhar Badgayan

“…The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. …”
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Best estimate of the planetary boundary layer height from multiple remote sensing measurements by D. Zhang, J. Comstock, C. Sivaraman, K. Mo, R. Krishnamurthy, J. Tian, T. Su, Z. Li, N. Roldán-Henao

“…The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. …”
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