Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach by Hai-Bang Ly, Thuy-Anh Nguyen, Binh Thai Pham

“…In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. …”
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Hybrid Symbolic Regression and Machine Learning Approaches for Modeling Gas Lift Well Performance by Samuel Nashed, Rouzbeh Moghanloo

“…The dataset covers a variety of parameters related to reservoirs, completions, and operations. …”
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Species determination using AI machine-learning algorithms: Hebeloma as a case study by Peter Bartlett, Ursula Eberhardt, Nicole Schütz, Henry J. Beker

“…Based on these data an Artificial Intelligence (AI) machine-learning species identifier has been developed that takes as input locality data and a small number of the morphological parameters. …”
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Comparative Performance of Textured Yarn Drawn through Apron and Godet in Draw Texturing Machine by Subhankar Maity, Bibekananda Basu, Abhishek Mishra

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Reculiarities of technological impact of construction machines used for pile foundation installation St. Petersburg by A. B. Ponomarev, D. A. Paskacheva, D. S. Korablev

“…The degree of construction machine impact is detected on completing construction works.Materials and methods. …”
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Optimizing Xylanase Production: Bridging Statistical Design and Machine Learning for Improved Protein Production by Merve Aslı Ergün, Başak Esin Köktürk-Güzel, Tuğba Keskin-Gündoğdu

“…Machine learning methods have emerged as effective alternatives to traditional statistical approaches in optimization. …”
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Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta, José Valeriano Alvarez-Cabal

“…To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. …”
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An Intelligent Prediction Method of the Karst Curtain Grouting Volume Based on Support Vector Machine by Jiandong Niu, Bin Wang, Haifa Wang, Zhiwei Deng, Jianxin Liu, Zewei Li, Guanjun Chen, Botao Zhang

“…In the study, based on the basic idea of support vector machine (SVM), a multiparameter comprehensive intelligent prediction method of the KCGV is proposed, which overcomes the limitation of few sample data in practical engineering. …”
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A Novel Forecasting System with Data Preprocessing and Machine Learning for Containerized Freight Market by Yonghui Duan, Xiaotong Zhang, Xiang Wang, Yingying Fan, Kaige Liu

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Phenology-Aware Machine Learning Framework for Chlorophyll Estimation in Cotton Using Hyperspectral Reflectance by Chunbo Jiang, Yi Cheng, Yongfu Li, Lei Peng, Gangshang Dong, Ning Lai, Qinglong Geng

“…This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various regression models to estimate leaf chlorophyll content (LCC) in cotton at six key reproductive stages. …”
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Chopped and minibars reinforced high-performance concrete: machine learning prediction of mechanical properties by Nikolai Ivanovich Vatin, Mohammad Hematibahar, Tesfaldet Hadgembes Gebre

“…A novel form of high-tech concrete known basalt fiber-reinforced high-performance concrete (BFHPC) has been developed using traditional materials that require extra admixtures to improve its mechanical properties. Machine learning (ML) techniques provide a more flexible and economical way to predict the mechanical property of chopped and minibar basalt fiber-reinforced high-performance concrete based on material properties and processing parameters, enabling durable and environmentally friendly construction. …”
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ALE interpretability analysis based on machine learning MOFs adsorption of tetracycline antibiotics in water by Zhang Canhui, Wang Sitan

“…According to the comparison of evaluation parameters, the model with high prediction performance for MOFs adsorption of TCs is XGB model. …”
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Power Losses in the Multi-Turn Windings of High-Speed PMSM Electric Machine Armatures by Oleksandr Makarchuk, Dariusz Całus

“…This paper investigates the dependencies between the design parameters of the armature (stator) winding of a high-speed PMSM machine and the electrical losses in its windings resulting from eddy currents. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…The initial dataset comprised over 100 parameters per patient, which was reduced to 48 clinically accessible features to ensure applicability in routine outpatient settings. …”
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Utilizing patient data: A tutorial on predicting second cancer with machine learning models by Hossein Sadeghi, Fatemeh Seif, Erfan Hatamabadi Farahani, Soraya Khanmohammadi, Shahla Nahidinezhad

“…Methods By employing machine learning (ML) models, specifically decision trees, in the research process, a practical framework is established for forecasting the occurrence of SC using patient data. …”
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Filament Type Recognition for Additive Manufacturing Using a Spectroscopy Sensor and Machine Learning by Gorkem Anil Al, Uriel Martinez-Hernandez

“…Data are collected using the Triad Spectroscopy module AS7265x (composed of AS72651, AS72652, AS72653 sensor units) positioned at three measurement distances (12 mm, 16 mm, 20 mm) to evaluate recognition performance under varying configurations. Machine learning models, including k-Nearest Neighbors (kNN), Logistic Regression, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), are employed with hyperparameter tuning applied to optimise classification accuracy. …”
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Machine Learning Prediction of Tritium‐Helium Groundwater Ages in the Central Valley, California, USA by Abdullah Azhar, Indrasis Chakraborty, Ate Visser, Yang Liu, Jory Chapin Lerback, Erik Oerter

“…The ability to predict groundwater ages based on more accessible parameters via Machine Learning (ML) would advance our ability to guide sustainable management of groundwater resources. …”
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Machine Learning‐Accelerated Reconstruction of Periodic Nanostructures with X‐ray Fluorescence Spectroscopy Methods by Vinh‐Binh Truong, Analía Fernández Herrero, Kas Andrle, Victor Soltwisch, Philipp Hönicke

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Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete by Muhammad Izhar Shah, Shazim Ali Memon, Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed

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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…This approach provides insights into the model’s decision-making process, clarifying the complex nature of machine learning algorithms. Using Shapley Additive Explanations (SHAP), time and the average size of reactive aggregate were identified as critical parameters influencing ASR expansion. …”
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