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

“…This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized linear model (GLM) to enhance both predictive accuracy and uncertainty quantification in Vs prediction. …”
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Experimental and analytical justification of the asphalt concrete cutting process by road milling machines working equipment by D. V. Furmanov, N. E. Lysakov, L. M. Shamakhov

“…To calculate the parameters of the working body of the road milling machine.Materials and methods. …”
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Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review by Fernando Venâncio Mucomole, Carlos Augusto Santos Silva, Lourenço Lázaro Magaia

“…The included studies’ statistically measured parameters showed high trends of dependence on the variability in transmittances. …”
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A novel machine learning-based approach to thermal integrity profiling of concrete pile foundations by Javier Sánchez Fernández, Agustín Ruiz López, David M.G. Taborda

“…This work demonstrates the applicability and robustness of machine learning algorithms in enhancing nondestructive TIP testing of concrete foundations, thereby improving the safety and efficiency of civil engineering projects.…”
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Integrating Inverse Data Envelopment Analysis and Machine Learning for Enhanced Road Transport Safety in Iran by Seyyed Mojtaba Tabatabai, Fatemeh Al-Sadat Tabatabai

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Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber by T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale, L. K. Nayak, D. B. Shakyawar, Indran Suyambulingam, P. Senthamaraikannan, R. Kumar

“…In this study, a total of 432 patterns of input and output parameters obtained from laboratory experiments were used to develop machine learning algorithms (Random forest, support vector, and XGBoost). …”
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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

“…Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. …”
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Estimating Energy Consumption During Soil Cultivation Using Geophysical Scanning and Machine Learning Methods by Jasper Tembeck Mbah, Katarzyna Pentoś, Krzysztof S. Pieczarka, Tomasz Wojciechowski

“…These data, along with soil texture, served as inputs for predicting fuel consumption and field productivity. Three machine learning algorithms were tested: support vector machines (SVMs), multilayer perceptron (MLP), and radial basis function (RBF) neural networks. …”
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Machine learning based on patch antenna design and optimization for 5 G applications at 28GHz by Md․Sohel Rana, Sheikh Md․ Rabiul Islam, Sanjukta Sarker

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Schistosomiasis transmission: A machine learning analysis reveals the importance of agrochemicals on snail abundance in Rwanda. by Joseph Kagabo, Zadoki Tabo, Chester Kalinda, Elias Nyandwi, Nadine Rujeni

“…Water physico-chemical parameters were collected at all snail sampling sites. …”
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Research on Prediction model of Carotid‐Femoral Pulse Wave Velocity: Based on Machine Learning Algorithm by Minghui Chen, Jing Xiong, Moran Li, Tao Hu, Yi Zhang

“…It is feasible to use a machine learning algorithm based on baPWV and other readily available clinical parameters to predict cf‐PWV.…”
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Research and Simulation of Metro Pantograph-catenary SystemBased on Least Square Support Vector Machine by Jiang Wei, Huang Yuping

“…A model of pantograph-catenary system based on the error correlation was put forward, and the least squares supportvector machine (LSSVM) method was used to study the parameters of the model. …”
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Machine learning enhanced formation pressure prediction using integrated well logging and mud logging by Jiwen Liang, Ming Luo, Wentuo Li, Bo Sun, Chuanliang Yan, Zhongying Han, Yuanfang Cheng

“…The well logging and mud logging data were combined to analyze the correlation between various parameters. Analysis using the Spearman correlation coefficient revealed that pore pressure exhibits varying correlation relationships with different parameters. …”
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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management by Ying Deng, Yue Zhang, Daiwei Pan, Simon X. Yang, Bahram Gharabaghi

“…This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. …”
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Electrogastrogram-based detection of cybersickness with the application of wavelet transformation and machine learning: A case study by Ilija V. Tanasković, Nenad B. Popović, Jaka J. Sodnik, Sašo J. Tomažič, Nadica S. Miljković

“…The paper employs a 3-level discrete wavelet transformation (DWT) on the chosen channel to identify key parameters indicative of gastric disturbances. Furthermore, the paper investigates recovery from CS following VR and examines the application of unsupervised machine learning (ML) for segmenting EGG into baseline and CS, utilizing significant features previously identified. …”
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Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment by Pallavi Randive, Madhuri S. Bhagat, Mangesh P. Bhorkar, Rajesh M. Bhagat, Shilpa M. Vinchurkar, Sagar Shelare, Shubham Sharma, N. Beemkumar, S. Hemalatha, Parveen Kumar, Ankit Kedia, Ehab El Sayed Massoud, Deepak Gupta, Jasmina Lozanovic

“…They are inaccurate in predicting the complex interactions of jet mixing parameters, coagulant dosage, and environmental conditions. …”
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Novel PCA-driven extreme machine learning for comprehensive modelling of metropolitan wastewater treatment systems by Vini Antony Grace N, Ghadah Aldehim, Nuha Alruwais, Prabakar T.N.

“…This study models metropolitan wastewater treatment plants (MWWTPs) in Kolkata using an Extreme Learning Machine (ELM) combined with Principal Component Analysis (PCA). …”
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Research of the second pneumeseparating channel of the machine for preliminary grain cleaning MPO-30R «VELES» by A. L. Glushkov, Yu. V. Sychugov, V. A. Lazykin

“…This was done in order to study the impact of structural and technological parameters of this channel on the effect of cleaning the grain material from light impurities and determining the optimal values of the parameters studied. …”
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Differentiating Emphysema From Emphysema-Dominated COPD Patients with CT Imaging Feature and Machine Learning by Guo W, Li M, Li Y, Fan X, Wu L

“…The differing relationships between QCT parameters and lung function in these two groups suggest distinct pathophysiological processes. …”
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Singularity of Spatial 3-PRR Parallel Mechanism and Its Application in Irregular Surface Engraving Machine by Bin Sun, Ruiqin Li, Jingjing Liang

“…Based on the given parameters and constraints，the reachable workspace of the moving platform is determined by using workspace search method. …”
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