ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material by Zihua Chen, Runmei Zhang, Zhong Chen, Yu Zheng, Shunxiang Zhang

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Hyperparameter Optimization of ANN, SVM, and KNN Models for Classification of Hazelnuts Images Based on Shell Cracks and Feature Selection Method by H. Bagherpour, F. Fatehi, A. Shojaeian, R. Bagherpour

“…In this study, three famous machine learning models, including Support Vector Machine (SVM), K-nearest neighbors (KNN), and Multi-Layer Perceptron (MLP) were employed. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…However, machine learning models offer a time-efficient solution for predicting pavement performance. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
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Statistical and machine learning models for predicting university dropout and scholarship impact. by Stephan Romero, Xiyue Liao

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Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications by Damjan Strnad, Stefan Kohek, Borut Zalik, Libor Vasa, Andrej Nerat

“…Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. …”
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Using optimized dimensionality reduction and machine learning to explain driving processes of phytoplankton community assembly in large mountain rivers by Jingxu Ye, Daikui Li, Qi Liu, Jianying Song, Jiawei Song, Zhigang Zu, Yujun Yi

“…In this study, we employed a methodology combining optimized dimensionality reduction with advanced machine learning to construct a path analysis model for explaining the driving processes underlying phytoplankton community assembly in large mountain rivers. …”
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An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition by Micheal Dutt, Morten Goodwin, Christian W. Omlin

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Enterprise power emission reduction technology based on the LSTM–SVM model by Li Kun, Su Meng, Liu Qiang, Zhang Bin

“…Simulation experiments showed that after data warning, carbon emissions could be reduced by up to 48.26%, and electricity costs could be reduced by up to 60.48%. The machine learning-based power data warning method proposed in this study has important practical application value and can effectively help enterprises achieve emission reduction and cost control goals.…”
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Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets. …”
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A Triple-Optimized Extreme Learning Machine Model for Power Load Forecasting by Haoxiang Gao, Weixin Kang, Miao Fan

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Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review by Wenlong Wang, Zhe Wu, Dominic Peters, Berkay Citmaci, Carlos G. Morales-Guio, Panagiotis D. Christofides

“…The complexity of these systems – arising from coupled electrochemical reactions with mass, heat and charge transport phenomena – poses significant challenges in modeling, analysis, and control. Machine learning (ML) has emerged as a promising tool for addressing these challenges by providing data-driven solutions to complex process modeling, optimization, and advanced control. …”
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A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs by Alireza Mahmoudian, Mussa Mahmoudi, Mohammad Yekrangnia, Nima Tajik, Mostafa Mohammadzadeh Taleshi

“…In this study, an innovative application of machine learning is proposed to address this challenge. …”
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Optimized machine learning model for Alzheimer and epilepsy detection from EEG signals by P. Jasphin Jeni Sharmila, T. S. Shiny Angel

“…However, machine learning-based models lag in performance due to high dimensional EEG features. …”
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Empowering Diagnostics: An Ensemble Machine Learning Model for Early Liver Disease Detection by Abdulrahman Ahmed Jasim, Hajer Alwindawi, Layth Rafea Hazim

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Comparison of various machine learning regression models based on Human age prediction by Dr.Manaf K Hussein

“…In this study, five widely used machine learning  regression models (Linear support vector regression (L-SVR), radial basis function support vector regression (RBF-SVR), relevance vector regression (RVR), Elastic Net and Gaussian process regression (GPR)) were trained and evaluated to predict brain age using volumes of brain regions data. …”
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HARNESSING MACHINE LEARNING IN HPV DIAGNOSTICS: MODEL PERFORMANCE, EXPLAINABILITY, AND CLINICAL INTEGRATION by Bahar Senel

“…Future research should prioritize explainable AI (XAI), federated learning, and robust validation studies to enhance model generalizability and real-world applicability. …”
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Modeling Political Discourse in Indonesia’s 2024 Election Using Unsupervised Machine Learning by Malikhatul Ibriza, Maya Rini Handayani, Wenty Dwi Yuniarti, Khothibul Umam

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Influence of high Andean grasslands on landslide reduction in Peru by Albert Franco Cerna-Cueva, Katherin Lourdes Uriarte-Barraza, Grecia Isabel Lobatón-Tarazona, Wensty Saenz-Corrales, Casiano Aguirre-Escalante, Peter Coaguila-Rodriguez, Manuel Reategui-Inga

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Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review by Florin-Stefan Zamfir, Madalina Carbureanu, Sanda Florentina Mihalache

“…The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. …”
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