Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete by Ruyan Fan, Ankang Tian, Yikun Li, Yue Gu, Zhenhua Wei

“…Machine learning offers a data-driven way to predict compressive strength more efficiently. It reduces trial-and-error efforts and supports mix design optimization. …”
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Optimized CNN-LSTM with hybrid metaheuristic approaches for solar radiation forecasting by İrem Fatma Şener, İhsan Tuğal

“…The performance of several machine learning and deep learning models, including Long Short-Term Memory, Autoregressive Integrated Moving Average, Multilayer Perceptron, Random Forest, XGBoost, Support Vector Regression, and a hybrid CNN-LSTM model, is evaluated for daily solar radiation forecasting. …”
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Advancing Geotechnical Evaluation of Wellbores: A Robust and Precise Model for Predicting Uniaxial Compressive Strength (UCS) of Rocks in Oil and Gas Wells by Mohammadali Ahmadi

“…The investigation encompasses Linear Regression, ensemble methods (including Random Forest, Gradient Boosting, XGBoost, and LightGBM), support vector machine-based regression (SVM-SVR), and multilayer perceptron artificial neural network (MLP-ANN) models. …”
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Machine Learning Ensemble Classifiers for Feature Selection in Rice Cultivars by Chandrakumar Thangavel, D Sakthipriya

“…This research examines classification algorithms like K-Nearest Neighbor (KNN), Decision Tree (DT), NaiveBayes (NB), Support Vector Machine (SVM), and Random Forest (RF) with wrapper feature selection techniques like SFFS, SBEFS, CBFS, VIF, and RANDIM for environmental and seed data. …”
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Rebalancing Docked Bicycle Sharing System with Approximate Dynamic Programming and Reinforcement Learning by Young-Hyun Seo, Dong-Kyu Kim, Seungmo Kang, Young-Ji Byon, Seung-Young Kho

“…As a result, the proposed framework suggests the best operation option every 10 min based on the realized system variables and future demands predicted by the random forest method, minimizing the expected unmet demand. …”
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Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting by Michalis Patsiarikas, George Papageorgiou, Christos Tjortjis

“…Followed by preprocessing, feature engineering and selection techniques, three corresponding datasets are generated and their impact on future prices is examined, by employing ML models, such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). …”
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A novel motion key frame extraction and video stream classification based on reinforcement learning and feature fusion by Hongbo Cui, Tao Feng, Jinhui Zheng

“…Embedded feature selection method and random forest classifier are used to select the best feature subset. …”
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Communication and perception integrated positioning system in tunnel construction scenarios by Yu-song You, Zhong Shen, Liu-jie Jing, Qing-hai Yang

“…This method combines the moving average algorithm with the random forest classification algorithm, and multiple comparative experiments are conducted at the construction site. …”
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Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms by Chao Li, Jinhui Li, Zhongqi Shi, Li Li, Mingxiong Li, Dianqi Jin, Guo Dong

“…., long-short-term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) are used to predict the surface settlement. …”
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A comparative study of machine learning classifiers for intelligent fault diagnosis of electric vehicles based on FMECA data by Leila Boucerredj, Nadir Benalia

“…Furthermore, the RF model exhibited the lowest prediction error rate of 1.82%, confirming its robustness in accurately identifying faults. …”
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Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis by G. Uday Kiran, G. Nakkeeran, Dipankar Roy, Sumant Nivarutti Shinde, George Uwadiegwu Alaneme

“…The outcomes from both the training and testing phases demonstrated the strong predictive power of RSM, SVM, GB, ANN, and RF with a criterion used Root Mean square error (RMSE), Mean square error (MSE), Mean Absolute Error (MAE) and correlation coefficient (R). …”
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Prediction of the Characteristics of Concrete Containing Crushed Brick Aggregate by Marijana Hadzima-Nyarko, Miljan Kovačević, Ivanka Netinger Grubeša, Silva Lozančić

“…By testing various minimum leaf sizes and ensemble methods such as Random Forest and TreeBagger, the study evaluates metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). …”
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A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles by Yuchang Zhu, Li Ke, Yijing Wei, Xiao Zheng

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FORECASTING AUTOMOBILE DEMAND AND SALES IN THE NIGERIAN MARKET: A MACHINE LEARNING APPROACH TO URBAN MOBILITY, MARKET COMPETITION, AND POLICY INSIGHTS by Emmanuel Imuede Oyasor

“…In contrast, the MARS model underperformed, displaying the highest error rates and limited predictive capacity (R² = 0.43). …”
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Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. by Yeonuk Kim, Monica Garcia, T Andrew Black, Mark S Johnson

“…We found a strong correlation (r = 0.93) between the sensitivity of ET estimates to machine-learned parameters and model error (root-mean-square error; RMSE), indicating that reduced sensitivity minimizes error propagation and improves performance. …”
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Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting by Bambang Kun Cahyono, Muhammad Hidayatul Ummah, Ruli Andaru, Neil Andika, Adjie Pamungkas, Hepi Hapsari Handayani, Paramita Atmodiwirjo, Rory Nathan

“…Meanwhile, accuracy assessments indicated that Support Vector Regression had the most accurate predictions accompanied by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), R2, and Coefficient Correlation (CC) at 1.366, 0.947, 1.866, 0.948 and 0.982 respectively. …”
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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…Multiple machine learning algorithms, including Gradient Boosting, Light Gradient Boosting Machine (LightGBM), XGBoost, and Random Forest, are evaluated on dataset sizes of 20%, 35%, 65%, and 100%, with performance metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R 2. …”
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Research on prediction algorithm of effluent quality and development of integrated control system for waste-water treatment by JianWun Lai

“…With a Root Mean Absolute Error (RMSE) of 4.76 mg/L for 24-h horizons and a Mean Absolute Error (MAE) of 0.85 mg/L for 1-h predictions, the proposed model outperforms conventional methods in terms of prediction accuracy. …”
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The Investigation of Liability for Delegating of Excluded Lands Caused by Fault in the National Lands Detection by Majid Reza Shaykhi Nasrabadi, Sayyid Hassan Vahdati Shubairi, Muhammad Ali Saeedi, Sayyid Saeed Mousavinejad Naeini

“…It is obvious that the detection of national lands like other human activities is a process mixed with human error and in fact, the main question of this research is who is liable for compensating the losses of the executors of the delegated (disposal) projects, which occurs due to errors and mistakes in the nationalization of the excluded lands? …”
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Perbandingan Metode Supervised Machine Learning untuk Prediksi Prevalensi Stunting di Provinsi Jawa Timur by M Syauqi Haris, Ahsanun Naseh Khudori, Wahyu Teja Kusuma

“…In addition, several methods in supervised machine learning are also compared, namely, linear regression, support vector regression, and random forest regression. The support vector regression method in this study has a lower error value, namely 0.91 for MAE and 1.30 for MSE. …”
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