Transparent and reliable construction cost prediction using advanced machine learning and explainable AI by Lifei Chen, Changyong Xu, Wei Hong Lim, Abhishek Sharma, Sew Sun Tiang, Kim Soon Chong, El-Sayed M. El-kenawy, Amel Ali Alhussan, Marwa M. Eid, Doaa Sami Khafaga

“…Among these, HistGradient Boosting achieved the best performance on the testing dataset. …”
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Enhanced dry SO₂ capture estimation using Python-driven computational frameworks with hyperparameter tuning and data augmentation by Robert Makomere, Hilary Rutto, Alfayo Alugongo, Lawrence Koech, Evans Suter, Itumeleng Kohitlhetse

“…The data-driven models executed were multilayer perceptron, support vector regressor, random forest, categorical boosting, and light gradient boosting machine. The limited experimental samples were magnified to 342 datasets using the random interpolation and random scaling augmentation procedures and analyzed using the empirical cumulative distribution function and box plots. …”
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Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading by Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, Mohammed El-Meligy, Muhammad Faisal Javed, Rizwan Ullah

“…To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques—including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network—for predicting the peak response of RC beams under impact loads. …”
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Oxidative stress-related genes in uveal melanoma: the role of CALM1 in modulating oxidative stress and apoptosis and its prognostic significance by Yue Wu, Xiaoyan Cai, Menghan Hu, Runyan Cao, Yong Wang

“…Protein–protein interaction (PPI) networks were constructed to identify hub genes, and machine learning algorithms were utilized to screen for diagnostic genes, employing methods such as least absolute shrinkage and selection operator (LASSO) regression, random forest, support vector machine (SVM), gradient boosting machine (GBM), neural network algorithm (NNET), and eXtreme gradient boosting (XGBoost). …”
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Enhancing stroke prediction models: A mixing of data augmentation and transfer learning for small-scale dataset in machine learning by Imam Tahyudin, Ade Nurhopipah, Ades Tikaningsih, Puji Lestari, Yaya Suryana, Edi Winarko, Eko Winarto, Nazwan Haza, Hidetaka Nambo

“…The classification models employed in this study were four algorithms: Random Forest, Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting. We implemented the Synthetic Minority Over-sampling Technique for Nominal and Continuous to generate the artificial dataset. …”
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Replacing Gauges with Algorithms: Predicting Bottomhole Pressure in Hydraulic Fracturing Using Advanced Machine Learning by Samuel Nashed, Rouzbeh Moghanloo

“…For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. …”
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Thirty-day mortality risk prediction for geriatric patients undergoing non-cardiac surgery in the surgical intensive care unit by Mengke Ma, Jiatong Liu, Caiyun Li, Yingxue Chen, Huishu Jia, Aijie Hou, Hongzeng Xu

“…Five predictive models were established: categorical boosting (CatBoost), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM). …”
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A Novel Approach to Retinal Blood Vessel Segmentation Using Bi-LSTM-Based Networks by Pere Marti-Puig, Kevin Mamaqi Kapllani, Bartomeu Ayala-Márquez

“…Further refinements, including pre- and post-processing and the use of image rotations to combine multiple segmentation outputs, could significantly boost performance. …”
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MELTING RATE CALCULATION ON PREHEATED WIRES OF VARIOUS CHEMISTRY UNDER ARC WELDING by Evgeny N. Varukha, Alexander S. Korobtsov, Igor S. Morozkin

“…The effect of stick - out and preheat temperature increase of the welding wires on the productivity - boosting features of their melting is asse ssed.…”
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A Novel Autonomous Robotic Vehicle-Based System for Real-Time Production and Safety Control in Industrial Environments by Athanasios Sidiropoulos, Dimitrios Konstantinidis, Xenofon Karamanos, Theofilos Mastos, Konstantinos Apostolou, Theocharis Chatzis, Maria Papaspyropoulou, Kalliroi Marini, Georgios Karamitsos, Christina Theodoridou, Andreas Kargakos, Matina Vogiatzi, Angelos Papadopoulos, Dimitrios Giakoumis, Dimitrios Bechtsis, Kosmas Dimitropoulos, Dimitrios Vlachos

“…Finally, the Q-CONPASS system was validated in a real-life environment (i.e., the lift manufacturing industry), showcasing the importance of collecting and processing data in real-time to boost productivity and improve the well-being of workers.…”
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Generating High Spatial and Temporal Surface Albedo with Multispectral-Wavemix and Temporal-Shift Heatmaps by Sagthitharan Karalasingham, Ravinesh C. Deo, Nawin Raj, David Casillas-Perez, Sancho Salcedo-Sanz

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Development of hybrid stacking machine learning for evaluating parameters affecting refrigerated shrimp coated with chitosan-loaded Salvia officinalis nanoemulsions by Mehran Sayadi, Elahe Abedi, Najmeh Oliyaei, Maryam Mousavifard

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Machine Learning in the National Economy by Azamjon A. Usmonov

“…Special attention is given to the advantages of machine learning, including improved decision-making efficiency, process automation, and handling large volumes of data. …”
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Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising by Mingjie Xie, Haibing Guo

“…This adaptive technique autonomously adjusts variable penalty parameters to expedite algorithm convergence, thereby markedly boosting algorithmic performance. Through a fusion of simulations and empirical analyses, our research demonstrates that this novel methodology significantly amplifies the efficacy of denoising processes.…”
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Fault detection and diagnosis method for heterogeneous wireless network based on GAN by Xiaorong ZHU, Peipei ZHANG

“…Aiming at the problem that in the process of network fault detection and diagnosis,how to train the precise fault diagnosis and detection model based on small data volume,a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed.Firstly,the common network fault sources in heterogeneous wireless network environment was analyzed,and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm.Then,the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data.Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks,with an accuracy of 98.18%.…”
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Research on predicting the thermocompression deformation behavior of Mg–Li matrix composite using machine learning and traditional techniques by Dandan Li, Xiaoyu Hou, Yangfan Liu, Linhao Gu, Jinhui Wang, Jiaxuan Ma, Xiaoqiang Li, Zhi Jia, Qichi Le, Dexue Liu, Xincheng Yin

“…Artificial intelligence and machine learning (ML) technologies have emerged as powerful tools for analyzing the thermal compression deformation behavior of metal matrix composites, offering significant potential to optimize their plastic deformation processing techniques. In this study, the Al3La/LAZ532 composite based on in-situ self-reaction technology was successfully prepared by adding La2O3 particles. …”
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A superlattice interface and S-scheme heterojunction for ultrafast charge separation and transfer in photocatalytic H2 evolution by Sijie Wan, Wang Wang, Bei Cheng, Guoqiang Luo, Qiang Shen, Jiaguo Yu, Jianjun Zhang, Shaowen Cao, Lianmeng Zhang

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Landslide and Collapse Susceptibility Analysis in Wenchuan Earthquake-damaged Area Based on Ensemble Learning Methods by DING Jiawei, WANG Xiekang

“…Two state-of-the-art ensemble learning algorithms, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), are introduced to formulate dependable models for appraising susceptibility to landslides and collapses within the confines of Wenchuan County.MethodsA comprehensive evaluation of factors related to topography, geology, meteorology, and hydrology was conducted to select ten evaluative factors: Elevation, slope, aspect, terrain relief, distance to rivers, distance to faults, normalized difference vegetation index (NDVI), land cover type, average annual precipitation, and lithology. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

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Soil Salinity Mapping of Plowed Agriculture Lands Combining Radar Sentinel-1 and Optical Sentinel-2 with Topographic Data in Machine Learning Models by Diego Tola, Frédéric Satgé, Ramiro Pillco Zolá, Humberto Sainz, Bruno Condori, Roberto Miranda, Elizabeth Yujra, Jorge Molina-Carpio, Renaud Hostache, Raúl Espinoza-Villar

“…The most reliable salinity estimates are obtained for the R+O+T scenario, considering the feature selection process, with R² of 0.73, 0.74, 0.75, and 0.76 for DT, GB, RF, and XGB, respectively. …”
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