Implementation of XGBoost Models for Predicting CO₂ Emission and Specific Tractor Fuel Consumption by Nebojša Balać, Zoran Mileusnić, Aleksandra Dragičević, Mihailo Milanović, Andrija Rajković, Rajko Miodragović, Olivera Ećim-Đurić

“…Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization.…”
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Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model by Gang Li, Ruijiang Ran, Jun Fang, Hao Peng, Shengmin Wang

“…Then, the LSSVM with the strongest nonlinear modelling ability was selected to build the bridge construction early-warning model and adopted the SSA to optimize the LSSVM parameter combination, improving the early warning accuracy. …”
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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“…The researcher suggests increasing the data size, as it is possible to improve the accuracy of models by increasing the data size. …”
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Fusion of multi-scale attention for aerial images small-target detection model based on PARE-YOLO by Huiying Zhang, Pan Xiao, Feifan Yao, Qinghua Zhang, Yifei Gong

“…Evaluation on the VisDrone2019 dataset indicates that PARE-YOLO achieves a 5.9% improvement in mean Average Precision (mAP) at a threshold of 0.5, compared to the original YOLOv8 model. …”
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A Recognition Method for Adzuki Bean Rust Disease Based on Spectral Processing and Deep Learning Model by Longwei Li, Jiao Yang, Haiou Guan

“…Second, the competitive adaptive reweighted sampling (CARS) algorithm was implemented in the range of 425–825 nm to determine the optimal characteristic wavenumbers, thereby reducing data redundancy. …”
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A Pythagorean fuzzy MCDM model for evaluating career happiness in sports by selecting a suitable sport by JiaYan Zhu, Zeng Jiao

“…We present the MCDM algorithm for AHP and the derived AOs, offering solutions to practical numerical examples and identifying optimal sports options that improve career happiness. …”
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AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes by Ang Li, Yunxin Wang, Hongxu Chen

“…This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in biomedical diagnostics, particularly for AI-driven arrhythmia detection, hypertrophic cardiomyopathy (HCM) in athletes, and personalized medicine. …”
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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…This research addresses this gap by evaluating the predictability of machine learning approaches for evaluating the CS of rubberized mortar (RM) incorporating supplementary cementitious materials. Among the tested algorithms, including bagging, gradient boosting, and AdaBoost, the bagging model achieved the highest accuracy (R 2 = 0.975). …”
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Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…Real-time vehicle data are collected using cameras deployed along highways, and key traffic parameters such as flow, density, and speed are precisely extracted using the YOLOv8 object detection model. Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. …”
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A synergistic approach using digital twins and statistical machine learning for intelligent residential energy modelling by Ahmad Almadhor, Shtwai Alsubai, Natalia Kryvinska, Nejib Ghazouani, Belgacem Bouallegue, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…Abstract The growing need for energy efficiency in buildings has driven significant improvements in digitalisation and intelligent energy management. …”
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Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review by Muaaz Bin Kaleem, Wei He, Heng Li

“…Recently, researchers are working on the development of digital twin models to automate and optimize the BMS state estimation process by utilizing machine learning (ML) algorithms and cloud computing. …”
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Enhancing Pollen Prediction in Beijing, a Chinese Megacity: Leveraging Ensemble Learning Models for Greater Accuracy by Wenxi Ruan, Ziming Li, Zhaobin Sun, Xingqin An, Yuxin Zhao, Shuwen Zhang, Yinglin Liang, Yaqin Bu, Jingyi Xin, Xiaoyi Hang

“…The Weighted Ensemble model, which adjusts other models based on weighted optimization to mitigate excessive peaks, consistently yields stable results with an R2 exceeding 0.67. …”
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Distributed Coordinated Dispatch Model for Multi-area Interconnected Integrated Energy Systems Based on Sequential Cone Programming by Yujie REN, Yuhan HUANG, Zhenbo WEI

“…The solution of the distributed algorithm based on ATC is close to the global optimal solution of the distributed algorithm. …”
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A CART-Based Model for Analyzing the Shear Behaviors of Frozen–Thawed Silty Clay and Structure Interface by Fengpan Zhu, Bo Wang, Zhiqiang Liu

“…The physical and mechanical properties of the soil–structure interface under the freeze–thaw condition are complex, making empirical shear strength models poorly applicable. This study employs integrated machine learning algorithms to model the shear behavior of frozen–thawed silty clay and the structure interface. …”
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Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model by Yassine Bouslihim, Abdelkrim Bouasria, Budiman Minasny, Fabio Castaldi, Andree Mentho Nenkam, Ali El Battay, Abdelghani Chehbouni

“…This study presents a novel meta-learner framework that combines multiple machine learning algorithms and spectra processing algorithms to optimize SOC prediction using the PRISMA hyperspectral satellite imagery in the Doukkala plain of Morocco. …”
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The evaluation model of engineering practice teaching with complex network analytic hierarchy process based on deep learning by Xianlong Han, Xiaohui Chen

“…This study aims to help reveal the relationship between students’ performance and teaching evaluation factors, deepen the understanding of the evaluation model of engineering practice teaching in colleges and universities, and provide valuable guidance for optimizing teaching.…”
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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…The objective is to identify predictors of dropout and enhance intervention strategies.MethodsThe study utilized seven base machine learning algorithms to create a stacked ensemble model with three meta-learners: Random Forest (RF), Generalized Linear Model (GLM), and Extreme Gradient Boosting (XGBoost). …”
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A bayesian network model for neurocognitive disorders digital screening in Chinese population: development and validation study by Yifan Yu, Shuaijie Zhang, Hongkai Li, Fuzhong Xue

“…Gender and the top 30 variables with the highest coefficient of determination () in explaining the variance in NCD status were retained for model construction. Subsequently, the optimal network structure was identified using the Tabu search algorithm guided by Bayesian Information Criterion, with parameters estimated by maximum likelihood estimation. …”
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Research on primary frequency regulation and VSC-HVDC frequency synchronization control coordination method for large-scale hydropower DC export regional grid by Hongchun Shu, Huaibin Gao, Zongxue Shao, Yongzan Cui, Wei Zhao

“…An evolutionary algorithm is used to solve the optimal parameters hierarchically progressively. …”
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Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The integration of these models within an AdaBoost framework, coupled with advanced hyperparameter tuning via the Firefly Algorithm (FA), demonstrates a novel strategy for improving predictive accuracy and model robustness. …”
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