Unveiling Cuproptosis-Driven Molecular Clusters and Immune Dysregulation in Ankylosing Spondylitis by Wei B, Wang S, Li S, Gu Q, Yue Q, Tang Z, Zhang J, Liu W

“…Datasets (GSE25101 and GSE73754) and ELISA to assess the expression levels of the five genes and their corresponding proteins.Results: Seven cuproptosis-related DEGs and four immune cell types were identified, revealing significant immune heterogeneity in the immune cell infiltration between the two cuproptosis-related molecular clusters in AS. The eXtreme Gradient Boosting (XGB) model showed the highest predictive accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.725, and 5-gene prediction models were established. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Soil loss was formerly modeled by USLE, thus providing non-observation-based reference datasets for the calibration of parcel-specific prediction models using various ML methods (Random Forest, eXtreme Gradient Boosting, Regularized Support Vector Machine with Linear Kernel), which is a well-established approach in digital soil mapping (DSM). …”
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Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data by Hengrui Chen, Hong Chen, Zhizhen Liu, Xiaoke Sun, Ruiyu Zhou

“…To compare the classification performance of different classifiers, we use two different classification models: eXtreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART). …”
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The Potential for a GPU-Like Overlay Architecture for FPGAs by Jeffrey Kingyens, J. Gregory Steffan

“…Through simulation of a system that (i) is programmable via NVIDIA's high-level Cg language, (ii) supports AMD's CTM r5xx GPU ISA, and (iii) is realizable on an XtremeData XD1000 FPGA-based accelerator system, we demonstrate the potential for such a system to achieve 100% utilization of a deeply pipelined floating-point datapath.…”
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Understanding summertime peroxyacetyl nitrate (PAN) formation and its relation to aerosol pollution: insights from high-resolution measurements and modeling by B. Hu, B. Hu, B. Hu, N. Chen, N. Chen, R. Li, M. Huang, M. Huang, M. Huang, J. Chen, J. Chen, Y. Hong, Y. Hong, L. Xu, L. Xu, X. Fan, X. Fan, M. Li, M. Li, L. Tong, Q. Zheng, Y. Yang

“…The MCM model, with an index of agreement (IOA) value of 0.75, effectively investigates PAN formation, performing better during the clean period (<span class="inline-formula"><i>R</i>²</span>: 0.68; slope <span class="inline-formula"><i>K</i></span>: 0.91) than the haze one (<span class="inline-formula"><i>R</i>²</span>: 0.47; slope <span class="inline-formula"><i>K</i></span>: 0.75). Using eXtreme Gradient Boosting (XGBoost), we identified NH<span class="inline-formula">₃</span>, NO<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M13" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">3</mn><mo>-</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="9pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="a192f22c747584054322d55d69a940ca"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-25-905-2025-ie00001.svg" width="9pt" height="16pt" src="acp-25-905-2025-ie00001.png"/></svg:svg></span></span>, and PM<span class="inline-formula">_2.5</span> as the primary factors for simulation bias. …”
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Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis by Zheng Zhou, Jiyuan Hu, Jiayao Wang, Lijun Wang, Tianrong Qiao, Zhen Li, Shiyuan Cheng

“…This study jointly utilised Multi-temporal synthetic aperture radar interferometry (MT-InSAR), eXtreme Gradient Boosting (XGBoost), and hydrogeological analysis to quantitatively assess the extent and trends, as well as the causes of land deformation before and after the 7·20 event in Zhengzhou city. …”
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Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling by Daphne N. Katsarou, Eleni I. Georga, Maria A. Christou, Panagiota A. Christou, Stelios Tigas, Costas Papaloukas, Dimitrios I. Fotiadis

“…., linear regression (LR), automatic relevance determination (ARD), support vector regression (SVR), Gaussian process regression (GPR), eXtreme gradient boosting (XGBoost), and long short-term memory (LSTM)), XGBoost and SVR showed a dominant performance in the hypoglycaemic region and were selected as the constituent basis models of the ensemble model. …”
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Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen by Dengkuo Sun, Yuefeng Lu, Yong Qin, Miao Lu, Zhenqi Song, Ziqi Ding

“…The results show the following: (1) Using the prediction results of multilayer perceptron (MLP) and eXtreme Gradient Boosting (XGBoost) base models as inputs and fusing them with an AdaBoost classifier as the final metamodel, the goodness of fit of the overall building renewal regression model increased by 2.19%. (2) The regression model achieved an overall urban renewal prediction accuracy of 89.41%. …”
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Study on Finger Gesture Interface Using One-Channel EMG by Hee-Yeong Yang, Young-Shin Han, Choon-Sung Nam

“…Four machine learning models were used: eXtreme Gradient Boost, Random Forest, k-Nearest Neighbors, and Logistic Regression. …”
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The performance of a machine learning model in predicting accelerometer-derived walking speed by Aleksej Logacjov, Tonje Pedersen Ludvigsen, Kerstin Bach, Atle Kongsvold, Mats Flaaten, Tom Ivar Lund Nilsen, Paul Jarle Mork

“…The video recordings were labelled and used as ground truth for training an eXtreme Gradient Boosting (XGBoost) machine learning classifier. …”
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Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study by Huanhuan Ren, Haojie Song, Shaoguo Cui, Hua Xiong, Bangyuan Long, Yongmei Li

“…A clinical model was developed using eXtreme Gradient Boosting, an NCCT-based imaging model was created using deep learning, and an ensemble model integrated both models. …”
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All-Cause Mortality Prediction in Subjects with Diabetes Mellitus Using a Machine Learning Model and Shapley Values by Oana Mirea, Mostafa Ghelich Oghli, Oana Neagoe, Mihaela Berceanu, Eugen Țieranu, Liviu Moraru, Victor Raicea, Ionuț Donoiu

“…Methods: We included 1969 consecutive patients with DM type 1 (T1DM, <i>n</i> = 255) and type 2 (T2DM, <i>n</i> = 1714). eXtreme Gradient Boosting (XGBoost) was used for the prediction of all-cause mortality in this cohort and the Shapley additive explanation (SHAP) was used to assess the importance of each feature of the classifier. …”
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Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates by Siham Acharki, Ali Raza, Dinesh Kumar Vishwakarma, Mina Amharref, Abdes Samed Bernoussi, Sudhir Kumar Singh, Nadhir Al-Ansari, Ahmed Z. Dewidar, Ahmed A. Al-Othman, Mohamed A. Mattar

“…The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. …”
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Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease by Xunliang Li, Zhijuan Wang, Wenman Zhao, Rui Shi, Yuyu Zhu, Haifeng Pan, Deguang Wang

“…Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. …”
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FPGA Acceleration of Communication-Bound Streaming Applications: Architecture Modeling and a 3D Image Compositing Case Study by Tobias Schumacher, Tim Süß, Christian Plessl, Marco Platzner

“…We provide a characterization of the memory resources available on the XtremeData XD1000 reconfigurable computer. Based on this data, we present as a case study the implementation of a 3D image compositing accelerator that is able to double the frame rate of a parallel renderer.…”
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Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction by Yu-Hang Wang, Chang-Ping Li, Jing-Xian Wang, Zhuang Cui, Yu Zhou, An-Ran Jing, Miao-Miao Liang, Yin Liu, Jing Gao

“…Subsequently, Lasso–logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. …”
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XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites by Salman Khan, Sumaiya Noor, Tahir Javed, Afshan Naseem, Fahad Aslam, Salman A. AlQahtani, Nijad Ahmad

“…By fusing word embeddings with evolutionary descriptors, it applies the SHapley Additive exPlanations (SHAP) algorithm for optimal feature selection and uses eXtreme Gradient Boosting (XGBoost) for classification. …”
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Development, validation, and clinical application of a machine learning model for risk stratification and management of cervical cancer screening based on full-genotyping hrHPV tes... by Binhua Dong, Zhen Lu, Tianjie Yang, Junfeng Wang, Yan Zhang, Xunyuan Tuo, Juntao Wang, Shaomei Lin, Hongning Cai, Huan Cheng, Xiaoli Cao, Xinxin Huang, Zheng Zheng, Chong Miao, Yue Wang, Huifeng Xue, Shuxia Xu, Xianhua Liu, Huachun Zou, Pengming Sun

“…Methods: We developed, compared and validated four machine learning models (eXtreme gradient boosting [XGBoost], support vector machine [SVM], random forest [RF], and naïve bayes [NB]) for cervical cancer prediction, using data from a national cervical cancer screening project conducted in 267 healthcare centers in China. …”
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Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study by Wei Wu, Zheng-gang Fang, Shu-qin Yang, Cai-xia Lv, Shu-yi An

“…A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA.Design Time-series study.Setting The USA was the setting for this study.Main outcome measures Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models.Results In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model.Conclusions The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.…”
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Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…We evaluated four popular classifiers (K-Nearest Neighbor (K-NN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and FeedForward Neural Network (FFNN)) on benchmark datasets CICIDS2017 and Edge-IIoTset, conducting both binary and multi-class classifications. …”
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