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1. Machine learning-based maximum pipeline pitting corrosion depth prediction using hybrid FVIM-BNN-XGB model NSTL国家科技图书文献中心

Sun, Shuo |  Cui, Zhendong... -  《Engineering failure analysis》 - 2025,175 - 109603~ - 共20页

摘要:The pronounced nonlinear characteristics of |  corrosion depth in buried pipelines present significant |  challenges to the accurate characterization capabilities of |  traditional experimental and statistical methods. To address |  this challenge, the study proposes a hybrid machine
关键词: Hybrid machine learning framework |  Multivariate feature engineering |  Five-fold cross-validation |  Four Vector Intelligent Metaheuristic |  Maximum corrosion depth

2. A hybrid-ensemble model for software defect prediction for balanced and imbalanced datasets using AI-based techniques with feature preservation: SMERKP-XGB NSTL国家科技图书文献中心

Mohd Mustaqeem |  Tamanna Siddiqui... -  《Journal of Software》 - 2025,37(1) - e2731~ - 共34页

摘要: hybrid-ensemble (SMERKP-XGB) model. The proposed SMERKP | -XGB model is better than previously developed models | Maintaining software quality is a significant |  challenge as the complexity of software is increasing with |  the rise of the software industry. Software defects
关键词: Classification |  EDA |  Feature optimization |  K-PCA |  PROMISE dataset |  RFE-CV |  Software defects prediction (SDP) |  XG

3. Chapter 6 Effective Optimized Detection of Cardiovascular Disease by Supervised Machine Learning Techniques NSTL国家科技图书文献中心

Rojalin Mohapatra |  Parimal Kumar Giri... -  《EAI International Conference on Computational Intelligence and Generative AI》 -  EAI International Conference on Computational Intelligence and Generative AI - 2025, - 75~88 - 共14页

摘要: (XGB classifier), etc. In order to identify the ideal | Nowadays, heart illness is highly common if |  there is substantial damage to the heart's tissue | . With early monitoring, appropriate care, and dietary |  alterations, many potential hamstring difficulties can be
关键词: Random forest |  XGB classifier |  Logistic regression |  Feature visualization |  Machine learning

4. Application of machine learning in port throughput prediction NSTL国家科技图书文献中心

Shulin Liu |  Lei Huang -  《Fourth International Conference on Computer Vision,Application,and Algorithm (CVAA 2024)》 -  International Conference on Computer Vision,Application,and Algorithm - 2025, - 134862S.1~134862S.6 - 共6页

摘要:, this study developed a combined RF-SVM-XGB port | The rapid growth of global trade has made |  ports indispensable core nodes in the global logistics |  network. The study of port throughput has become a hot |  topic for modern researchers. This study focuses on
关键词: Throughput prediction |  Machine learning models |  SVM model |  XGB model

5. Comparison and prediction of shallow groundwater nitrate in Shaying River basin based on urban distribution using multiple machine learning approaches NSTL国家科技图书文献中心

Zipeng Huang |  Baonan He... -  《Water Environment Research》 - 2025,97(2) - e70033.1~e70033.15 - 共15页

摘要: was selected. Among the array of models, the XGB | Groundwater, a pivotal water resource in |  numerous regions worldwide, confronts formidable |  challenges posed by severe nitrate pollution. Traditional |  research methodologies aimed at addressing groundwater
关键词: groundwater nitrate |  machine learning |  Shaying River Basin |  variable importance |  XGB

6. How does the built environment affect transit use under different urban village renewal strategies? NSTL国家科技图书文献中心

Cao, Shiping |  Wang, Jian... -  《Transportation planning and technology》 - 2025,48(1) - 233~254 - 共22页

摘要: 2018 Zhuhai resident survey and employed the XGB-SHAP | Authorities often adopt rehabilitation or |  redevelopment strategies to enhance the built environment (BE | ) of urban villages to address issues like traffic |  congestion, pollution, and public security. There is a lack
关键词: Transit use |  built-environment (BE) |  urban village renewal |  XGB |  TRAVEL MODE CHOICE |  RESIDENTIAL SELF-SELECTION |  BOOSTING DECISION TREES |  BEHAVIOR |  DISTANCE |  CITY...

7. Experimental Verification for Machine-Learning Approaches in Compressive Strength Prediction of Alkali-Activated Concrete NSTL国家科技图书文献中心

Alaa M. Morsy |  Sara A. Saleh... -  《Journal of Structural Design and Construction Practice》 - 2025,30(1) - 4024098.1~4024098.17 - 共17页

摘要: gradient boosting (XGB), and long short-term memory with | . Overall, the XGB and LSTM-RNN methods were observed to | This study presents a new tool for predicting |  the compressive strength of alkali-activated |  concrete (AAC) based on its binder mineralogy. It was
关键词: Alkali-activated concrete (AAC) |  Artificial intelligence (AI) |  Artificial intelligence |  Decision trees |  Deep learning |  Machine learning (ML) |  Extreme gradient boosting |  Long short-term memory (LSTM) |  Shapley additive explanations (SHAP) |  Extreme gradient boosting (XGB)

8. Prediction of surgical necessity in children with ureteropelvic junction obstruction using machine learning NSTL国家科技图书文献中心

Alici, cigdem Arslan |  Tokar, Baran -  《Irish journal of medical science》 - 2025,194(2) - 583~590 - 共8页

摘要:BackgroundHydronephrosis developing at the |  ureteropelvic junction due to obstruction poses clinical |  challenges as it has the potential to cause renal |  damage.AimsThis study aims to evaluate how well machine learning |  models such, as XGBClassifier and Logistic Regression
关键词: Artificial intelligence |  Children |  Logistic regression |  Machine learning |  Ureteropelvic junction obstruction |  XGB classifier

9. Optimizing high-strength concrete compressive strength with explainable machine learning NSTL国家科技图书文献中心

Sapkota, Sanjog Chhe... |  Panagiotakopoulou, C...... -  《Multiscale and multidisciplinary modeling, experiments and design》 - 2025,8(3) - 共26页

摘要:, and time-consuming. Extreme Gradient Boosting (XGB |  tenfold cross-validation, with the LS-XGB model | . Other models, including CSA-XGB, WS-XGB, HH-XGB, IW | -XGB, and FO-XGB, also demonstrated strong |  applied to the best-performing LS-XGB model. The
关键词: High-strength concrete |  Ensemble learning |  SHAP analysis |  Metaheuristics algorithm |  Nature inspired algorithm

10. Robust filling of extra-long gaps in eddy covariance CO 2 flux measurements from a temperate deciduous forest using eXtreme Gradient Boosting NSTL国家科技图书文献中心

Liu, Yujie |  Lucas, Benjamin... -  《Agricultural and Forest Meteorology》 - 2025,364 - 共13页

摘要: Gradient Boosting (XGB) against MDS, using various |  narrowly-prescribed set of predictor variables, with XGB |  error (RMSE) of XGB decreased by 9.5 %, and the R-2 |  validation test. XGB outperformed MDS for both day and |  carbon uptake, by -110 +/- 74 g C m(-2) y(-1) for XGB
关键词: Net ecosystem exchange |  Machine learning |  Vegetation indices |  Ameriflux |  Gap filling |  PhenoCam
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