Revealing the nature of soil liquefaction using machine learning

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 21, 2025

Язык: Английский

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Enhancing unconfined compressive strength prediction in nano-silica stabilized soil: a comparative analysis of ensemble and deep learning models

Modeling Earth Systems and Environment, Год журнала: 2024, Номер 10(4), С. 5079 - 5102

Опубликована: Май 31, 2024

Язык: Английский

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Machine learning unveils the complex nonlinearity of concrete materials’ uniaxial compressive strength

International Journal of Construction Management, Год журнала: 2024, Номер unknown, С. 1 - 15

Опубликована: Апрель 25, 2024

Uniaxial compressive strength (CS), a mechanical property that depends on the type of fine and coarse aggregates, water-cement (w/c) ratio, age, volume admixtures, etc., is difficult to estimate for concrete materials. Hence, an investigation application machine learning (ML) techniques predict UCS precisely affordably described here. Different algorithms, including ensemble models like Classification regression trees (CART), XGBoost (XGB), Bagging (Bagg), AdaBoost (AdaBo) Random Forest Regression (RF), non-ensemble Ridge (RR), Partial least square (PLS), K-Nearest Neighbors algorithm (KNN) were used. A total 1206 experimental results datasets collected. The key input parameters soft computing method, which produces CS as output, are composed nine different elements. quantity cement, aggregate, amount water, plasticizer, slump, days curing some major components. By comparing ML-derived with findings using various performance indices, study demonstrates model effectively approximates materials reliability robustness. Furthermore, research includes sensitivity analysis based developed optimal model, revealing influence mix highlighting strongly nonlinear nature

Язык: Английский

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Title: Basic Emotion Detection Accuracy Using Artificial Intelligence Approaches in Facial Emotions Recognition System: A Systematic Review.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112867 - 112867

Опубликована: Фев. 1, 2025

Язык: Английский

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Dual-stage explainable ensemble learning model for diabetes diagnosis

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126899 - 126899

Опубликована: Фев. 1, 2025

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A three layer stacked multimodel transfer learning approach for deep feature extraction from Chest Radiographic images for the classification of COVID-19

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 147, С. 110241 - 110241

Опубликована: Фев. 25, 2025

Язык: Английский

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Pre-transplant and transplant parameters predict long-term survival after hematopoietic cell transplantation using machine learning

Transplant Immunology, Год журнала: 2025, Номер unknown, С. 102211 - 102211

Опубликована: Фев. 1, 2025

Язык: Английский

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Prediction models based on machine learning algorithms for COVID-19 severity risk

BMC Public Health, Год журнала: 2025, Номер 25(1)

Опубликована: Май 13, 2025

Язык: Английский

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Fine-tuning pre-trained networks with emphasis on image segmentation: A multi-network approach for enhanced breast cancer detection

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 139, С. 109666 - 109666

Опубликована: Ноя. 18, 2024

Язык: Английский

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A novel two-enhancive aspect module in convolutional neural networks for multivariate time series classification

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125755 - 125755

Опубликована: Ноя. 1, 2024

Язык: Английский

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