The impact of data noise on permanent deformation behaviour of asphalt concrete mixtures

Ali, Yasir; Irfan, Muhammad; Hussain, Etikaf

doi:10.1080/10298436.2018.1549324

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Furthermore, this study revealed that the BP neural network model displayed a lower predictive accuracy compared with random forest model, possibly due to the presence of data noise. Data noise refers to inaccuracies introduced by sample selection and measuring area [37]. The random forests could use the randomness of the sample and feature set to efficiently train highly generalized models, which were relatively robust against data noise [38].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Chen,

Xu,

Zhang

et al. 2024

Applied Sciences

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Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

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Section: Discussionmentioning

confidence: 99%

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Chen,

Xu,

Zhang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Furthermore, this study revealed that the BP neural network model displayed a lower predictive accuracy compared with random forest model, possibly attributed to the presence of data noise. Data noise refers to inaccuracies introduced causing by sample selection and measuring area [26]. The random forests could use randomness of the sample and feature set to efficiently trained highly generalized models, which were relatively robust against data noise [27].…”

Section: Discussionmentioning

confidence: 99%

Prediction of glass chemical composition and type identification based on machine learning algorithms

Chen

Zhang

et al. 2023

Preprint

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Glass was a significant symbol of early trade exchanges on the Silk Road. However, the glass artifacts were susceptible to environmental influences when buried underground, leading to weathering and changes in their chemical composition. Analyzing the chemical composition of ancient glass was conducive to evaluate weathering status and propose the measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learnings were used. Results indicated a significant correlation (p < 0.05) was only observed between surface weathering and the glass types (high-potassium and lead-barium). Based on the random forest and logistic regression, the primary chemical components that signifying glass types and weathering status were determined by PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented superior capability for identifying glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates great potential of machine learning for glass chemical component estimation and glass type and weathering status identification providing technical guidance for the appraisal of ancient glass artifacts.

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An eXtreme Gradient Boosting model for predicting dynamic modulus of asphalt concrete mixtures

Ali

Hussain

Irfan

et al. 2021

Construction and Building Materials

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The impact of data noise on permanent deformation behaviour of asphalt concrete mixtures

Cited by 7 publications

References 28 publications

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Prediction of glass chemical composition and type identification based on machine learning algorithms

An eXtreme Gradient Boosting model for predicting dynamic modulus of asphalt concrete mixtures

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