[HTML][HTML] Machine learning for an explainable cost prediction of medical insurance
Predictive modeling in healthcare continues to be an active actuarial research topic as more
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …
A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds
This study proposes a novel approach that combines machine learning (ML) and density
functional theory (DFT) methods to construct a quantitative structure-properties relationship …
functional theory (DFT) methods to construct a quantitative structure-properties relationship …
[HTML][HTML] Promoting the suitability of rice husk ash concrete in the building sector via contemporary machine intelligence techniques
Eco-friendly concrete is in great demand, and as a consequence, the necessity to find
sustainable alternatives to ordinary cement has become critical. In recent years, there has …
sustainable alternatives to ordinary cement has become critical. In recent years, there has …
Development of compressive strength prediction platform for concrete materials based on machine learning techniques
K Liu, L Zhang, W Wang, G Zhang, L Xu, D Fan… - Journal of Building …, 2023 - Elsevier
With the continuous development of artificial intelligence, machine learning (ML), as an
important branch, is used to promote the digitalization of concrete. Considering that the …
important branch, is used to promote the digitalization of concrete. Considering that the …
[HTML][HTML] Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric …
The construction industry is making efforts to reduce the environmental impact of cement
production in concrete by incorporating alternative and supplementary cementitious …
production in concrete by incorporating alternative and supplementary cementitious …
Machine learning in energy storage material discovery and performance prediction
Energy storage material is one of the critical materials in modern life. However, due to the
difficulty of material development, the existing mainstream batteries still use the materials …
difficulty of material development, the existing mainstream batteries still use the materials …
Implementation of quantum machine learning in predicting corrosion inhibition efficiency of expired drugs
This study explores the potential of quantum machine learning (QML)'s potential in
predicting expired pharmaceutical compounds' corrosion inhibition capacity. This …
predicting expired pharmaceutical compounds' corrosion inhibition capacity. This …
[HTML][HTML] Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites
This paper explores advanced machine learning approaches to enhance the prediction
accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers …
accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers …
[HTML][HTML] A feature restoration for machine learning on anti-corrosion materials
Materials informatics often struggles with small datasets. Our study introduces the Gaussian
Mixture Model Virtual Sample Generation (GMM-VSG) approach to enhance feature …
Mixture Model Virtual Sample Generation (GMM-VSG) approach to enhance feature …
Prediction of compressive strength of recycled aggregate concrete using machine learning and Bayesian optimization methods
X Zhang, C Dai, W Li, Y Chen - Frontiers in Earth Science, 2023 - frontiersin.org
With the sustainable development of the construction industry, recycled aggregate (RA) has
been widely used in concrete preparation to reduce the environmental impact of …
been widely used in concrete preparation to reduce the environmental impact of …