Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning
Cancer is a fatal disease caused by a combination of genetic diseases and a variety of
biochemical abnormalities. Lung and colon cancer have emerged as two of the leading …
biochemical abnormalities. Lung and colon cancer have emerged as two of the leading …
Explainable ensemble learning data-driven modeling of mechanical properties of fiber-reinforced rubberized recycled aggregate concrete
Colossal amounts of construction and demolition waste (C&D) and waste tires have become
a considerable global environmental concern. To alleviate this issue, it is proposed to use …
a considerable global environmental concern. To alleviate this issue, it is proposed to use …
Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers
Carbon dioxide storage in underground saline aquifers is considered a promising technique
for decreasing atmospheric CO 2 emissions. The CO 2 residual and solubility in deep saline …
for decreasing atmospheric CO 2 emissions. The CO 2 residual and solubility in deep saline …
Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model
This study conducted a comprehensive analysis of multiple supervised machine learning
models, regressors and classifiers, to accurately predict diamond prices. Diamond pricing is …
models, regressors and classifiers, to accurately predict diamond prices. Diamond pricing is …
Prediction of hydrogen solubility in aqueous solutions: Comparison of equations of state and advanced machine learning-metaheuristic approaches
Hydrogen is the primary carrier of renewable energy stored underground. Understanding
the solubility of hydrogen in water is critical for subsurface storage. Accurately measuring the …
the solubility of hydrogen in water is critical for subsurface storage. Accurately measuring the …
Modelling CO2 diffusion coefficient in heavy crude oils and bitumen using extreme gradient boosting and Gaussian process regression
Q Lv, A Rashidi-Khaniabadi, R Zheng, T Zhou… - Energy, 2023 - Elsevier
In this work, five machine learning models based on Gaussian process regression (GPR)
and Extreme gradient boosting (XGBoost) were developed for estimating the diffusion …
and Extreme gradient boosting (XGBoost) were developed for estimating the diffusion …
Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme
This study explores the use of machine learning algorithms to predict hydrogen wettability in
underground storage sites. The motivation for this research is the need to find a safe and …
underground storage sites. The motivation for this research is the need to find a safe and …
Modeling solubility of CO2–N2 gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state
R Nakhaei-Kohani, E Taslimi-Renani… - Scientific Reports, 2022 - nature.com
Determining the solubility of non-hydrocarbon gases such as carbon dioxide (CO2) and
nitrogen (N2) in water and brine is one of the most controversial challenges in the oil and …
nitrogen (N2) in water and brine is one of the most controversial challenges in the oil and …
Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state
Abstract Knowledge of the solubilities of hydrocarbon components of natural gas in pure
water and aqueous electrolyte solutions is important in terms of engineering designs and …
water and aqueous electrolyte solutions is important in terms of engineering designs and …
Application of robust machine learning methods to modeling hydrogen solubility in hydrocarbon fuels
MR Mohammadi, F Hadavimoghaddam… - International Journal of …, 2022 - Elsevier
Having accurate information about the hydrogen solubility in hydrocarbon fuels and
feedstocks is very important in petroleum refineries and coal processing plants. In the …
feedstocks is very important in petroleum refineries and coal processing plants. In the …