[HTML][HTML] Application of machine learning to stress corrosion cracking risk assessment
AH Alamri - Egyptian Journal of Petroleum, 2022 - Elsevier
One of the greatest challenges faced by industries today is corrosion and of which, one of
the most vital forms is stress corrosion cracking (SCC). It brings highest forms of risks to the …
the most vital forms is stress corrosion cracking (SCC). It brings highest forms of risks to the …
Predictive modeling of biomass gasification with machine learning-based regression methods
Biomass gasification is a promising power generation process due to its ability to utilize
waste materials and similar renewable energy sources. Predicting the outcomes of this …
waste materials and similar renewable energy sources. Predicting the outcomes of this …
Hydrogen production optimization from sewage sludge supercritical gasification process using machine learning methods integrated with genetic algorithm
Hydrogen production from the supercritical water gasification (SCWG) of sewage sludge
(SS) is a sustainable and efficient process. However, the challenging and intricate task for …
(SS) is a sustainable and efficient process. However, the challenging and intricate task for …
[PDF][PDF] The effect of gamma value on support vector machine performance with different kernels
Currently, the support vector machine (SVM) regarded as one of supervised machine
learning algorithm that provides analysis of data for classification and regression. This …
learning algorithm that provides analysis of data for classification and regression. This …
Data-driven interpretation, comparison and optimization of hydrogen production from supercritical water gasification of biomass and polymer waste: Applying …
S Azadvar, O Tavakoli - International Journal of Hydrogen Energy, 2024 - Elsevier
Supercritical water gasification (SCWG) has been utilized for producing hydrogen from
organic wastes, which is associated with sustainable development. Nevertheless, identifying …
organic wastes, which is associated with sustainable development. Nevertheless, identifying …
How the accuracy and confidence of sensitivity classification affects digital sensitivity review
Government documents must be manually reviewed to identify any sensitive information, eg,
confidential information, before being publicly archived. However, human-only sensitivity …
confidential information, before being publicly archived. However, human-only sensitivity …
[HTML][HTML] Predicting overall mass transfer coefficients of CO2 capture into monoethanolamine in spray columns with hybrid machine learning
In order to avoid the catastrophic effects of global warming, we need to reduce CO 2
emissions. Currently, the most mature technology to reduce large industrial CO 2 emissions …
emissions. Currently, the most mature technology to reduce large industrial CO 2 emissions …
Thermal optimization of Li-ion battery pack using genetic algorithm integrated with machine learning
This research work is focused on the “Air-Cooled” Battery Thermal Management System
(BTMS) through the optimization of cell spacing of the battery pack. The Computational Fluid …
(BTMS) through the optimization of cell spacing of the battery pack. The Computational Fluid …
An Improved Double Channel Long Short‐Term Memory Model for Medical Text Classification
There are a large number of symptom consultation texts in medical and healthcare Internet
communities, and Chinese health segmentation is more complex, which leads to the low …
communities, and Chinese health segmentation is more complex, which leads to the low …
SI (FS) 2: Fast simultaneous instance and feature selection for datasets with many features
N Garcia-Pedrajas, JAR del Castillo… - Pattern Recognition, 2021 - Elsevier
Data reduction is becoming increasingly relevant due to the enormous amounts of data that
are constantly being produced in many fields of research. Instance selection is one of the …
are constantly being produced in many fields of research. Instance selection is one of the …