A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
The optimal co-planning of the integrated energy system (IES) and machine learning (ML)
application on the multivariable prediction of IES parameters have mostly been carried out …
application on the multivariable prediction of IES parameters have mostly been carried out …
Prediction of concrete and FRC properties at high temperature using machine and deep learning: a review of recent advances and future perspectives
Concrete structures when exposed to elevated temperature significantly decline their
original properties. High temperatures substantially affect the concrete physical and …
original properties. High temperatures substantially affect the concrete physical and …
An improved random forest based on the classification accuracy and correlation measurement of decision trees
Z Sun, G Wang, P Li, H Wang, M Zhang… - Expert Systems with …, 2024 - Elsevier
Random forest is one of the most widely used machine learning algorithms. Decision trees
used to construct the random forest may have low classification accuracies or high …
used to construct the random forest may have low classification accuracies or high …
A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks
The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on
everyday life to large industrial systems. Unfortunately, this has attracted the attention of …
everyday life to large industrial systems. Unfortunately, this has attracted the attention of …
[PDF][PDF] Science and Business
NM Abdulkareem, AM Abdulazeez - International Journal, 2021 - academia.edu
Machine Learning is a significant technique to realize Artificial Intelligence. The Random
Forest Algorithm can be considered as one of the Machine Learning's representative …
Forest Algorithm can be considered as one of the Machine Learning's representative …
Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical …
This study employs various machine learning algorithms to model the electrical conductivity
and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a …
and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a …
A deep learning ensemble with data resampling for credit card fraud detection
Credit cards play an essential role in today's digital economy, and their usage has recently
grown tremendously, accompanied by a corresponding increase in credit card fraud …
grown tremendously, accompanied by a corresponding increase in credit card fraud …
Comparative study of regressor and classifier with decision tree using modern tools
Abstract Machine Learning is one of the importantareas for modeling the data and itcan be
saidthat this is the core part of the field of Data Science. Supervised Machine Learning …
saidthat this is the core part of the field of Data Science. Supervised Machine Learning …
Intelligent technologies for construction machinery using data-driven methods
Z Zheng, F Wang, G Gong, H Yang, D Han - Automation in Construction, 2023 - Elsevier
Along with the rapid development of infrastructure worldwide, traditional manual operations
have been a concern that restricts the high efficiency, safety, and quality of construction …
have been a concern that restricts the high efficiency, safety, and quality of construction …
An interpretable machine learning approach for hepatitis b diagnosis
Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious
public health problem globally. Substantial efforts have been made to apply machine …
public health problem globally. Substantial efforts have been made to apply machine …