A survey of ensemble learning: Concepts, algorithms, applications, and prospects
Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …
machine learning applications by combining the predictions from two or more base models …
An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks
Abstract Internet of Medical Things (IoMT), an application of Internet of Things (IoT), is
addressing countless limitation of traditional health-care systems such as quality of patient …
addressing countless limitation of traditional health-care systems such as quality of patient …
Machine learning—a review of applications in mineral resource estimation
NK Dumakor-Dupey, S Arya - Energies, 2021 - mdpi.com
Mineral resource estimation involves the determination of the grade and tonnage of a
mineral deposit based on its geological characteristics using various estimation methods …
mineral deposit based on its geological characteristics using various estimation methods …
A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks
With the development of internet of things (IoT), capabilities of computing, networking
infrastructure, storage of data and management have come very close to the edge of …
infrastructure, storage of data and management have come very close to the edge of …
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 …
A literature review on satellite image time series forecasting: Methods and applications for remote sensing
C Lara‐Alvarez, JJ Flores… - … : Data Mining and …, 2024 - Wiley Online Library
Satellite image time‐series are time series produced from remote sensing images; they
generally correspond to features or indicators extracted from those images. With the …
generally correspond to features or indicators extracted from those images. With the …
Smart crawfish: A concept of underwater multi-bolt looseness identification using entropy-enhanced active sensing and ensemble learning
With the rapid development of the oil industry, more and more subsea pipelines enter into
service, and thus we are facing the challenge of safe operation and maintenance of these …
service, and thus we are facing the challenge of safe operation and maintenance of these …
A normal behavior model based on power curve and stacked regressions for condition monitoring of wind turbines
Wind turbines (WTs) are complex systems composed of multiple components. In order to
assess the overall condition of a given WT, one may need to employ multiple models; such a …
assess the overall condition of a given WT, one may need to employ multiple models; such a …
A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical …
E Yaghoubi, E Yaghoubi, A Khamees… - Neural Computing and …, 2024 - Springer
Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble
learning (EL) are four outstanding approaches that enable algorithms to extract information …
learning (EL) are four outstanding approaches that enable algorithms to extract information …
A systematic literature review: usage of logistic regression for malware detection
Malwares are serious threats since decades and now they are becoming a huge risk due to
the increasing nature of their attacks. At first computer virus named “brain” was introduced …
the increasing nature of their attacks. At first computer virus named “brain” was introduced …