Ensemble machine learning paradigms in hydrology: A review
Recently, there has been a notable tendency towards employing ensemble learning
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
A review of ensemble learning algorithms used in remote sensing applications
Y Zhang, J Liu, W Shen - Applied Sciences, 2022 - mdpi.com
Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …
Deep model reassembly
In this paper, we explore a novel knowledge-transfer task, termed as Deep Model
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …
Predrnn: A recurrent neural network for spatiotemporal predictive learning
The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …
learning from the historical context, where the visual dynamics are believed to have modular …
Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system
The mission of an intrusion detection system (IDS) is to monitor network activities and
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …
A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges
Abstract Internet of Things (IoT) is widely accepted technology in both industrial as well as
academic field. The objective of IoT is to combine the physical environment with the cyber …
academic field. The objective of IoT is to combine the physical environment with the cyber …
Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
DC Feng, ZT Liu, XD Wang, Y Chen, JQ Chang… - … and Building Materials, 2020 - Elsevier
In this paper, an intelligent approach based on the machine learning technique is proposed
for predicting the compressive strength of concrete. This approach employs the adaptive …
for predicting the compressive strength of concrete. This approach employs the adaptive …
Machine learning from theory to algorithms: an overview
Abstract The current SMAC (Social, Mobile, Analytic, Cloud) technology trend paves the way
to a future in which intelligent machines, networked processes and big data are brought …
to a future in which intelligent machines, networked processes and big data are brought …
Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements
This paper presents a practical yet comprehensive implementation of the ensemble methods
for prediction of the shear strength for reinforced concrete deep beams with/without web …
for prediction of the shear strength for reinforced concrete deep beams with/without web …
Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big …
The water quality prediction performance of machine learning models may be not only
dependent on the models, but also dependent on the parameters in data set chosen for …
dependent on the models, but also dependent on the parameters in data set chosen for …