In big data fields, with increasing computing capability, artificial neural networks have shown
great strength in solving data classification and regression problems. The traditional training …

Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …

We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …

Countering cyber threats, especially attack detection, is a challenging area of research in the
field of information assurance. Intruders use polymorphic mechanisms to masquerade the …

In this study, a new hybrid artificial intelligence approach is proposed to model the ultrasonic
welding of a polymeric material blend. The proposed approach is composed of an ensemble …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

With randomly generated weights between input and hidden layers, a random vector
functional link network is a universal approximator for continuous functions on compact sets …

As a powerful tool for data regression and classification, neural networks have received
considerable attention from researchers in fields such as machine learning, statistics …

Convolutional Neural Networks have achieved state-of-the-art performance on a wide range
of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling …

Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …