Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …

This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …

Machine learning (ML) models have been widely used in the modeling, design and
prediction in energy systems. During the past two decades, there has been a dramatic …

Though deep neural networks have achieved significant progress on various tasks, often
enhanced by model ensemble, existing high-performance models can be vulnerable to …

Over the last decade Deep learning-based models surpasses classical machine learning
models in a variety of text classification tasks. The primary challenge with text classification …

Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors
into benignancy or malignancy. However, conventional CAD software has some problems …

In numerous industrial applications where safety, efficiency, and reliability are among
primary concerns, condition-based maintenance (CBM) is often the most effective and …

Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …

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