Neural network ensembles are gaining popularity by harnessing the complementary wisdom
of multiple base models. Ensemble teams with high diversity promote high failure …

The extended utilization of digitized Whole Slide Images is transforming the workflow of
traditional clinical histopathology to the digital era. The ongoing transformation has …

While automated driving is often advertised with better-than-human driving performance, this
chapter reviews that it is nearly impossible to provide direct statistical evidence on the …

Rumors regarding epidemic diseases such as COVID 19, medicines and treatments,
diagnostic methods and public emergencies can have harmful impacts on health and …

Deep neural network ensembles hold the potential of improving generalization performance
for complex learning tasks. This paper presents formal analysis and empirical evaluation to …

Ensemble methods combine multiple individual models for prediction, which have
demonstrated their effectiveness in accurate uncertainty quantification (UQ) and strong …

Deep neural network ensembles have become attractive learning techniques with better
generalizability over individual models. Some mission critical applications may require a …

Deep neural network ensembles combine the wisdom of multiple deep neural networks to
improve the generalizability and robustness over individual networks. It has gained …

Convolutional Neural Networks (CNNs) have demonstrated remarkable success with great
accuracy in classification problems. However, the lack of interpretability of the predictions …

The objective of the present study is to improve the genetic algorithm (GA) supremacy in
selecting the most suitable and relevant features within a highly dimensional dataset. This …