The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Over the history of Artificial Neural Networks (ANNs), only a minority of algorithms integrate
structural changes of the network architecture into the learning process. Modern …

Abstract Concept drifts and class imbalance are two primary challenges in supervised data
stream classification, whereas their co-occurrence presents a more complicated learning …

Image fusion is indispensable in a comprehensive medical imaging pipeline. By embracing
deep learning technology, medical image fusion has achieved tremendous progress over …

Class imbalance and concept drifts could deteriorate the performance of classifiers in data
stream learning as their co-occurrence presents a complicated learning scenario. This …

Deep learning models have been widely used during the last decade due to their
outstanding learning and abstraction capacities. However, one of the main challenges any …

Video motion magnification is the task of making subtle minute motions visible. Many times
subtle motion occurs while being invisible to the naked eye, eg, slight deformations in …

Modeling the architecture search process on a supernet and applying a differentiable
method to find the importance of architecture are among the leading tools for differentiable …

The co-occurrence of evolving concepts and imbalanced data deteriorates the learning
performance of classifiers in data streams. Recent studies do not account for data difficulty …

Streaming data analysis faces two primary challenges: concept drifts and class imbalance.
The co-occurrence of virtual drifts and class imbalance is a common real-world scenario …