Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Over the past few years, the computer vision domain has evolved and made a revolutionary
transition from human-engineered features to automated features to address challenging …

Multimodal multi-label emotion recognition (MMER) is a vital yet challenging task in affective
computing. Despite significant progress in previous works, there are three limitations:(i) …

Capsule network (CapsNet) is a novel concept demonstrating the importance of learning
spatial hierarchical relationship between features for the effective recognition of images …

Today, cancer is one of the leading causes of death in humans in the world. Cancers affect
different parts of the human anatomy in different ways. There are significantly more deaths …

Abstract Capsule Networks (CapsNets) were proposed to mitigate the shortcomings of
Convolutional Neural Networks (CNNs) such as invariance. Even though they have …

The use of cryptographic privacy-preserving techniques in Federated Learning (FL)
inadvertently induces a security dilemma because tampered local model parameters are …

Machine reading comprehension is a crucial and challenging task in natural language
processing (NLP). Recently, knowledge graph (KG) embedding has gained massive …

Abstractive summarization (AS) aims to generate more flexible and informative descriptions
than extractive summarization. Nevertheless, it often distorts or fabricates facts in the original …

Vision Transformers (ViT) are commonly utilized in image recognition and related
applications. It delivers impressive results when it is pre-trained using massive volumes of …