Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Continual learning (CL) aims to enable information systems to learn from a continuous data
stream across time. However, it is difficult for existing deep learning architectures to learn a …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

The application of social distancing by the government has a significant impact on learning
activities in tertiary institutions. Colleges divert class meetings with online meetings in …

An internet connection has been crucial in the era of globalization to enhance human
activities in various activities of economic, culture, defense, and many others, especially in …

Online learning is a learning methodology implemented during the recent COVID-19
outbreaks. Lecturers and students need to use appropriate online platformsarising from the …

Advanced AI models hold the promise of tremendous benefits for humanity, but society
needs to proactively manage the accompanying risks. In this paper, we focus on what we …

The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

The use of data generated passively by personal electronic devices, such as smartphones,
to measure human function in health and disease has generated significant research …

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning
setting, which requires the entire training data to be made available prior to the learning …