With advanced digitalisation, we can observe a massive increase of user-generated content
on the web that provides opinions of people on different subjects. Sentiment analysis is the …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

In this study, we propose the use of Variational Autoencoders (VAEs) and Generative
Adversarial Networks (GANs) to generate synthetic data for crop recommendation (CR). CR …

Sentiment classification is an interesting and crucial research topic in the field of natural
language processing (NLP). Data-driven methods, including machine learning and deep …

We propose a multi-task ensemble framework that jointly learns multiple related problems.
The ensemble model aims to leverage the learned representations of three deep learning …

Aspect-level sentiment classification aims to predict the sentiment of a text in different
aspects and it is a fine-grained sentiment analysis task. Recent work exploits an Attention …

Hazardous liquid pipeline (HLP) leaks not only result in energy waste and environmental
pollution, but also pose a threat to people's lives and property. The estimation of leakage …

Recurrent neural networks (RNNs) have achieved state-of-the-art performances on various
applications. However, RNNs are prone to be memory-bandwidth limited in practical …

In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an
aberration from normal behavior, or an unexpected observation requiring immediate …

In the realm of the five-category classification endeavor, there has been limited exploration
of applied techniques for classifying Arabic text. These methods have primarily leaned on …