The goal of sustainable development can be attained by the efficient management of
renewable energy resources. Wind energy is attracting attention worldwide due to its …

The recent advances usually mine market information from the chaotic data to conduct a
stock movement prediction task. However, the current stock price movement prediction …

An integrated deep learning paradigm for the analysis of document-based sentiments is
presented in this article. Generally, sentiment analysis has enormous applications in the real …

Training recurrent neural networks (RNNs) has become a go-to approach for generating and
evaluating mechanistic neural hypotheses for cognition. The ease and efficiency of training …

Time series modelling has been successfully handled by Long Short-Term Memory (LSTM)
models. Yet their performance can be severely inhibited by the occurrence of missing values …

Text classification is a fundamental task in natural language processing and is essential for
many tasks like sentiment analysis and question classification etc. As we all know, different …

It is important and challenging to infer stochastic latent semantics for natural language
applications. The difficulty in stochastic sequential learning is caused by the posterior …

Time-series forecasting is crucial in the efficient operation and decision-making processes of
various industrial systems. Accurately predicting future trends is essential for optimizing …

Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an
important input to many fire simulation models. There are two main approaches to estimating …

Biomedical event extraction is an important branch of biomedical information extraction.
Trigger extraction is the most essential sub-task in event extraction, which has been widely …