Neural networks : the official journal of the International Neural Network Society
Jan 6, 2021
With the rapid increase of data availability, time series classification (TSC) has arisen in a wide range of fields and drawn great attention of researchers. Recently, hundreds of TSC approaches have been developed, which can be classified into two c...
MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-di...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
Convolutional neural networks (CNNs) have shown an effective way to learn spatiotemporal representation for action recognition in videos. However, most traditional action recognition algorithms do not employ the attention mechanism to focus on essent...
IEEE transactions on neural networks and learning systems
Jan 4, 2021
Open-domain dialog generation, which is a crucial component of artificial intelligence, is an essential and challenging problem. In this article, we present a personalized dialog system, which leverages the advantages of multitask learning and reinfo...
International journal of legal medicine
Jan 3, 2021
Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom testing methods are laborious, time-consuming, and costly and usually require specif...
In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed ...
Medical & biological engineering & computing
Jan 2, 2021
Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely...
Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effecti...
Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in ...
Neural networks : the official journal of the International Neural Network Society
Dec 25, 2020
In this paper, we propose Stacked DeBERT, short for StackedDenoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel encoding sc...
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