With the latest advances in deep learning-based generative models, it has not taken long to
take advantage of their remarkable performance in the area of time series. Deep neural …

Simple Summary Transfer learning plays a major role in medical image analyses; however,
obtaining adequate training image datasets for machine learning algorithms can be …

In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

Unsupervised/self-supervised time series representation learning is a challenging problem
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …

Time series are usually rarely or sparsely labeled, which limits the performance of deep
learning models. Self-supervised representation learning can reduce the reliance of deep …

Neural networks have become a powerful tool in pattern recognition and part of their
success is due to generalization from using large datasets. However, unlike other domains …

Sensors, as an important part of structural health monitoring systems (SHMSs), will be
abnormal sometimes due to their deterioration or environment effect, which will result in data …

The application of machine learning and artificial intelligence techniques in the medical
world is growing, with a range of purposes: from the identification and prediction of possible …

Wearable Human Activity Recognition (WHAR) is an important research field of ubiquitous
and mobile computing. Deep WHAR models suffer from the overfitting problem caused by …

Falls are an important factor in significantly deteriorating quality of life of older adults,
consequently leading to both physical and psychological harm. A wearable-based fall …