This systematic review explores the use of machine learning (ML) in predicting diabetes,
focusing on datasets, algorithms, training methods, and evaluation metrics. It examines …

The ability to access brain information in real-time is crucial both for a better understanding
of cognitive functions and for the development of therapeutic applications based on brain …

Self-supervised contrastive learning offers a means of learning informative features from a
pool of unlabeled data. In this paper, we investigate another useful approach. We propose …

Collecting sufficient high-quality training data for deep neural networks is often expensive or
even unaffordable in medical image segmentation tasks. We thus propose to train the …

Learning from external data is an effective and efficient way of training deep networks, which
can substantially alleviate the burden on collecting training data and annotations. It is of …

The adoption of deep learning-based healthcare decision support systems such as the
detection of irregular cardiac rhythm is hindered by challenges such as lack of access to …

The effectiveness of non-parametric methods for regression comes at the price of high
computational complexity. In fact, these methods scale as O (N 3), where N is the number of …

Training data for deep learning algorithms can have many redundancies, which should be
resolved to achieve faster training speed and efficient storage usage. We proposed a …

The noisiness and low quality of IT operations management data is a major challenge in
using machine learning to assist IT operations management. Our system mitigates this …

With recent advances in artificial intelligence, there is an increased demand in the
adaptation of deep learning for decision support systems in consumer applications. Despite …