An autoencoder is a specific type of a neural network, which is mainly designed to encode
the input into a compressed and meaningful representation and then decode it back such …

Additive manufacturing facilitates producing of complex parts due to its design freedom in a
wide range of applications. Despite considerable advancements in additive manufacturing …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …

Graph clustering is a fundamental task which discovers communities or groups in networks.
Recent studies have mostly focused on developing deep learning approaches to learn a …

Background Echocardiography is the diagnostic modality for assessing cardiac systolic and
diastolic function to diagnose and manage heart failure. However, manual interpretation of …

Mental illnesses, such as depression, are highly prevalent and have been shown to impact
an individual's physical health. Recently, artificial intelligence (AI) methods have been …

Clustering is central to many data-driven bioinformatics research and serves a powerful
computational method. In particular, clustering helps at analyzing unstructured and high …