We present a comprehensive, detailed review of time-series data analysis, with emphasis on
deep time-series clustering (DTSC), and a case study in the context of movement behavior …

Recently, deep clustering utilizing Graph Neural Networks has shown good performance in
the graph clustering. However, the structure information of graph was underused in existing …

The minimum spanning tree clustering algorithm is known to be capable of detecting
clusters with irregular boundaries. The paper presents a novel hierarchical clustering …

The achievement of convolutional neural networks (CNNs) in a variety of applications is
accompanied by a dramatic increase in computational costs and memory requirements. In …

To address the inflexibility of the convolutional autoencoder (CAE) in adjusting the network
structure and the difficulty of accurately delineating complex class boundaries in power load …

Deep learning models have revolutionized various domains, with Multi-Layer Perceptrons
(MLPs) being a cornerstone for tasks like data regression and image classification …

Numerous graph-based clustering algorithms relying on k-nearest neighbor (KNN) have
been proposed. However, the performance of these algorithms tends to be affected by many …

Underwater acoustic signal separation is a key technique for underwater communications.
The existing methods are mostly model-based, and cannot accurately characterize the …

In this article, we introduce a web-based malware detection system that leverages a deep-
learning approach. Our primary objective is the development of a robust deep-learning …

Representation learning based on autoencoders has received great concern for its potential
ability to capture valuable latent information. Conventional autoencoders pursue minimal …