Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …

Class label noise is a critical component of data quality that directly inhibits the predictive
performance of machine learning algorithms. While many data-level and algorithm-level …

With the rise of neural network, deep learning technology is more and more widely used in
various fields. Federated learning is one of the training types in deep learning. In federated …

It is recognized the importance of knowing the descriptive properties of a dataset when
tackling a data science problem. Having information about the redundancy, complexity and …

Recently recommender systems become more and more significant in the daily life such as
event recommendation, content recommendation and commodity recommendation, and so …

Recent advances in machine learning (ML) bring more opportunities for greater
implementation of smart government construction. However, there are many challenges in …

The emergence of large amounts of hesitant fuzzy data brings more opportunities and
challenges for optimal decision-making results. The granularity of the hesitant fuzzy set has …

Cutter suction dredgers (CSDs) play a very important role in the construction of ports,
waterways and navigational channels. Currently, most of CSDs are mainly manipulated by …

Background Anomaly detection is the process of identifying observations that differ greatly
from the majority of data. Unsupervised anomaly detection aims to find outliers in data that is …

In this paper, we propose a novel lattice-based linkable ring signature scheme based on the
Borromean ring signature. In our scheme, to avoid the extra overhead caused by reject …