Network traffic classification has been widely studied to fundamentally advance network
measurement and management. Machine Learning is one of the effective approaches for …

Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing
multidimensional data. However, when data is a live streaming feed, conventional DR …

As the widely deployment of different sensors and Internet of Things, a large volume of
multivariate time series has been collected. However, there are many missing values in the …

Nowadays, the issue of MV imputation has become one of the research hotspots in the field
of data quality, since the missing values (MVs) are prevalent in real-world datasets and bring …

Quick Access Recorder (QAR), an important device for storing data from various flight
parameters, contains a large amount of valuable data and comprehensively records the real …

Standard multivariate techniques like Principal Component Analysis (PCA) are based on the
eigendecomposition of a matrix and therefore require complete data sets. Recent …

Abstract “A rotten apple spoils the whole bunch” deciphers the research problem domain.
Taking a broader perspective, the existence of anomaly in a graphical community would …

In today's environment to find the missing value in the data set has become the biggest
challenge for the industry people, scientists, academicians and for the researchers. With the …

Abstract Emerging Artificial Intelligence (AI) techniques, including both Machine Learning
algorithms and Deep Learning models, have become viable solutions to support network …

Data sets with missing values bring great challenges to k-means clustering (KMC). At
present, most studies focus on KMC data with low missing ratio while few studies on KMC …