A contemporary and comprehensive survey on streaming tensor decomposition
K Abed-Meraim, NL Trung… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Tensor decomposition has been demonstrated to be successful in a wide range of
applications, from neuroscience and wireless communications to social networks. In an …
applications, from neuroscience and wireless communications to social networks. In an …
Machine learning-based anomaly detection using K-mean array and sequential minimal optimization
Recently, artificial intelligence (AI) techniques have been used to describe the
characteristics of information, as they help in the process of data mining (DM) to analyze …
characteristics of information, as they help in the process of data mining (DM) to analyze …
MTV: Visual analytics for detecting, investigating, and annotating anomalies in multivariate time series
Detecting anomalies in time-varying multivariate data is crucial in various industries for the
predictive maintenance of equipment. Numerous machine learning (ML) algorithms have …
predictive maintenance of equipment. Numerous machine learning (ML) algorithms have …
Revisiting the design patterns of composite visualizations
Composite visualization is a popular design strategy that represents complex datasets by
integrating multiple visualizations in a meaningful and aesthetic layout, such as …
integrating multiple visualizations in a meaningful and aesthetic layout, such as …
An irrelevant attributes resistant approach to anomaly detection in high-dimensional space using a deep hypersphere structure
J Zheng, H Qu, Z Li, L Li, X Tang - Applied Soft Computing, 2022 - Elsevier
It is a grand challenge to detect anomalies existing in subspaces from a high-dimensional
space. Most existing state-of-the-art methods implicitly or explicitly rely on distances. Since …
space. Most existing state-of-the-art methods implicitly or explicitly rely on distances. Since …
Contextual anomaly detection on time series: a case study of metro ridership analysis
The increase in the amount of data collected in the transport domain can greatly benefit
mobility studies and create high value-added mobility information for passengers, data …
mobility studies and create high value-added mobility information for passengers, data …
A survey of visual analytics in urban area
Nowadays, the population has been overgrowing due to urbanization, yielding many severe
problems in the urban area, including traffic congestion, unbalanced distribution of urban …
problems in the urban area, including traffic congestion, unbalanced distribution of urban …
Anomaly detection for high-dimensional space using deep hypersphere fused with probability approach
J Zheng, J Li, C Liu, J Wang, J Li, H Liu - Complex & Intelligent Systems, 2022 - Springer
Data distribution presents sparsity in a high-dimensional space, thus difficulty affording
sufficient information to distinguish anomalies from normal instances. Moreover, a high …
sufficient information to distinguish anomalies from normal instances. Moreover, a high …
Deep fuzzy contrast-set deviation point representation and trajectory detection
U Ahmed, JCW Lin, G Srivastava - IEEE Transactions on Fuzzy …, 2022 - ieeexplore.ieee.org
Cooperative intelligent transportation systems (ITS) are on the rise in the field of
transportation. The trajectory-based knowledge graph enables the ITS to have semantic and …
transportation. The trajectory-based knowledge graph enables the ITS to have semantic and …
Pmu tracker: A visualization platform for epicentric event propagation analysis in the power grid
The electrical power grid is a critical infrastructure, with disruptions in transmission having
severe repercussions on daily activities, across multiple sectors. To identify, prevent, and …
severe repercussions on daily activities, across multiple sectors. To identify, prevent, and …