Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …
systems continues to generate massive amounts of data. Many approaches have been …
Semi-supervised and un-supervised clustering: A review and experimental evaluation
K Taha - Information Systems, 2023 - Elsevier
Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …
efficient mechanism for overcoming these challenges is to cluster the data into a compact …
Variational LSTM enhanced anomaly detection for industrial big data
With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly
discussed topic in digital and intelligent industry field. The security problem existing in the …
discussed topic in digital and intelligent industry field. The security problem existing in the …
Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning
Y Li, Y Song, L Jia, S Gao, Q Li… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Nowadays, the industrial Internet of Things (IIoT) has been successfully utilized in smart
manufacturing. The massive amount of data in IIoT promote the development of deep …
manufacturing. The massive amount of data in IIoT promote the development of deep …
Convergence of blockchain and edge computing for secure and scalable IIoT critical infrastructures in industry 4.0
Critical infrastructure systems are vital to underpin the functioning of a society and economy.
Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial …
Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial …
Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach
Y Song, R **n, P Chen, R Zhang, J Chen… - Future Generation …, 2023 - Elsevier
Cloud computing provides scalable and elastic resources to customers as a low-cost, on-
demand utility service. Multivariate time series anomaly detection is crucial to promise the …
demand utility service. Multivariate time series anomaly detection is crucial to promise the …
Graph neural networks for anomaly detection in industrial Internet of Things
The Industrial Internet of Things (IIoT) plays an important role in digital transformation of
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Time series anomaly detection has been a perennially important topic in data science, with
papers dating back to the 1950s. However, in recent years there has been an explosion of …
papers dating back to the 1950s. However, in recent years there has been an explosion of …
Cloud-edge orchestration for the Internet of Things: Architecture and AI-powered data processing
Y Wu - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
The Internet of Things (IoT) has been deeply penetrated into a wide range of important and
critical sectors, including smart city, water, transportation, manufacturing, and smart factory …
critical sectors, including smart city, water, transportation, manufacturing, and smart factory …
Privacy-aware cross-platform service recommendation based on enhanced locality-sensitive hashing
Recommender systems are a promising way for users to quickly find the valuable
information that they are interested in from massive data. Concretely, by capturing the user's …
information that they are interested in from massive data. Concretely, by capturing the user's …