A comprehensive survey of anomaly detection techniques for high dimensional big data
Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …
that has various applications in the real world. However, many existing anomaly detection …
A review of local outlier factor algorithms for outlier detection in big data streams
Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …
are different from the normal form of a dataset. It has drawn considerable interest in the field …
A new deep transfer learning based on sparse auto-encoder for fault diagnosis
Fault diagnosis plays an important role in modern industry. With the development of smart
manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods …
manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods …
Anomaly detection based on convolutional recurrent autoencoder for IoT time series
C Yin, S Zhang, J Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the
technology of wireless and mobile communication. The data of target regions are collected …
technology of wireless and mobile communication. The data of target regions are collected …
Kappa updated ensemble for drifting data stream mining
Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …
of contemporary machine learning. Algorithms designed for such scenarios must take into …
A review on deep learning applications in prognostics and health management
Deep learning has attracted intense interest in Prognostics and Health Management (PHM),
because of its enormous representing power, automated feature learning capability and best …
because of its enormous representing power, automated feature learning capability and best …
A survey of outlier detection in high dimensional data streams
The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …
streams in a wide range of fields, such as genomics, signal processing, and finance. The …
A new explainable deep learning framework for cyber threat discovery in industrial IoT networks
Industrial Internet of Things (IIoT) and Industry 4.0 empower interrelation among
manufacturing processes, industrial machines, and utility services. The time-critical data …
manufacturing processes, industrial machines, and utility services. The time-critical data …
Adaptive kernel density-based anomaly detection for nonlinear systems
This paper presents an unsupervised, density-based approach to anomaly detection. The
purpose is to define a smooth yet effective measure of outlierness that can be used to detect …
purpose is to define a smooth yet effective measure of outlierness that can be used to detect …
Industry 4.0 towards Forestry 4.0: Fire detection use case
Forestry 4.0 is inspired by the Industry 4.0 concept, which plays a vital role in the next
industrial generation revolution. It is ushering in a new era for efficient and sustainable forest …
industrial generation revolution. It is ushering in a new era for efficient and sustainable forest …