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Temporal Anomaly Detection in Attention-Deficit/Hyperactivity Disorder Using Recurrent Neural Networks. [PDF]

Cureus
Bouchouras G, Sofianidis G, Kotis K.
europepmc   +1 more source

Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment. [PDF]

Sensors (Basel)
Desikan J, Singh SK, Jayanthiladevi A, Bhushan S, Rishiwal V, Kumar M.   +5 more
europepmc   +1 more source

Greedy Ensemble Hyperspectral Anomaly Detection. [PDF]

J Imaging
Hossain M, Younis M, Robinson A, Wang L, Preza C.   +4 more
europepmc   +1 more source

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Anomaly detection: A survey

ACM Computing Surveys, 2009
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic ...
V. Chandola, A. Banerjee, Vipin Kumar
semanticscholar   +3 more sources

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

Proceedings of the VLDB Endowment, 2022
Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging ...
Shreshth Tuli, G. Casale, N. Jennings
semanticscholar   +1 more source

BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection

IEEE Transactions on Geoscience and Remote Sensing, 2023
Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that deviate from the surrounding background in unlabeled hyperspectral images (HSIs).
Degang Wang, Lina Zhuang, Lianru Gao, Xu Sun, Min Huang, A. Plaza   +5 more
semanticscholar   +1 more source

AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

International Conference on Learning Representations, 2023
Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset.
Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen   +4 more
semanticscholar   +1 more source

Revisiting Reverse Distillation for Anomaly Detection

Computer Vision and Pattern Recognition, 2023
Anomaly detection is an important application in large-scale industrial manufacturing. Recent methods for this task have demonstrated excellent accuracy but come with a latency trade-off.
Tran Dinh Tien, Anh Tuan Nguyen, Nguyen H. Tran, Ta Duc Huy, S. T. Duong, Chanh D. Tr, Nguyen, S. Q. Truong   +7 more
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Masked Swin Transformer Unet for Industrial Anomaly Detection

IEEE Transactions on Industrial Informatics, 2023
The intelligent detection process for industrial anomalies employs artificial intelligence methods to classify images that deviate from a normal appearance.
Jielin Jiang, Jiale Zhu, Muhammad Bilal, Yan Cui, Neeraj Kumar, Ruihan Dou, Feng Su, Xiaolong Xu   +7 more
semanticscholar   +1 more source