An Information System Supporting Insurance Use Cases by Automated Anomaly Detection by Thoralf Reis, Alexander Kreibich, Sebastian Bruchhaus, Thomas Krause, Florian Freund, Marco X. Bornschlegl, Matthias L. Hemmje

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Decompose Auto-Transformer Time Series Anomaly Detection for Network Management by Bo Wu, Chao Fang, Zhenjie Yao, Yanhui Tu, Yixin Chen

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Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data by Ana Minic, Luka Jovanovic, Nebojsa Bacanin, Catalin Stoean, Miodrag Zivkovic, Petar Spalevic, Aleksandar Petrovic, Milos Dobrojevic, Ruxandra Stoean

“…This work explores the potential of recurrent neural networks for anomaly detection in ECG readings. Furthermore, to attain the best possible performance for these networks, training parameters, and network architectures are optimized using a modified version of the well-established particle swarm optimization algorithm. …”
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Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection by Zhiyu Ren, Xiaojie Li, Jing Peng, Ken Chen, Qushan Tan, Xi Wu, Canghong Shi

“…Abstract Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. …”
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Hybrid Machine Learning–Statistical Method for Anomaly Detection in Flight Data by Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri

Subjects: “…unsupervised anomaly detection…”
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Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns by Marek Florkowski

“…Anomaly detection can also handle transients and disturbances that appear in the PD image as an indication of an abnormal state. …”
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Unveiling the IoT's dark corners: anomaly detection enhanced by ensemble modelling by Jisha Jose, J. E. Judith

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Human Body Shapes Anomaly Detection and Classification Using Persistent Homology by Steve de Rose, Philippe Meyer, Frédéric Bertrand

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Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey by Huu-Thanh Duong, Viet-Tuan Le, Vinh Truong Hoang

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Local Correlation Integral Approach for Anomaly Detection Using Functional Data by Jorge R. Sosa Donoso, Miguel Flores, Salvador Naya, Javier Tarrío-Saavedra

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Alleviating Class-Imbalance Data of Semiconductor Equipment Anomaly Detection Study by Da Hoon Seol, Jeong Eun Choi, Chan Young Kim, Sang Jeen Hong

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Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences by Jorge Meira, João Carneiro, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Paulo Novais, Goreti Marreiros

“…We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. …”
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Anomaly detection in a fleet of industrial assets with hierarchical statistical modeling by Maharshi Dhada, Mark Girolami, Ajith Kumar Parlikad

Subjects: “…Anomaly detection…”
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TEC Anomalies Detection for Qinghai and Yunnan Earthquakes on 21 May 2021 by Yingbo Yue, Hannu Koivula, Mirjam Bilker-Koivula, Yuwei Chen, Fuchun Chen, Guilin Chen

“…The long short-term memory model is a kind of method to predict time series and has been used for the prediction of total electron content, and the relative power spectrum method is one of the pre-seismic infrared anomaly detection algorithms in the frequency domain. In this paper, a new method combining these two algorithms is used to extract abnormal signals; thus scientists can more easily detect anomalies of total electron content similar to those before the Qinghai and Yunnan earthquakes happened on 21 May 2021. …”
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Progressive Temporal-Spatial-Semantic Analysis of Driving Anomaly Detection and Recounting by Rixing Zhu, Jianwu Fang, Hongke Xu, Jianru Xue

“…This paper proposes a progressive unsupervised driving anomaly detection and recounting (D&R) framework. The highlights are three-fold: (1) We formulate driving anomaly D&R as a temporal-spatial-semantic (TSS) model, which achieves a coarse-to-fine focusing and generates convincing driving anomaly D&R. (2) This work contributes an unsupervised D&R without any training data while performing an effective performance. (3) We novelly introduce the traffic saliency, isolation forest, visual semantic causal relations of driving scene to effectively construct the TSS model. …”
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Anomaly Detection in Time Series Data and its Application to Semiconductor Manufacturing by Rakhoon Hwang, Seungtae Park, Youngwook Bin, Hyung Ju Hwang

Subjects: “…Anomaly detection…”
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Anomaly Detection in 6G Networks Using Machine Learning Methods by Mamoon M. Saeed, Rashid A. Saeed, Maha Abdelhaq, Raed Alsaqour, Mohammad Kamrul Hasan, Rania A. Mokhtar

Subjects: “…anomaly detection…”
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Change Point Enhanced Anomaly Detection for IoT Time Series Data by Elena-Simona Apostol, Ciprian-Octavian Truică, Florin Pop, Christian Esposito

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Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review by Ali Nawaz, Shehroz S. Khan, Amir Ahmad

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Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach by Leo Tišljarić, Sofia Fernandes, Tonči Carić, João Gama

Subjects: “…anomaly detection…”
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