Event recognition and anomaly detection using machine learning by Peng, Xinggan

“…Abnormal event recognition and anomaly detection aim to discover incidents, patterns or objects that do not conform to expected behaviour. …”
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From soft sensing to anomaly detection in combined sewer systems by Palmitessa, Rocco

“…By investigating and testing updated 1D hydrodynamic models and LSTM neural networks, this thesis demonstrates their concrete potential for soft sensing and anomaly detection applications and promotes their integration in the monitoring and control workflows of modern utility companies.…”
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Data-driven phase extraction for anomaly detection of repetitive human movements by Jatesiktat, Prayook

“…This standardization allows the principal component analysis reconstruction to be applied for anomaly detection. The whole modelling workflow not only works on a gold-standard marker-based motion capture system, but it is also compatible with our motion capture system which integrates a Kinect sensor with wrist-worn inertial measurement units (IMU). …”
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Probabilistic digital twin of water treatment facilities by Wei, Yuying

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Graph mining in Fintech-driven payment industry for risk management by Chen, Zhe

“…In specific, the three applications include seller community detection, risky seller detection, and cyber network anomaly detection. Seller community detection aims to identify the community-wise relationship between sellers on the payment network. …”
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Enabling advanced analytics for industry applications using machine learning and graph mining by Zhao, Lin

“…In this study, an end-to-end anomaly detection solution is proposed that uses only normal wafer maps for training to identify abnormal wafer maps. …”
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Sparse representation and its applications to airborne platforms by Srinivasan, Abirami

“…Sparse-representation based anomaly detection uses the sparsity of the signals to train a dictionary. …”
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Edge processing in IoT using approximate and in-memory computing by Bose, Sumon Kumar

“…In our work, we propose an anomaly detection scheme that can be trained only using healthy machine data. …”
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Advanced data-analytics for power system security enhancement by Chen, Zhebin

“…The techniques employed in this thesis involve anomaly detection (to detect cyber-attacks, e.g., Long Short-Term Memory, Local Outlier Factor, etc.), interpretation analysis (to quantify the contributions of input features against the machine learning models, e.g., SHapley Additive exPlanations and Shapley Additive Global importancE), adversarial attacks and mitigation (to measure the model vulnerability), and ensemble learning (to enhance the reliability of the predictions). …”
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Privacy-preserving disease detection techniques in e-healthcare systems by Wang, Guoming

“…To reduce the query latency, we propose a novel sensor anomaly detection technique (SADS) for detecting high-risk disease. …”
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Security investigation of autonomous driving systems by Han, Xingshuo

“…Based on that, we introduce two novel defense methods: (1) ADS-Lead, an effective collaborative anomaly detection method to safeguard ADS lane-following mechanisms. (2) VisionGuard, a unified defense framework capable of detecting and mitigating various physical adversarial attacks on ADS perception. …”
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Real-time human balance and gait evaluation algorithms of a robotic balance assistant using optimization and learning-based methods by Foo, Ming Jeat

“…The second method is based on anomaly detection with neural networks to learn the patterns of ADLs for it to predict future data. …”
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Using deep learning for quality control in cyber-manufacturing by Dai, Wenting

“…Based on these two principles, unsupervised learning (including clustering and anomaly detection), and active learning are focused on in this thesis. …”
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Self-supervised learning for early detection of neurodegenerative diseases with small data by Jiang, Hongchao

“…Therefore, we can formulate the problem as an anomaly detection task and model how healthy a data sample is. …”
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Neuromorphic machine learning for audio processing : from bio-inspiration to biomedical applications by Acharya, Jyotibdha

“…In the third work, different feature extraction and classification strategies are implemented for audio based respiratory anomaly detection and then low precision representations of the proposed networks are explored for memory efficient wearable implementation. …”
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Deep learning for texture recognition: from streamlined architecture to multimodal extensions by Mao, Shangbo

“…Beyond its streamlined architecture, our method sets new performance standards on well-established texture classification datasets including FMD, DTD, and 4D Light, as well as an industrial dataset for metal surface anomaly detection. Additionally, our approach maintains competitive results on the MIT-Indoor scene recognition dataset. …”
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