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Self-Supervised Learning for Time-Series Anomaly Detection in Industrial Internet of Things

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-59050-9_12openalex-snapshot
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Apache Kafka
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inferredcites →https://doi.org/10.1016/j.future.2016.12.040openalex-snapshot
CLAPP: A self constructing feature clustering approach for anomaly detection
inferredcites →https://doi.org/10.1016/j.iot.2019.100059openalex-snapshot
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
inferredcites →https://doi.org/10.1088/1741-2560/8/3/036015openalex-snapshot
Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement
inferredcites →https://doi.org/10.1109/28.491471openalex-snapshot
Statistical process control of an industrial process in real time
inferredcites →https://doi.org/10.1109/access.2019.2949697openalex-snapshot
Self-Supervised Domain Adaptation for Computer Vision Tasks
inferredcites →https://doi.org/10.1109/bigdata50022.2020.9378139openalex-snapshot
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/cscloud.2017.15openalex-snapshot
Machine Learning for Anomaly Detection and Categorization in Multi-Cloud Environments
inferredcites →https://doi.org/10.1109/cvpr.2019.00057openalex-snapshot
Latent Space Autoregression for Novelty Detection
inferredcites →https://doi.org/10.1109/cvpr.2019.00301openalex-snapshot
OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations
inferredcites →https://doi.org/10.1109/icassp40776.2020.9053569openalex-snapshot
Multi-Task Self-Supervised Learning for Robust Speech Recognition
inferredcites →https://doi.org/10.1109/icmla.2019.00211openalex-snapshot
Anomaly Detection using Supervised Learning and Multiple Statistical Methods
inferredcites →https://doi.org/10.1109/ictc52510.2021.9621047openalex-snapshot
Anomaly Detection in Semiconductor Cleanroom Using Isolation Forest
inferredcites →https://doi.org/10.1109/jsen.2020.3040696openalex-snapshot
Semi-Supervised Bearing Fault Diagnosis and Classification Using Variational Autoencoder-Based Deep Generative Models
inferredcites →https://doi.org/10.1109/rtcsa.2018.00035openalex-snapshot
Hierarchical Attention-Based Anomaly Detection Model for Embedded Operating Systems
inferredcites →https://doi.org/10.1109/tii.2019.2952917openalex-snapshot
LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT
inferredcites →https://doi.org/10.1109/tnnls.2019.2935975openalex-snapshot
Unsupervised Anomaly Detection With LSTM Neural Networks
inferredcites →https://doi.org/10.1145/1390156.1390294openalex-snapshot
Extracting and composing robust features with denoising autoencoders
inferredcites →https://doi.org/10.1186/s12911-016-0318-zopenalex-snapshot
Nearest neighbor imputation algorithms: a critical evaluation
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wav2vec: Unsupervised Pre-Training for Speech Recognition
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Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals
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Unsupervised Representation Learning by Predicting Image Rotations
inferredcites →https://doi.org/10.48550/arxiv.1901.10738openalex-snapshot
Unsupervised Scalable Representation Learning for Multivariate Time Series
inferredcites →https://doi.org/10.48550/arxiv.2103.03230openalex-snapshot
Barlow Twins: Self-Supervised Learning via Redundancy Reduction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-50514-0_4openalex-snapshot
Machine Learning Techniques for Industrial Internet of Things
inferred← citeshttps://doi.org/10.1007/978-3-031-87837-4_19openalex-snapshot
Machine Learning Techniques for Industrial Internet of Things: A Survey
inferred← citeshttps://doi.org/10.1007/978-3-032-05548-4_28openalex-snapshot
Self-supervised Learning for Anomaly Detection in Large-Scale Cloud Infrastructure
inferred← citeshttps://doi.org/10.1007/978-3-032-07343-3_24openalex-snapshot
Pretraining Techniques for Steel Surface Roughness Prediction with Long Thin Spatial Industrial Data
inferred← citeshttps://doi.org/10.1007/978-3-032-10209-6_34openalex-snapshot
Abnormal Detection in Industrial Ovens Using XGBoost and IoT Data
inferred← citeshttps://doi.org/10.1007/978-3-032-20533-9_1openalex-snapshot
Performance Comparison Between Multilayer Perceptrons and Kolmogorov-Arnold Networks for Fault Detection in Automotive Engines
inferred← citeshttps://doi.org/10.1007/978-3-032-20735-7_34openalex-snapshot
Enhanced Dyslexia Detection Through Edge-Integrated Deep Learning and Augmentation Techniques
inferred← citeshttps://doi.org/10.1007/978-981-96-0840-9_28openalex-snapshot
Adaptive Plug-and-Play Framework for Time Series Anomaly Detection with Temporal Drift
inferred← citeshttps://doi.org/10.1007/s00170-026-18577-6openalex-snapshot
Decoding the future of edge computing in smart manufacturing: an industry 5.0 perspective
inferred← citeshttps://doi.org/10.1007/s10586-025-05234-yopenalex-snapshot
Fog-empowered anomaly detection in IoT networks using one-class asymmetric stacked autoencoder
inferred← citeshttps://doi.org/10.1007/s10845-025-02745-8openalex-snapshot
Dta-qc: an AI-driven framework for adaptive quality control and intelligent test optimization in 5 G manufacturing
inferred← citeshttps://doi.org/10.1007/s41870-025-02525-wopenalex-snapshot
Self-supervised quantum relational reasoning (S2QR2) of time series data for mental health monitoring
inferred← citeshttps://doi.org/10.1007/s42417-026-02455-2openalex-snapshot
Self-Supervised Vibration Analytics for Predictive Maintenance of Multistage Compressors
inferred← citeshttps://doi.org/10.1016/j.aej.2024.07.013openalex-snapshot
DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection
inferred← citeshttps://doi.org/10.1016/j.engappai.2026.114629openalex-snapshot
Adaptive salience autoencoder for unsupervised anomaly detection in computer network traffic
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121921openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.rineng.2025.104277openalex-snapshot
Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment
inferred← citeshttps://doi.org/10.1051/itmconf/20268203019openalex-snapshot
Geocrypt: Location-Based Secure File Encryption and Access Control
inferred← citeshttps://doi.org/10.1109/access.2024.3522325openalex-snapshot
Self-Supervised Anomaly Detection Using Outliers for Multivariate Time Series
inferred← citeshttps://doi.org/10.1109/access.2025.3551750openalex-snapshot
AI-Powered IoT: A Survey on Integrating Artificial Intelligence With IoT for Enhanced Security, Efficiency, and Smart Applications
inferred← citeshttps://doi.org/10.1109/aectsd65988.2025.11410990openalex-snapshot
Time Series Machine Learning Model for Analyzing Growth of Insider Threats Across Global Sectors
inferred← citeshttps://doi.org/10.1109/cbase67452.2025.11335517openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icmlt65785.2025.11193363openalex-snapshot
Temporal-Spatial Attention Network (TSAN) for DoS Attack Detection in Network Traffic
inferred← citeshttps://doi.org/10.1109/icrtcst68392.2026.11545415openalex-snapshot
IoT Data Analytics with Deep Neural Networks: Trends and Future Directions
inferred← citeshttps://doi.org/10.1109/jiot.2023.3318298openalex-snapshot
Lightweight LAE for Anomaly Detection With Sound-Based Architecture in Smart Poultry Farm
inferred← citeshttps://doi.org/10.1109/nmitcon65824.2025.11187706openalex-snapshot
Multimodal Self-Supervised Anomaly Detection in Industrial Systems Using TCN and Swin Transformer
inferred← citeshttps://doi.org/10.1145/3770575openalex-snapshot
A Review on Self-Supervised Learning in Time Series Anomaly Detection: Recent Advances and Open Challenges
inferred← citeshttps://doi.org/10.2478/ama-2023-0051openalex-snapshot
Method of Machining Centre Sliding System Fault Detection using Torque Signals and Autoencoder
inferred← citeshttps://doi.org/10.33693/2313-223x-2026-13-2-162-175openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/app151810080openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/computers15040231openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/electronics13101799openalex-snapshot
Data-Driven Prediction Model for Analysis of Sensor Data
inferred← citeshttps://doi.org/10.3390/electronics14010104openalex-snapshot
Efficient Real-Time Anomaly Detection in IoT Networks Using One-Class Autoencoder and Deep Neural Network
inferred← citeshttps://doi.org/10.3390/pr14152452openalex-snapshot
Self-Supervised CNN–Transformer Anomaly Detection for Bearing Health Monitoring
inferred← citeshttps://doi.org/10.3390/s23198286openalex-snapshot
Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning
inferred← citeshttps://doi.org/10.3390/s25092945openalex-snapshot
End-to-End Architecture for Real-Time IoT Analytics and Predictive Maintenance Using Stream Processing and ML Pipelines
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The Management of IoT-Based Organizational and Industrial Digitalization Using Machine Learning Methods
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