Search Results - "convolutional LSTM autoencoder"
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The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder
ISSN: 1424-8220, 1424-8220Published: Switzerland MDPI AG 09.04.2024Published in Sensors (Basel, Switzerland) (09.04.2024)“…) and a convolutional LSTM autoencoder to achieve the feature extraction, health index analysis, and RUL prediction for rotating machinery…”
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A deep learning approach for error detection and quantification in extrusion-based bioprinting
ISSN: 2214-7853, 2214-7853Published: Elsevier Ltd 2022Published in Materials today : proceedings (2022)“…Quality control in extrusion-based bioprinting (EBB) represents a crucial step to: i) reduce the trial-and-error process and associated material consumption,…”
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Video Anomaly Detection using Variational Convolutional LSTM Autoencoder
Published: IEEE 26.05.2023Published in 2023 International Conference on Communication, Circuits, and Systems (IC3S) (26.05.2023)“…Unintentional, inadvertent, unanticipated, or unplanned events are referred to as anomalies or abnormal events. Anomaly detection in surveiiance video has been…”
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Conference Proceeding -
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StepEncog: A Convolutional LSTM Autoencoder for Near-Perfect fMRI Encoding
ISSN: 2161-4407Published: IEEE 01.07.2019Published in Proceedings of ... International Joint Conference on Neural Networks (01.07.2019)“… In this paper, we present StepEncog, a convolutional LSTM autoencoder model trained on fMRI voxels…”
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Conference Proceeding -
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A Robust Deep Learning Model to Predict Epileptic Seizures based on Electroencephalographic (EEG) Signals
Published: IEEE 16.12.2024Published in 2024 9th International Conference on Communication and Electronics Systems (ICCES) (16.12.2024)“… This paper presents a novel approach Deep Neural Optimum Transformation (DNOT) for epileptic seizure prediction based on EEG signals using a hybrid deep learning model, Convolutional LSTM AutoEncoder (CLSTM-AE…”
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Conference Proceeding -
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DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection
ISSN: 0925-2312, 1872-8286Published: Elsevier B.V 28.04.2024Published in Neurocomputing (Amsterdam) (28.04.2024)“… For capturing temporal patterns, the framework employs a Convolutional LSTM Autoencoder (ConvLSTM-AE) module, enabling effective learning and representation of temporal dependencies in video data…”
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A deep learning approach for anomaly detection in large-scale Hajj crowds
ISSN: 0178-2789, 1432-2315Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024Published in The Visual computer (01.08.2024)“…Hajj is an annual Islamic event attended by millions of pilgrims every year from around the globe. It is considered to be the biggest religious event that…”
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Enhanced Short-Term GHI Prediction using Cloud-Aware Deep Autoencoder Network
Published: IEEE 04.08.2025Published in 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA) (04.08.2025)“… Visible-band satellite imagery is used to generate spatiotemporal cloud mask cubes, which are encoded via a Convolutional LSTM Autoencoder to learn cloud evolution patterns…”
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Conference Proceeding -
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Visual anomaly detection in video by variational autoencoder
ISSN: 2331-8422Published: Ithaca Cornell University Library, arXiv.org 08.03.2022Published in arXiv.org (08.03.2022)“…Video anomalies detection is the intersection of anomaly detection and visual intelligence. It has commercial applications in surveillance, security,…”
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A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data
ISSN: 2331-8422Published: Ithaca Cornell University Library, arXiv.org 25.02.2023Published in arXiv.org (25.02.2023)“…Efficient querying and retrieval of healthcare data is posing a critical challenge today with numerous connected devices continuously generating petabytes of…”
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Deep Learning Interference Cancellation in Wireless Networks
ISSN: 2331-8422Published: Ithaca Cornell University Library, arXiv.org 11.09.2020Published in arXiv.org (11.09.2020)“…With the crowding of the electromagnetic spectrum and the shrinking cell size in wireless networks, crosstalk between base stations and users is a major…”
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Unsupervised Feature Learning for Audio Analysis
ISSN: 2331-8422Published: Ithaca Cornell University Library, arXiv.org 11.12.2017Published in arXiv.org (11.12.2017)“… It incorporates the two following novel contributions: First, an audio frame predictor based on a Convolutional LSTM autoencoder is demonstrated, which is used for unsupervised feature extraction…”
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