Výsledky vyhledávání - "convolutional LSTM autoencoder"

Video Anomaly Detection using Variational Convolutional LSTM Autoencoder

Autoři: Sandhya Rani Sahoo Jaideep Kokkiligadda Ratnakar Dash

Zdroj: 2023 International Conference on Communication, Circuits, and Systems (IC3S). :1-5

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

Autoři: Yangde Gao Zahoor Ahmad Jong-Myon Kim

Zdroj: Sensors (Basel)
Sensors, Vol 24, Iss 8, p 2382 (2024)

Témata: health index analysis, convolutional LSTM autoencoder, Chemical technology, Communication, remaining useful life, 0202 electrical engineering, electronic engineering, information engineering, TP1-1185, 02 engineering and technology, 0210 nano-technology, 3. Good health

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38675999
https://doaj.org/article/e9c4faea42fb40c3a039f68109ed00b5

Bidirectional Convolutional LSTM Autoencoder for Risk Detection

Zdroj: International Journal of Advanced Trends in Computer Science and Engineering. 9:8585-8589

StepEncog: A Convolutional LSTM Autoencoder for Near-Perfect fMRI Encoding

Autoři: Raju S. Bapi Manish Gupta Vijay Rowtula a další

Zdroj: 2019 International Joint Conference on Neural Networks (IJCNN). :1-8

Témata: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Fall Detection from Thermal Camera Using Convolutional LSTM Autoencoder

Autoři: Jacob Nogas Shehroz S. Khan Alex Mihailidis

Zdroj: EasyChair Preprints.

Přístupová URL adresa: https://easychair.org/publications/preprint_open/S48v
https://easychair.org/publications/preprint/S48v
https://easychair.org/publications/preprint_open/S48v

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.

Autoři: Gao, Yangde Ahmad, Zahoor Kim, Jong-Myon

Zdroj: Sensors (14248220); Apr2024, Vol. 24 Issue 8, p2382, 13p

Témata: REMAINING useful life, MONITORING of machinery, ROTATING machinery, SIGNAL denoising, FEATURE extraction

A deep learning approach for error detection and quantification in extrusion-based bioprinting

Autoři: Bonatti A. F. Vozzi G. Kai Chua C. a další

Zdroj: Materials Today : Proceedings

Témata: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Extrusion-based bioprinting, Quality control, Convolutional LSTM autoencoder, Deep learning

A deep learning approach for anomaly detection in large-scale Hajj crowds.

Autoři: Aldayri, Amnah Albattah, Waleed

Zdroj: Visual Computer; Aug2024, Vol. 40 Issue 8, p5589-5603, 15p

Témata: PILGRIMAGE to Mecca, DEEP learning, COMPUTER vision, CROWDS, COLLECTIVE behavior, COMPUTER engineering

A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data

Autoři: Mahfuz, Sazia Zulkernine, Farhana

Témata: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2302.12972

Convolutional LSTM Neural Network Autoencoder Based Fault Detection in Manufacturing Predictive Maintenance

Autoři: Young Jun Park

Zdroj: Journal of Machine and Computing. :914-923

Visual anomaly detection in video by variational autoencoder

Autoři: Waseem, Faraz Martinez, Rafael Perez Wu, Chris

Témata: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2203.03872

EEG Motor imagery classification based on a ConvLSTM Autoencoder framework augmented by attention BiLSTM.

Autoři: Mirzaei, Sayeh Ghasemi, Parisa Bakhtyari, Mohammadreza

Zdroj: Multimedia Tools & Applications; Jun2025, Vol. 84 Issue 19, p20545-20566, 22p

Témata: LONG short-term memory, COGNITIVE psychology, MOTOR imagery (Cognition), MENTAL imagery, AUTOENCODERS

Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance.

Autoři: Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem a další

Zdroj: Discover Internet of Things; 3/24/2025, Vol. 5 Issue 1, p1-25, 25p

Témata: VIDEO surveillance, ANOMALY detection (Computer security), PATTERN perception, IMAGE segmentation

A Machine Learning Model for Crowd Density Classification in Hajj Video Frames.

Autoři: Shah, Afnan A.

Zdroj: International Journal of Advanced Computer Science & Applications; 2024, Vol. 15 Issue 12, p292-299, 8p

Detection and localization of anomalous objects in video sequences using vision transformers and U-Net model.

Autoři: Berroukham, Abdelhafid Housni, Khalid Lahraichi, Mohammed

Zdroj: Signal, Image & Video Processing; Sep2024, Vol. 18 Issue 8/9, p6379-6390, 12p

Irregular Event Classification in Surveillance Videos Using an Auto-Encoder Deep Learning Framework.

Autoři: Berwal, Krishan Rathore, Shubham Dahiya, Sanjay a další

Zdroj: IETE Journal of Research; Sep2024, Vol. 70 Issue 9, p7432-7440, 9p

Témata: VIDEO surveillance, CLASSIFICATION

Unsupervised Feature Learning for Audio Analysis

Autoři: Meyer, Matthias Beutel, Jan Thiele, Lothar

Témata: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/1712.03835

DAST-Net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection.

Autoři: Kommanduri, Rangachary Ghorai, Mrinmoy

Zdroj: Neurocomputing. Apr2024, Vol. 579, pN.PAG-N.PAG. 1p.

Témata: *INTRUSION detection systems (Computer security), *ANOMALY detection (Computer security), *FEATURE extraction, *EYE tracking

AEMNet: Unsupervised Video Anomaly Detection Method Based on Attention-Enhanced Memory Networks.

Autoři: Zhang, Linliang Yan, Lianshan Peng, Shouxin a další

Zdroj: International Journal of Pattern Recognition & Artificial Intelligence; Jun2024, Vol. 38 Issue 8, p1-31, 31p

Témata: INTRUSION detection systems (Computer security), ANOMALY detection (Computer security), COMPUTER vision, LEARNING ability, VIDEOS

Interference Management for a Wireless Communication Network Using a Recurrent Neural Network Approach.

Autoři: Sejan, Mohammad Abrar Shakil Rahman, Md Habibur Aziz, Md Abdul a další

Zdroj: Mathematics (2227-7390); Jun2024, Vol. 12 Issue 11, p1755, 17p

Témata: WIRELESS communications, RECURRENT neural networks, COMMUNICATION in management, CO-channel interference, TELECOMMUNICATION, MACHINE learning