Search Results - "Convolutional LSTM autoencoder"

Video Anomaly Detection using Variational Convolutional LSTM Autoencoder

Authors: Sandhya Rani Sahoo Jaideep Kokkiligadda Ratnakar Dash

Source: 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

Authors: Yangde Gao Zahoor Ahmad Jong-Myon Kim

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

Subject Terms: 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

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

Bidirectional Convolutional LSTM Autoencoder for Risk Detection

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

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

Authors: Raju S. Bapi Manish Gupta Vijay Rowtula et al.

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

Subject Terms: 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

Authors: Jacob Nogas Shehroz S. Khan Alex Mihailidis

Source: EasyChair Preprints.

Access URL: 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.

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

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

Subject Terms: 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

Authors: Bonatti A. F. Vozzi G. Kai Chua C. et al.

Source: Materials Today : Proceedings

Subject Terms: 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.

Authors: Aldayri, Amnah Albattah, Waleed

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

Subject Terms: 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

Authors: Mahfuz, Sazia Zulkernine, Farhana

Index Terms: Computer Science - Machine Learning, text

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

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

Authors: Young Jun Park

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

Visual anomaly detection in video by variational autoencoder

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

Index Terms: 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.

Authors: Mirzaei, Sayeh Ghasemi, Parisa Bakhtyari, Mohammadreza

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

Subject Terms: 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.

Authors: Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem et al.

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

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

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

Authors: Shah, Afnan A.

Source: 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.

Authors: Berroukham, Abdelhafid Housni, Khalid Lahraichi, Mohammed

Source: 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.

Authors: Berwal, Krishan Rathore, Shubham Dahiya, Sanjay et al.

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

Subject Terms: VIDEO surveillance, CLASSIFICATION

Unsupervised Feature Learning for Audio Analysis

Authors: Meyer, Matthias Beutel, Jan Thiele, Lothar

Index Terms: 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.

Authors: Kommanduri, Rangachary Ghorai, Mrinmoy

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

Subject Terms: *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.

Authors: Zhang, Linliang Yan, Lianshan Peng, Shouxin et al.

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

Subject Terms: 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.

Authors: Sejan, Mohammad Abrar Shakil Rahman, Md Habibur Aziz, Md Abdul et al.

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

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