Suchergebnisse - Conventional LSTM autoencoder

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders

Autoren: Babaey, Vahid Faragardi, Hamid Reza

Quelle: Computers. 14(6)

Schlagwörter: autoencoder, ensemble classification, GRU, LSTM, neural networks, stacked, tokenization, zero-day attacks

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-368654
https://doi.org/10.3390/computers14060205

Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning

Autoren: Kherad, M. Moayyedi, M. K. Fotouhi-Ghazvini, F. et al.

Quelle: Sensors. 25(16)

Schlagwörter: Autoencoders, Control Of Dynamical Systems, Cyber-physical Systems, Deep Reinforcement Learning, Long Short-term Memory, Sensor-driven Modeling, Circular Cylinders, Computer Control Systems, Convolution, Dynamical Systems, Dynamics, Embedded Systems, Flow Control, Fuel Additives, Nonlinear Dynamical Systems, Real Time Control, Robust Control, Auto Encoders, Control Of Dynamical System, Cybe-physical Systems, High-dimensional, Higher-dimensional, Reinforcement Learning Agent, Reinforcement Learnings, Short Term Memory

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-73172
https://doi.org/10.3390/s25165149

A Study on Abnormal Detection in Urban Inundation Sensor Using LSTM-Autoencoder Techniques

Autoren: Sung Wook An Byung Hyun Lee Byung Sik Kim

Forecasting gold price using hybrid deep neural network LSTM-autoencoder.

Autoren: Saini, Agampreet Singh, Rahul Kumar Sinha, Puneet

Quelle: Discover Artificial Intelligence; 10/22/2025, Vol. 5 Issue 1, p1-19, 19p

Schlagwörter: FORECASTING, LONG short-term memory, GOLD sales & prices, DEEP learning, ECONOMIC forecasting, AUTOENCODERS

Using LSTM Autoencoder and Dynamic Signal Decomposition for Efficient PAPR Reduction in UWA-OFDM

Autoren: Mansoor Jan Syed Agha Hassnain Mohsan Victor P. Gil Jimenez et al.

Quelle: IEEE Access, Vol 13, Pp 195050-195065 (2025)

Schlagwörter: Underwater acoustic communication, orthogonal frequency division multiplexing, long-short term memory, autoencoder, peak-to-average power ratio, dynamic signal decomposition, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/11239062/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/d8d39f7446574d05b1c83e473145391a

Meta-Learning-Based LSTM-Autoencoder for Low-Data Anomaly Detection in Retrofitted CNC Machine Using Multi-Machine Datasets.

Autoren: Woo, Ji-Min Ju, Seong-Hyeon Sung, Jin-Hyeon et al.

Quelle: Systems; Jul2025, Vol. 13 Issue 7, p534, 27p

Schlagwörter: LONG short-term memory, MACHINE learning, OUTLIER detection, DATA reduction, NUMERICAL control of machine tools, MANUFACTURING industries, INDUSTRIAL efficiency

A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection

Autoren: D. Padma Prabha C. Victoria Priscilla

Quelle: The Scientific Temper. 15:2216-2224

Meta-Learning-Based LSTM-Autoencoder for Low-Data Anomaly Detection in Retrofitted CNC Machine Using Multi-Machine Datasets

Autoren: Ji-Min Woo Seong-Hyeon Ju Jin-Hyeon Sung et al.

Quelle: Systems, Vol 13, Iss 7, p 534 (2025)

Schlagwörter: computer numerical control machine, long short-term memory autoencoder, low-data learning, meta-learning, multi-machine learning, predictive maintenance, Systems engineering, TA168, Technology (General), T1-995

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2079-8954/13/7/534; https://doaj.org/toc/2079-8954

Zugangs-URL: https://doaj.org/article/1c72c72b72144dafae47e5ee3ac91317

Energy-efficient Papr Reduction For Aco-ofdm Vlc: A Hybrid Approach Using Zadoff-chu Precoding With Lstm Autoencoder

Autoren: Alisha Chhavi Sharma Pankaj Kumar Sharma

Estimation of Fugl–Meyer Assessment Upper-Extremity Sub-Scores Using a Mixup-Augmented LSTM Autoencoder and Wearable Sensor Data.

Autoren: Liu, Minghao Lu, Hsuan-Yu Tong, Shuk-Fan et al.

Quelle: Sensors (14248220); Nov2025, Vol. 25 Issue 21, p6663, 18p

Schlagwörter: STROKE patients, DEEP learning, MOTOR ability testing, AUTOENCODERS, FORELIMB, WEARABLE technology, MOTOR ability

LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data

Autoren: Siddharth Seshan Dirk Vries Jasper Immink et al.

Quelle: Journal of Hydroinformatics, Vol 26, Iss 2, Pp 441-458 (2024)

Schlagwörter: anomaly detection, arima models, autoencoder models, data reconciliation, wastewater treatment plant data, Information technology, T58.5-58.64, Environmental technology. Sanitary engineering, TD1-1066

Dateibeschreibung: electronic resource

Relation: http://jhydro.iwaponline.com/content/26/2/441; https://doaj.org/toc/1464-7141; https://doaj.org/toc/1465-1734

Zugangs-URL: https://doaj.org/article/9f515eba47544466abe139bc4a7d907a

Enhanced Wearable ECG Monitoring with LSTM Autoencoder-Based Anomaly Detection

Autoren: Abbasi, Ali Raza Samra Aslam Saba Akram et al.

Quelle: Journal of Computers and Intelligent Systems; Vol. 3 No. 4 (2025): J. of Comp. & Int. Sys.; 244-255 ; 3007-3391

Schlagwörter: Wearable Sensors, Anomaly Detection, LSTM Autoencoder, ECG, Machine Learning, Deep Learning

Dateibeschreibung: application/pdf

Relation: https://journals.iub.edu.pk/index.php/JCIS/article/view/4146/2189; https://journals.iub.edu.pk/index.php/JCIS/article/view/4146

Verfügbarkeit: https://journals.iub.edu.pk/index.php/JCIS/article/view/4146

Developing a novel Temporal Air-quality Risk Index using LSTM autoencoder: A case study with South Korean air quality data

Autoren: Hyerim, Park Wonho, Sohn Eunjin, Kang et al.

Quelle: Science of The Total Environment. 978:179303

Schlagwörter: Air Pollutants, Deep Learning, Air Pollution, Republic of Korea, Humans, Environmental Exposure, Autoencoder, Risk Assessment, Environmental Monitoring

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40245507

Deep learning-based anomaly detection in Drift Tube Quadrupole operation for the KOMAC LINAC.

Autoren: Kim, Dong-Hwan Kim, Han-Sung Kwon, Hyeok-Jung et al.

Quelle: Journal of Nuclear Science & Technology; Aug2025, Vol. 62 Issue 8, p709-714, 6p

Schlagwörter: DEEP learning, MACHINE learning, LINEAR accelerators, FAULT diagnosis, AUTOENCODERS, CONDITION-based maintenance, OUTLIER detection

Geografische Kategorien: KOREA

LSTM autoencoder based parallel architecture for deepfake audio detection with dynamic residual encoding and feature fusion.

Autoren: Muruganandham, Priyanka Thangasamy, Govardhana Rajan Jayaraman, Sangeetha et al.

Quelle: Scientific Reports; 7/2/2025, Vol. 15 Issue 1, p1-18, 18p

Schlagwörter: LONG short-term memory, COGNITIVE psychology, AUTOENCODERS, PARALLEL processing, IMPERSONATION

GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis.

Autoren: Lee, Daehee Choo, Hyunseung Jeong, Jongpil

Quelle: Sensors (14248220); Aug2024, Vol. 24 Issue 15, p4855, 24p

Schlagwörter: CONVOLUTIONAL neural networks, TIME complexity, SUPPORT vector machines, MANUFACTURING processes, TIME series analysis, AUTOENCODERS

Noise-robust markerless video gait anomaly detection via two-stage acquisition and LSTM autoencoders.

Autoren: Yoon, Hyerin Jo, Eunah Ryu, Seungjae et al.

Quelle: Scientific Reports; 11/26/2025, Vol. 15 Issue 1, p1-18, 18p

Schlagwörter: SARCOPENIA, PARKINSON'S disease, ORTHOPEDICS, RECURRENT neural networks, GAIT disorders, MOTION analysis

Intelligent Differentiation Framework for Lewy Body Dementia and Alzheimer's Disease Using Adaptive Multi-Cascaded ResNet–Autoencoder–LSTM Network.

Autoren: Sravani, K. RaviSankar, V.

Quelle: International Journal of Image & Graphics; Nov2025, Vol. 25 Issue 6, p1-30, 31p

Comparison of Anomaly Detection Using Statistical Method and Deep Learning Method in Jakarta Air Quality Index Data.

Autoren: Pangestika, Adelia Putri Angraini, Yenni Sumertajaya, I Made

Quelle: Ingénierie des Systèmes d'Information; May2025, Vol. 30 Issue 5, p1313-1324, 12p

Schlagwörter: AIR quality indexes, DEEP learning, OUTLIER detection, MATHEMATICAL statistics, AUTOENCODERS, POLLUTION

Geografische Kategorien: JAKARTA (Indonesia)

Text Mining and Unsupervised Deep Learning for Intrusion Detection in Smart-Grid Communication Networks.

Autoren: Azar, Joseph Al Saleh, Mohammed Couturier, Raphaël et al.

Quelle: Internet of Things (IoT); Jun2025, Vol. 6 Issue 2, p22-N.PAG, 22p

Schlagwörter: COMPUTER network traffic, TEXT mining, INDUSTRIAL controls manufacturing, AUTOENCODERS, ANOMALY detection (Computer security), DEEP learning