Suchergebnisse - LSTM autoencoder~

Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM

Autoren: Maulana Ahsan Fadillah Yenni Angraini Rahma Anisa

Quelle: JOIN: Jurnal Online Informatika, Vol 10, Iss 1, Pp 227-238 (2025)

Schlagwörter: ocsvm, weather, Electronic computers. Computer science, QA75.5-76.95, lstm autoencoder, lstm, anomaly detection

Zugangs-URL: https://doaj.org/article/40bb9600e82a47f0a6decd94607ceb8a

LSTM Autoencoder Model to Identify Electric Vehicles in Grouped Smart Meter Data

Autoren: Quakernack, Lars Engelmann, Thomas Haubrock, Jens et al.

Quelle: 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). :1-5

Schlagwörter: electrical vehicle, Autoencoder, LSTM, smart grid, control

Zugangs-URL: https://pub.uni-bielefeld.de/record/3004508
https://doi.org/10.1109/isgteurope62998.2024.10863725

Forecasting gold price using hybrid deep neural network LSTM-autoencoder

Autoren: Agampreet Saini Rahul Kumar Singh Puneet Sinha

Quelle: Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-19 (2025)

Schlagwörter: Gold price prediction, LSTM, CNN, Autoencoders, Hybrid models, Time series forecasting, Computational linguistics. Natural language processing, P98-98.5, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2731-0809

Zugangs-URL: https://doaj.org/article/8664dff01c3d468cb12b3b530daf8eb2

Anomaly Detection of Gas Pipeline Operational Data Using TCN-Autoencoder and LSTM-Autoencoder Models

Autoren: Anak Agung Gde Pradnyana Aditya Firman Ihsan null Hasmawati

Quelle: 2025 International Conference on Data Science and Its Applications (ICoDSA). :1347-1352

Hybrid Outlier Detection Framework Based on Optimized KMeans and HDBSCAN Using Bat Algorithm and LSTM Autoencoder

Autoren: Mai Abdelsamie Hosam Refaat Mohammed Abdallah et al.

Quelle: Statistics, Optimization & Information Computing. 14:970-1017

Monitoring autocorrelated processes using the latent vector in LSTM autoencoder

Autoren: Seohyeon Jeong Dalmin Jeong Seungmin Lee et al.

Quelle: Korean Journal of Applied Statistics. 38:439-454

Adversarial Data Augmentation Enhanced LSTM Autoencoder for Anomaly Detection in Industrial Pipeline Networks

Autoren: Xi Chen Yu Fan Xin Lin et al.

Quelle: Journal of Signal Processing Systems. 97:197-207

Performance Evaluation of LSTM Autoencoder for Behavioral-Based Insider Threat Detection

Autoren: Sara Iseed Mohammad M N Hamarsheh

Quelle: 2025 International Conference on Smart Applications, Communications and Networking (SmartNets). :1-6

Robust Anomaly Detection in Financial Markets Using LSTM Autoencoders and Generative Adversarial Networks

Autoren: Jian Yang Lili Liu

Quelle: Engineering: Open Access. 3:01-11

A Hybrid LSTM-Autoencoder Framework for Accurate Prediction of Diabetic Kidney Disease

Autoren: null Konne Madhavi

Quelle: Journal of Information Systems Engineering and Management. 10:929-941

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

Autoren: Young Jun Park

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

Development and Model Compression of CNN-LSTM-Autoencoder Model for Anomaly Detection of UAVs

Autoren: Sangil Lee Hanseob Lee Dong-Gil Na et al.

Quelle: Journal of Society of Korea Industrial and Systems Engineering. 48:155-162

Anomaly Behavior Detection in Mobile Robot Navigation for Repetitive Tasks Using CNN-LSTM Autoencoder Reconstruction

Autoren: Sheng-Wei Sim Noriyuki Kawarazaki Danny Wee-Kiat Ng et al.

Quelle: 2025 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). :55-60

Deep Learning for Anomaly Detection: A CNN-LSTM Autoencoder Approach

Autoren: Vandana Pathak Manoj Diwakar Sanjay Roka et al.

Quelle: 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). :827-832

Обнаружение аномалий на основе LSTM-Автокодировщик для транзакций по кредитным картам: LSTM-Autoencoder based anomaly detection for credit card transactions

Quelle: Scientific works of KubSTU. :62-73

Predictive maintenace with LSTM autoencoder and explainability by SHAP and LIME

Autoren: Brito , Luís Miguel Vieira de

Quelle: Brito, L. M. V. (2023). Predictive maintenace with LSTM autoencoder and explainability by SHAP and LIME [Dissertação de Mestrado em Ciência de Dados, Universidade Portucalense]. Repositório Institucional UPT. http://hdl.handle.net/11328/5248

Schlagwörter: LSTM, Anomaly detection, SHAP, LIME, Predictive maintenance, Ciências Naturais - Ciências da Computação e da Informação

Dateibeschreibung: application/pdf

Verfügbarkeit: https://hdl.handle.net/11328/5248

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

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

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-18 (2025)

Schlagwörter: Deepfake audio detection, DRDE, LSTM-AE, Feature fusion model, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/42021aa2da2b4e5d9c098ea623a29da6

Abstractive text summarization from Amazon food review dataset using modified attention based Bi-LSTM autoencoder model

Autoren: P L. Prabha M. Parvathy

Quelle: Journal of the Chinese Institute of Engineers. 48:268-282

Intrusion Detection for Edge-IoT Using LSTM-Autoencoder

Autoren: Bodjré Aka Hugues Félix Kié Eba Victoire N’guessan N’takpé Christian Placide et al.

Quelle: Open Journal of Applied Sciences. 15:2638-2647

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

Autoren: Sung Wook An Byung Hyun Lee Byung Sik Kim