Výsledky vyhľadávania - "LSTM-Autoencoder"

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

Autori: Maulana Ahsan Fadillah Yenni Angraini Rahma Anisa

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

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

Prístupová URL adresa: https://doaj.org/article/40bb9600e82a47f0a6decd94607ceb8a

Monitoring autocorrelated processes using the latent vector in LSTM autoencoder

Autori: Seohyeon Jeong Dalmin Jeong Seungmin Lee a ďalší

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

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

Autori: Xi Chen Yu Fan Xin Lin a ďalší

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

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

Autori: Mai Abdelsamie Hosam Refaat Mohammed Abdallah a ďalší

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

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

Autori: Sara Iseed Mohammad M N Hamarsheh

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

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

Autori: Anak Agung Gde Pradnyana Aditya Firman Ihsan null Hasmawati

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

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

Autori: Sangil Lee Hanseob Lee Dong-Gil Na a ďalší

Zdroj: 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

Autori: Sheng-Wei Sim Noriyuki Kawarazaki Danny Wee-Kiat Ng a ďalší

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

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

Autori: null Konne Madhavi

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

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

Autori: Vandana Pathak Manoj Diwakar Sanjay Roka a ďalší

Zdroj: 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

Zdroj: Scientific works of KubSTU. :62-73

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

Autori: Quakernack, Lars Engelmann, Thomas Haubrock, Jens a ďalší

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

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

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

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

Autori: P L. Prabha M. Parvathy

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

Intrusion Detection for Edge-IoT Using LSTM-Autoencoder

Autori: Bodjré Aka Hugues Félix Kié Eba Victoire N’guessan N’takpé Christian Placide a ďalší

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

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

Autori: Sung Wook An Byung Hyun Lee Byung Sik Kim

DARE-LSTMAE: LSTM Autoencoder model with attention mechanism and optimized reconstruction error for anomaly detection in biogas plants

Autori: K. Meena

Zdroj: International Journal of Intelligent Systems Technologies and Applications. 24

Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with LSTM Autoencoder for Enhanced Fault Detection

Autori: Rupa Devi B Suseela G Ranjith Kumar Painam a ďalší

Zdroj: Journal of Machine and Computing. :931-942

Forecasting gold price using hybrid deep neural network LSTM-autoencoder

Autori: Agampreet Saini Rahul Kumar Singh Puneet Sinha

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

Predmety: 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

Popis súboru: electronic resource

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

Prístupová URL adresa: https://doaj.org/article/8664dff01c3d468cb12b3b530daf8eb2

Estimation of frequency-dependent impedances in power grids by deep lstm autoencoder and random forest

Autori: Bagheri, Azam Bongiorno, Massimo Gu, Irene Yu-Hua a ďalší

Zdroj: Energies. 14(13)

Predmety: Random forest regres-sion, Frequency-dependent grid impedance, LSTM autoencoder, Time-series analysis, PRBS, Unsupervised deep learning

Popis súboru: electronic

Prístupová URL adresa: https://research.chalmers.se/publication/524893
https://research.chalmers.se/publication/524893/file/524893_Fulltext.pdf

Nonlinear dynamic industrial process fault detection based on attention-enhanced LSTM autoencoder

Autori: Sheng Lin Jianchang Liu Qingxiu Guo a ďalší

Zdroj: Transactions of the Institute of Measurement and Control.