Enhanced Anomaly Detection in Manufacturing Processes Through Hybrid Deep Learning Techniques-Reference-Cited by-同舟云学术

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Enhanced Anomaly Detection in Manufacturing Processes Through Hybrid Deep Learning Techniques

Published:2023 Issue: Volume:11 Page:93368-93380
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lee Kyung Sung¹^ORCID,Kim Seong Beom¹^ORCID,Kim Hee-Woong¹^ORCID

Affiliation:

1. Graduate School of Information, Yonsei University, Seoul, South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10230081.pdf?arnumber=10230081

Reference43 articles.

1. Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management

2. Machine learning on big data: Opportunities and challenges

3. Sustainable MLOps: Trends and Challenges

4. Performance of Machine Learning Algorithms for Class-Imbalanced Process Fault Detection Problems

5. Long short term memory networks for anomaly detection in time series;malhotra;Proc ESANN,2015

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predictive Maintenance and Real Time Monitoring using IoT and Cloud Computing;2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN);2024-07-03

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