A Residual LSTM based Multi-Label Classification Framework for Proactive SLA Management in a Latency Critical NFV Application Use-Case-Reference-Cited by-同舟云学术

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A Residual LSTM based Multi-Label Classification Framework for Proactive SLA Management in a Latency Critical NFV Application Use-Case

Published:2022-01-08 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)
language:
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Author:

Jalodia Nikita¹,Taneja Mohit¹,Davy Alan¹,Dezfouli Behnam²

Affiliation:

1. Waterford Institute Of Technology,Walton Institute for Information and Communication Systems Science,Department of Computing and Mathematics,Waterford,Ireland

2. Santa Clara University,Department of Computer Science and Engineering,Santa Clara,CA,USA

Funder

Science Foundation Ireland

European Regional Development Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9700484/9700495/09700502.pdf?arnumber=9700502

Reference35 articles.

1. Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition

2. Deep Residual Learning for Image Recognition;he,2015

3. Long Short-Term Memory

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