Explainable Deep-Learning Approaches for Packet-Level Traffic Prediction of Collaboration and Communication Mobile Apps-Reference-Cited by-同舟云学术

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Explainable Deep-Learning Approaches for Packet-Level Traffic Prediction of Collaboration and Communication Mobile Apps

Published:2024 Issue: Volume:5 Page:1299-1324
ISSN:2644-125X
Container-title:IEEE Open Journal of the Communications Society
language:
Short-container-title:IEEE Open J. Commun. Soc.

Author:

Guarino Idio¹^ORCID,Aceto Giuseppe¹^ORCID,Ciuonzo Domenico¹^ORCID,Montieri Antonio¹^ORCID,Persico Valerio¹^ORCID,Pescapè Antonio¹^ORCID

Affiliation:

1. Department of Electrical Engineering and Information Technologies, University of Napoli “Federico II,”, Naples, Italy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8782661/10362961/10438860.pdf?arnumber=10438860

Reference46 articles.

1. The Lockdown Effect

2. Impact of the COVID-19 pandemic on the Internet latency: A large-scale study

3. Characterizing and Modeling Traffic of Communication and Collaboration Apps Bloomed With COVID-19 Outbreak

4. Instant Messaging Meets Video Conferencing: Studying the Performance of IM Video Calls

Cited by 2 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. Explainable Stacking Models based on Complementary Traffic Embeddings;2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW);2024-07-08

2. XAI for Interpretable Multimodal Architectures with Contextual Input in Mobile Network Traffic Classification;2024 IFIP Networking Conference (IFIP Networking);2024-06-03

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