The advancement and ubiquity of digital networks have fundamentally transformed numerous spheres of human activity. At the heart of this phenomenon lies the Transmission Control Protocol (TCP) model, whose influence is particularly notable in the exponential growth of the Internet due to its potential ability to transmit flexibly through an advanced Congestion Control (CC). Seeking an even more efficient CC mechanism, this work proposes the construction of Deep Learning Neural Networks (MLP, LSTM, and CNN) for classifying network congestion levels.

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[1] Marcelo Silva, "Deep Congestion Control", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/nnnc-kf77. Accessed: Dec. 09, 2024.
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doi = {10.21227/nnnc-kf77},
url = {http://dx.doi.org/10.21227/nnnc-kf77},
author = {Marcelo Silva },
publisher = {IEEE Dataport},
title = {Deep Congestion Control},
year = {2024} }
TY - DATA
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Marcelo Silva. (2024). Deep Congestion Control. IEEE Dataport. http://dx.doi.org/10.21227/nnnc-kf77
Marcelo Silva, 2024. Deep Congestion Control. Available at: http://dx.doi.org/10.21227/nnnc-kf77.
Marcelo Silva. (2024). "Deep Congestion Control." Web.
1. Marcelo Silva. Deep Congestion Control [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/nnnc-kf77
Marcelo Silva. "Deep Congestion Control." doi: 10.21227/nnnc-kf77