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Sep 10, 2019 · Our study has several genuinely new points: First, it is performed on a large dataset (~50 million flows), which requires a new training scheme ...
Sep 9, 2019 · Overall, this study shows that fine-grained network traffic forecasting using three classes with DNNs is feasible, and that it can be performed ...
We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic ...
We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic ...
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A study of deep learning networks on mobile traffic forecasting. Abstract: With evolution toward the fifth generation (5G) cellular technologies, forecasting ...
We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic ...
A Study of Deep Learning for Network Traffic Data Forecasting. https://doi ... A survey of techniques for internet traffic classification using machine learning.
This paper proposes using ML to enable automated iterative calculations and model attributes such as trends and seasonality, failure events, subsequent ...
The Deep Learning for Network Traffic Prediction (DL4NTP) system is split into two files: DL4NTP.py, which preprocceses the data and implements the models ...
Abstract: Accurately predicting metrics such as bandwidth utilization in future networks can assist service providers in predicting network congestion, ...
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