HIGHLIGHTS
- who: Mohamed Khalafalla Hassan and colleagues from the School of Electrical Engineering, University Technology Malaysia, Skudai, Johor, Malaysia have published the research: Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts, in the Journal: Sensors 2022, 22, 3592. of /2022/
- what: This study proposes a reliable hybrid bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. In this work, a real dataset was collected and analyzed. This framework was used to determine when to build new hybrid . . .
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