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Found 11 papers, 0 papers with code
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction
In our proposed traffic prediction model, we used an average of all IMFs components for signal denoising.
DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection
Our increasingly connected world continues to face an ever-growing amount of network-based attacks.
Transfer Learning Based Efficient Traffic Prediction with Limited Training Data
In this paper, we investigated and evaluated the performance of the deep transfer learning technique in traffic prediction with inadequate historical data leveraging the knowledge of our pre-trained model.
Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution Internet Traffic Prediction
However, our proposed hybrid model considerably reduces the performance gap between identical and out-of-distribution evaluation compared with the standalone model, indicating the decomposition technique's effectiveness in the case of out-of-distribution generalization.
Deep Sequence Modeling for Anomalous ISP Traffic Prediction
Several deep sequences models were implemented to predict real traffic without and with outliers and show the significance of outlier detection in real-world traffic prediction.
Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction
As a result, many different mathematical models have been studied to capture the general trend of the network traffic and predict accordingly.
An Empirical Study on Internet Traffic Prediction Using Statistical Rolling Model
Then, we performed a comparative performance analysis among AutoRegressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), SARIMA with eXogenous factors (SARIMAX), and Holt-Winter for single-step prediction.
Appliance Operation Modes Identification Using Cycles Clustering
The emerging technologies of Internet of Things (IoT) and Machine Learning (ML) deliver techniques that have the potential to efficiently conserve energy and improve the utilization of energy consumption.
Towards Network Traffic Monitoring Using Deep Transfer Learning
Network traffic is growing at an outpaced speed globally.
Machine Learning Towards Enabling Spectrum-as-a-Service Dynamic Sharing
Adopting effective and efficient spectrum sharing mechanisms is in itself a challenging task given the multitude of levels and techniques that can be integrated to enable it.
Demand Response For Residential Uses: A Data Analytics Approach
In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption.
