Search

Now showing items 1-2 of 2

An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

Nakip, M.; Karakayali, K.; Guzelis, C.; Rodoplu, V. (Institute of Electrical and Electronics Engineers Inc., 2021)

We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and ...

Subspace-Based Emulation of the Relationship between Forecasting Error and Network Performance in Joint Forecasting-Scheduling for the Internet of Things

Nakip, M.; Helva, A.; Guzelis, C.; Rodoplu, V. (IEEE, 2021)

We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology, an Artificial Neural Network (ANN) learns ...