Asynchronous Time-Sensitive Networking for 5G Backhauling
Fifth Generation (5G) phase 2 rollouts are around the corner to make mobile ultra-reliable and low-latency services a reality. However, to realize […]
Evaluation of Machine Learning Techniques for Security in SDN
Software Defined Networking (SDN) has emerged as the most viable programmable network architecture to solve many challenges in legacy networks. SDN separates […]
Federated Machine Learning
The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be […]
Machine Learning Meets Communication Networks
The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, […]
Vegetation height estimation using ubiquitous foot-based wearable platform
Vegetation height plays a key role in many environmental applications such as landscape characterization, conservation planning and disaster management, and biodiversity assessment […]
Hiding in the Crowd
To cope with the lack of on-device machine learning samples, this article presents a distributed data augmentation algorithm, coined federated data augmentation […]
Gender Identification from Arabic Speech Using Machine Learning
Speech recognition is becoming increasingly used in real-world applications. One of the interesting applications is automatic gender recognition which aims to recognize […]
Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing
This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating […]
Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT
To date, model-based reliable communication with low latency is of paramount importance for time-critical wireless control systems. In this work, we study […]
Proxy Experience Replay
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains […]