Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI
refers to the practice of doing AI computations near the users at the network's edge, instead …

With the rapid development of the Internet of Everything (IoE), the number of smart devices
connected to the Internet is increasing, resulting in large-scale data, which has caused …

This study analyzes the sustainability of the metaverse technology by adopting a
responsible digitalization perspective to drive the transition to Industry 5.0. This is motivated …

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …

The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the
impact of COVID-19, and the push for alternative greener energy are driving the need for …

Edge computing is a distributed computing paradigm such that client data are processed at
the periphery of the network, as close as possible to the originating source. Since the 21st …

We demonstrate a 222-GBaud OOK optical transmitter for short-reach transmission, with
direct detection after 120 m of standard single mode fiber. To reach this record symbol rate …

The microservices architecture is a promising approach for application development,
deployment, and evolution, both on cloud and emerging fog/edge platforms. Microservices' …

The advancement in resource-intensive and latency-sensitive applications challenge the
legacy systems in Mobile Cloud Computing (MCC) in terms of network congestion …