Deep learning and medical diagnosis: A review of literature
M Bakator, D Radosav - Multimodal Technologies and Interaction, 2018 - mdpi.com
In this review the application of deep learning for medical diagnosis is addressed. A
thorough analysis of various scientific articles in the domain of deep neural networks …
thorough analysis of various scientific articles in the domain of deep neural networks …
Smart resource allocation for mobile edge computing: A deep reinforcement learning approach
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …
capabilities has brought about a proliferation of numerous complex and computation …
AI-assisted network-slicing based next-generation wireless networks
The integration of communications with different scales, diverse radio access technologies,
and various network resources renders next-generation wireless networks (NGWNs) highly …
and various network resources renders next-generation wireless networks (NGWNs) highly …
A survey of deep learning: Platforms, applications and emerging research trends
WG Hatcher, W Yu - IEEE access, 2018 - ieeexplore.ieee.org
Deep learning has exploded in the public consciousness, primarily as predictive and
analytical products suffuse our world, in the form of numerous human-centered smart-world …
analytical products suffuse our world, in the form of numerous human-centered smart-world …
Blockchain and machine learning for communications and networking systems
Recently, with the rapid development of information and communication technologies, the
infrastructures, resources, end devices, and applications in communications and networking …
infrastructures, resources, end devices, and applications in communications and networking …
Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions
The ever-increasing number of resource-constrained machine-type communication (MTC)
devices is leading to the critical challenge of fulfilling diverse communication requirements …
devices is leading to the critical challenge of fulfilling diverse communication requirements …
[HTML][HTML] Deep learning for network traffic monitoring and analysis (NTMA): A survey
Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
networks, generate a massive and heterogeneous amount of traffic data. In such networks …
Machine learning for networking: Workflow, advances and opportunities
Recently, machine learning has been used in every possible field to leverage its amazing
power. For a long time, the networking and distributed computing system is the key …
power. For a long time, the networking and distributed computing system is the key …
Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning
RQ Hu - IEEE Transactions on Vehicular Technology, 2018 - ieeexplore.ieee.org
This paper studies the joint communication, caching and computing design problem for
achieving the operational excellence and the cost efficiency of the vehicular networks …
achieving the operational excellence and the cost efficiency of the vehicular networks …
Machine learning driven smart electric power systems: Current trends and new perspectives
MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …