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Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions
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 …
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
Human action recognition using attention based LSTM network with dilated CNN features
Human action recognition in videos is an active area of research in computer vision and
pattern recognition. Nowadays, artificial intelligence (AI) based systems are needed for …
pattern recognition. Nowadays, artificial intelligence (AI) based systems are needed for …
Deep reinforcement learning for cyber security
The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …
being exposed to cyberattacks more than ever. The complexity and dynamics of …
Thirty years of machine learning: The road to Pareto-optimal wireless networks
Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …
complex compelling applications both in military and civilian fields, where the users are able …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Edge-enabled two-stage scheduling based on deep reinforcement learning for internet of everything
Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic,
which is playing an increasingly indispensable role in modern intelligent applications. These …
which is playing an increasingly indispensable role in modern intelligent applications. These …
Secure and energy efficient-based E-health care framework for green internet of things
This paper proposes a secure and energy-efficient Internet of Things (IoT) model for e-
health. The main objective is to secure the transmission and retrieval of biomedical images …
health. The main objective is to secure the transmission and retrieval of biomedical images …
Optimizing space-air-ground integrated networks by artificial intelligence
It is widely acknowledged that the development of traditional terrestrial communication
technologies cannot provide all users with fair and high quality services due to scarce …
technologies cannot provide all users with fair and high quality services due to scarce …
Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing
Offloading computation-intensive tasks (eg, blockchain consensus processes and data
processing tasks) to the edge/cloud is a promising solution for blockchain-empowered …
processing tasks) to the edge/cloud is a promising solution for blockchain-empowered …