… , whereas in unsupervised learning there exist only predictors, hence the algorithms have to
… most popular machine learning algorithms used in practice. Machine learning methods can …

… Additionally, the combination of machine learning algorithms and cloud computing enables
IoT devices to extract valuable insights from large amounts of data, enabling predictive and …

… To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, …
Cloud Computing and Mobile Edge Computing, but we opted to go with mobile over multi-…

… different ML algorithms that are used to overcome the cloud security … , semi-supervised, and
reinforcement learning. Then, we … of machine learning in detecting network security of edge …

… by utilizing Machine Learning techniques and Edge Cloud Computing technology. In
contrast to state-of-the-art contributions, we propose a holistic approach comprising the target-…

… role in the emerging edge computing paradigm, which aims to reduce the wireless
transmission latency between end-users and edge clouds. Deep learning techniques, which have …

… We apply a classic reinforcement learning method named Q-learning, in which we input
environmental parameters, labeled initial parameters and workflow x into the inner model. We …

… on ML techniques in the emerging cloud computing paradigm… of emerging cloud computing
paradigms: cloud, edge, fog, … cloud computing domain ie, edge computing, fog computing, …

… of designing a distributed deep learning-driven task offloading (DDTO) algorithm is to find
a … them, we consider the edge and cloud computing to be heterogeneous environments to …

… for multilevel vehicular edge-cloud computing networks. To conserve energy and …
reinforcement learning form is generated and we propose a distributed deep learning algorithm …