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A comprehensive survey on applications of transformers for deep learning tasks
Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …
mechanism to capture contextual relationships within sequential data. Unlike traditional …
A comprehensive survey: Evaluating the efficiency of artificial intelligence and machine learning techniques on cyber security solutions
Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …
Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
Intrusion detection in the iot under data and concept drifts: Online deep learning approach
Although the existing machine learning-based intrusion detection systems in the Internet of
Things (IoT) usually perform well in static environments, they struggle to preserve their …
Things (IoT) usually perform well in static environments, they struggle to preserve their …
Dynamic scheduling for stochastic edge-cloud computing environments using a3c learning and residual recurrent neural networks
The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the
emergence of the Fog computing paradigm, which allows seamlessly harnessing both …
emergence of the Fog computing paradigm, which allows seamlessly harnessing both …
DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing
Task scheduling in cloud paradigm brought attention of all researchers as it is a challenging
issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size …
issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size …
Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems
Recommendation systems are often challenged by the existence of cold-start items for which
no previous rating is available. The standard content-based or collaborative-filtering …
no previous rating is available. The standard content-based or collaborative-filtering …
Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge–cloud computing environments
The wide-spread embracement and integration of Internet of Things (IoT) has inevitably lead
to an explosion in the number of IoT devices. This in turn has led to the generation of …
to an explosion in the number of IoT devices. This in turn has led to the generation of …
Enhanced multi-verse optimizer for task scheduling in cloud computing environments
Cloud computing is a trending technology that allows users to use computing resources
remotely in a pay-per-use model. One of the main challenges in cloud computing …
remotely in a pay-per-use model. One of the main challenges in cloud computing …
Task scheduling and VM placement to resource allocation in Cloud computing: challenges and opportunities
Recently, there has been growing interest in distributed models for addressing issues
related to Cloud computing environments, particularly resource allocation. This involves two …
related to Cloud computing environments, particularly resource allocation. This involves two …