This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network ...
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This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.
Instagram is an actively used social media platform among the 18-24 age group worldwide. People in this age group are especially vulnerable to mental disorders and suffer health consequences. The paper presents the fi...
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We study the impact of forbidding short cycles to the edge density of k-planar graphs;a k-planar graph is one that can be drawn in the plane with at most k crossings per edge. Specifically, we consider three settings,...
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Recent advances in Augmented and Virtual Reality show that they will play a critical role in our daily lives. However, interaction in such virtual environments is challenging as digital contents cannot be physically f...
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the well-being of individuals is seriously threatened by lung cancer, which has one of the highest fatality rates of all cancers. Therefore, to increase patient life expectancies, early diagnosis of lung nodules is es...
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In preparation for the upcoming FAU Hack-a-Thon, we have implemented extensive support structures to ensure that all participating teams are thoroughly prepared for the competition. This preparation includes the provi...
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Machine Learning (ML) models, particularly Deep Learning (DL), have made rapid progress and achieved significant milestones across various applications, including numerous safety-critical contexts. However, these mode...
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In the burgeoning landscape of Internet of Things (IoT) networks, efficient management of resources is paramount for ensuring optimal performance and resource utilization. Dynamic scheduling, particularly in the conte...
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ISBN:
(纸本)9798350383867
In the burgeoning landscape of Internet of Things (IoT) networks, efficient management of resources is paramount for ensuring optimal performance and resource utilization. Dynamic scheduling, particularly in the context of cloud-edge-terminal IoT networks, presents a significant challenge due to the diverse and dynamic nature of connected devices and their varying computational requirements. Traditional centralized approaches to scheduling may prove inadequate in such dynamic environments, necessitating the exploration of novel techniques. This project proposes a pioneering approach to address the dynamic scheduling challenges in IoT networks by leveraging collaborative policy learning through federated reinforcement techniques. The proposed framework harnesses the power of federated learning, a decentralized machine learning paradigm, to collectively train policies for dynamic scheduling tasks across distributed edge and terminal devices while preserving data privacy and security. Key components of the proposed framework include a collaborative learning architecture that orchestrates the exchange of policy updates among edge and terminal devices, enabling them to adaptively refine their scheduling policies based on local observations and feedback. Reinforcement learning serves as the underlying mechanism for policy optimization, allowing devices to learn and adapt to evolving network conditions and user demands over time. By decentralizing the learning process and leveraging the collective intelligence of edge and terminal devices, the proposed framework offers several advantages. These include improved scalability, reduced communication overhead, and enhanced resilience to network failures. Furthermore, the federated approach ensures data privacy and regulatory compliance by keeping sensitive information localized to individual devices. To evaluate the effectiveness of the proposed framework, comprehensive simulations and real-world experiments will be conducted u
In the present-day scenario, it is observed that the effect of any natural or man-made disaster creates a havoc mess on society. The change in human behavior plays a crucial part in achieving sustainability. The devel...
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Cloud computing has recent advances when it comes to storing and processing the information in a server, away from the end user. Numerous users are using the services offered by cloud for various applications. Some us...
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