This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural net...
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The European Union developed the Smart Readiness Indicator (SRI) to enhance energy efficiency and encourage the adoption of smart technologies in buildings, tackling their high energy usage and carbon emissions. This ...
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Multimodal models can experience multimodal collapse, leading to sub-optimal performance on tasks like fine-grained e-commerce product classification. To address this, we introduce an approach that leverages multimoda...
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Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute the...
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Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Neverthe...
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Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces **...
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Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces *** a GCN model for large-scale graphs in a conventional way requires high computation and storage ***,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant *** this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of *** highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all ***,we discuss some challenges and future research directions of the sampling methods.
Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. U...
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ISBN:
(纸本)9798350342086
Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ
Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in *** reasons inc...
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Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in *** reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security *** protecting MANETs from attack,encryption and authentication schemes have their ***,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the *** are a secondary defiance system for mobile ad-hoc networks *** since they monitor network traffic and report anything ***,many scientists have employed deep neural networks(DNNs)to address intrusion detection *** paper used MANET to recognize com-plex patterns by focusing on security standards through efficiency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent *** with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)*** results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the ti
Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting ...
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The rapid growth in the construction sector has led to increased energy consumption and carbon emissions. Calculating energy usage and emissions is essential to energy security and promoting sustainable sector develop...
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