As the usage of multi-cloud setups grows, resource management will become a major concern. Because of the dynamic nature of these environments, as well as fluctuating workloads and service-level targets, an effective ...
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The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and...
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Online social networks are becoming more and more popular, according to recent trends. The user's primary concern is the secure preservation of their data and privacy. A well-known method for preventing individual...
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Latest measurements correlated to the cloud computing technology, found to be very unreliable. For smooth conduction of cloud technology, the report is getting more than 100 values i.e., being added to the cost of the...
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The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study intro...
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The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.
The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human *** majority of currently available methods use either a generative adversarial network(GAN)or a recurren...
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The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human *** majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting *** is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly *** resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting *** network excels in generating conditional text by extracting style vectors from a series of style *** model performs admirably on a range of handwriting synthesis tasks,including the production of text that is *** works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting *** fields include digital forensics,creative writing,and document security.
Unmanned aerial vehicles(UAVs) can be effectively used as serving stations in emergency communications because of their free movements, strong flexibility, and dynamic coverage. In this paper, we propose a coordinated...
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Unmanned aerial vehicles(UAVs) can be effectively used as serving stations in emergency communications because of their free movements, strong flexibility, and dynamic coverage. In this paper, we propose a coordinated multiple points based UAV deployment framework to improve system average ergodic rate, by using the fuzzy C-means algorithm to cluster the ground users and considering exclusive forest channel models for the two cases, i.e., associated with a broken base station or an available base station. In addition, we derive the upper bound of the average ergodic rate to reduce computational complexity. Since deep reinforcement learning(DRL) can deal with the complex forest environment while the large action and state space of UAVs leads to slow convergence, we use a ratio cut method to divide UAVs into groups and propose a hierarchical clustering DRL(HC-DRL) approach with quick convergence to optimize the UAV deployment. Simulation results show that the proposed framework can effectively reduce the complexity, and outperforms the counterparts in accelerating the convergence speed.
To effectively combat atmospheric pollution caused by greenhouse gases, immediately switching to power plants that rely solely on renewable energy sources is imperative. With the vast availability of solar energy in K...
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The concept of making everything easily and widely accessible has revolutionized the network industry. Even with the rapid advancements in networks and information technology, we still have difficulty guarding against...
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Due to an increase in the load of network, load balancing service, i.e., a service that gives an equal volume of each task assignment to each of the servers in data centers, it is usually performed by the specialized ...
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