In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential r...
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In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)*** proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the *** optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each *** the score values of alternatives are computed based on the aggregated *** alternative with the maximum score value is selected as a better *** applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning ***,we have validated the proposed approach with a numerical ***,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.
The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprec...
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The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE
The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of *** identifies better locally optimal solutions than the original K-means *** is,it achieves solutions that yield smaller objective function v...
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The Coordinate Descent Method for K-means(CDKM)is an improved algorithm of *** identifies better locally optimal solutions than the original K-means *** is,it achieves solutions that yield smaller objective function values than the K-means ***,CDKM is sensitive to initialization,which makes the K-means objective function values not small *** selecting suitable initial centers is not always possible,this paper proposes a novel algorithm by modifying the process of *** proposed algorithm first obtains the partition matrix by CDKM and then optimizes the partition matrix by designing the split-merge criterion to reduce the objective function value *** split-merge criterion can minimize the objective function value as much as possible while ensuring that the number of clusters remains *** algorithm avoids the distance calculation in the traditional K-means algorithm because all the operations are completed only using the partition *** on ten UCI datasets show that the solution accuracy of the proposed algorithm,measured by the E value,is improved by 11.29%compared with CDKM and retains its efficiency advantage for the high dimensional *** proposed algorithm can find a better locally optimal solution in comparison to other tested K-means improved algorithms in less run time.
Thanks to its ubiquity,using radio frequency (RF) signals for sensing has found widespread *** traditional integrated sensing and communication systems,such as joint radar-communication systems,common sensing tasks in...
Thanks to its ubiquity,using radio frequency (RF) signals for sensing has found widespread *** traditional integrated sensing and communication systems,such as joint radar-communication systems,common sensing tasks include target localization and ***,increasingly intelligent systems,such as smart agriculture,lowaltitude economy,and smart healthcare,have demanded more comprehensive and continuous information sensing capabilities to support higher-level *** sensing has the potential to offer both spatial and temporal continuity,meeting the multi-dimensional sensing needs of these intelligent ***,numerous advanced systems have been proposed,expanding the application scope of RF sensing to be more pervasive,including discrete state ubiquitous sensing tasks (such as material identification [1]),and continuous state ubiquitous sensing tasks (such as health monitoring [2]).With the advent of the 6G era,it is anticipated that the sensing potential of RF systems will be further unleashed.
The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE service...
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The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.
The coronavirus disease 2019 (COVID-19) has posed significant challenges globally, with image classification becoming a critical tool for detecting COVID-19 from chest X-ray and CT images. Convolutional neural network...
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In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large langua...
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In this paper,we investigate a Reconfigurable Intelligent Surface(RIS)-assisted secure Symbiosis Radio(SR)network to address the information leakage of the primary transmitter(PTx)to potential ***,the RIS serves as a ...
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In this paper,we investigate a Reconfigurable Intelligent Surface(RIS)-assisted secure Symbiosis Radio(SR)network to address the information leakage of the primary transmitter(PTx)to potential ***,the RIS serves as a secondary transmitter in the SR network to ensure the security of the communication between the PTx and the Primary Receiver(PRx),and simultaneously transmits its information to the PTx concurrently by configuring the phase *** the presence of multiple eavesdroppers and uncertain channels in practical scenarios,we jointly optimize the active beamforming of PTx and the phase shifts of RIS to maximize the secrecy energy efficiency of RIS-supported SR networks while satisfying the quality of service requirement and the secure communication *** solve this complicated non-convex stochastic optimization problem,we propose a secure beamforming method based on Proximal Policy Optimization(PPO),which is an efficient deep reinforcement learning algorithm,to find the optimal beamforming strategy against *** results show that the proposed PPO-based method is able to achieve fast convergence and realize the secrecy energy efficiency gain by up to 22%when compared to the considered benchmarks.
Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy ...
Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.
Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of f...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.
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