Ransomware is one of the most advanced malware which uses high computer resources and services to encrypt system data once it infects a system and causes large financial data losses to the organization and individuals...
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Federated learning is widely used to solve the problem of data decentralization and can provide privacy protectionfor data owners. However, since multiple participants are required in federated learning, this allows a...
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Federated learning is widely used to solve the problem of data decentralization and can provide privacy protectionfor data owners. However, since multiple participants are required in federated learning, this allows attackers tocompromise. Byzantine attacks pose great threats to federated learning. Byzantine attackers upload maliciouslycreated local models to the server to affect the prediction performance and training speed of the global model. Todefend against Byzantine attacks, we propose a Byzantine robust federated learning scheme based on backdoortriggers. In our scheme, backdoor triggers are embedded into benign data samples, and then malicious localmodels can be identified by the server according to its validation dataset. Furthermore, we calculate the adjustmentfactors of local models according to the parameters of their final layers, which are used to defend against datapoisoning-based Byzantine attacks. To further enhance the robustness of our scheme, each localmodel is weightedand aggregated according to the number of times it is identified as malicious. Relevant experimental data showthat our scheme is effective against Byzantine attacks in both independent identically distributed (IID) and nonindependentidentically distributed (non-IID) scenarios.
Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis ...
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Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing ***,traditional optimization methods often overlook the energy imbalance caused by node loa...
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Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing ***,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network *** To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is *** K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search *** proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the *** The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering *** calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network *** This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network *** research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.
Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learnin...
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Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learning-based methods. Among the traditional methods, the methods based on directional features are mainstream because they have high recognition rates and are robust to illumination changes and small noises. However, to date, in these methods, the stability of the palmprint directional response has not been deeply studied. In this paper, we analyse the problem of directional response instability in palmprint recognition methods based on directional feature. We then propose a novel palmprint directional response stability measurement (DRSM) to judge the stability of the directional feature of each pixel. After filtering the palmprint image with the filter bank, we design DRSM according to the relationship between the maximum response value and other response values for each pixel. Using DRSM, we can judge those pixels with unstable directional response and use a specially designed encoding mode related to a specific method. We insert the DRSM mechanism into seven classical methods based on directional feature, and conduct many experiments on six public palmprint databases. The experimental results show that the DRSM mechanism can effectively improve the performance of these methods. In the field of palmprint recognition, this work is the first in-depth study on the stability of the palmprint directional response, so this paper has strong reference value for research on palmprint recognition methods based on directional features.
The growing dependence on deep learning models for medical diagnosis underscores the critical need for robust interpretability and transparency to instill trust and ensure responsible usage. This study investigates th...
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This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in differe...
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This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.
Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to...
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The paper addresses the critical problem of application workflow offloading in a fog environment. Resource constrained mobile and Internet of Things devices may not possess specialized hardware to run complex workflow...
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Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the ef...
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Graph Neural Networks(GNNs)have become a widely used tool for learning and analyzing data on graph structures,largely due to their ability to preserve graph structure and properties via graph representation ***,the effect of depth on the performance of GNNs,particularly isotropic and anisotropic models,remains an active area of *** study presents a comprehensive exploration of the impact of depth on GNNs,with a focus on the phenomena of over-smoothing and the bottleneck effect in deep graph neural *** research investigates the tradeoff between depth and performance,revealing that increasing depth can lead to over-smoothing and a decrease in performance due to the bottleneck *** also examine the impact of node degrees on classification accuracy,finding that nodes with low degrees can pose challenges for accurate *** experiments use several benchmark datasets and a range of evaluation metrics to compare isotropic and anisotropic GNNs of varying depths,also explore the scalability of these *** findings provide valuable insights into the design of deep GNNs and offer potential avenues for future research to improve their performance.
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