The arithmetic and logic unit (ALU) is a key element of complex circuits and an intrinsic part of the most widely recognized complex circuits in digital signal processing. Also, recent attention has been brought to re...
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Corn, Rice, and Wheat serve as primary staple foods globally, playing a pivotal role in the economies of numerous countries. Despite their paramount importance, these cereal crops face susceptibility to various diseas...
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Wireless Sensor Networks (WSNs) play an important role in the modern era and security has become an important research area. Intrusion Detection System (IDS) improve network security by monitoring the network state so...
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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.
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.
In recent years, deep learning has significantly advanced skin lesion segmentation. However, annotating medical image data is specialized and costly, while obtaining unlabeled medical data is easier. To address this c...
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To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentat...
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To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object ***,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient ***,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training *** results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,*** detection performance surpasses that of other single-task or multi-task algorithm models.
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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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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Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user *** address this,our study presents a Personalized Adaptive Multi-Prod...
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Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user *** address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited ***-GRU captures both spatial and sequential dependencies in user-item *** innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant *** approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item *** model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional *** study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.
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