During times of disaster, the impact on public health can be profound, with challenges ranging from vector borne diseases like malaria to traumatic injuries such as bone fractures. However, the availability of medical...
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Biometric recognition systems are essential for secure authentication, leveraging unique physiological and behavioral characteristics to identify individuals. The Biometric recognition systems risk of unauthorized acc...
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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.
Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research *** studies have focused on recognizing diseases from images of whole *** approach limits the resulting mod...
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Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research *** studies have focused on recognizing diseases from images of whole *** approach limits the resulting models’ability to estimate leaf disease severity or identify multiple anomalies occurring on the same *** studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition *** those studies,however,the lesions were laboriously cropped by *** study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this *** datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network(CNN)models,*** report that GoogLeNet’s disease recognition accuracy improved by more than 15%when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves.A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this *** proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union(mIoU)score of 0.8448 and 0.6257 for the leaf and lesion pixel classes,*** terms of mean boundary F1 score,the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes,***,a fully automatic algorithm for leaf disease recognition from individual lesions is *** algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease *** proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.
Recent years have seen significant studies into applying machine learning techniques for stock price prediction. However, most existing work in this field focuses on examining historical stock price data for forecasti...
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Increasing demand for spectrum causes the emergence of technologies like Cognitive Radio (CR). The resources like bandwidth and energy are primarily shared by the primary and secondary users in the CR network. The uti...
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This paper proposes a cavity-backed substrate-integrated-waveguide dual-feed self-diplexing MIMO antenna for Internet of Things (IoT) applications. To implement a self-diplexing operation, the half-mode SIW cavity is ...
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The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports opera...
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The surge in online activities has led to the increasing popularity of sharing video data across diverse applications, including online education tutorials, social networking, video calling, and OTT platforms. Encrypt...
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With its untameable and traceable properties,blockchain technology has been widely used in the field of data *** to preserve individual privacy while enabling efficient data queries is one of the primary issues with s...
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With its untameable and traceable properties,blockchain technology has been widely used in the field of data *** to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data *** this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in *** the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy ***,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF *** propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF *** framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy *** data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy *** propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query *** VB-cm tree uses the vector commitment to verify the query *** fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing *** conduct an extensive evaluation of the proposed *** experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.
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