Distributed Denial of Service (DDoS) attacks pose a significant threat to network infrastructures, leading to service disruptions and potential financial losses. In this study, we propose an ensemble-based approach fo...
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Blockchain is influencing the social media platforms by promising to solve the biggest issues of today, such as privacy concerns and content moderation, and the ability to provide a decentralized system for the manage...
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Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed ***,the open system architecture inherent to federated learning systems raises concer...
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Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed ***,the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential *** attacks turn into a major menace to federated learning on account of their concealed property and potent destructive *** altering the local model during routine machine learning training,attackers can easily contaminate the global *** detection and aggregation solutions mitigate certain threats,but they are still insufficient to completely eliminate the influence generated by ***,federated unlearning that can remove unreliable models while maintaining the accuracy of the global model has become a *** some existing federated unlearning approaches are rather difficult to be applied in large neural network models because of their high computational ***,we propose SlideFU,an efficient anti-poisoning attack federated unlearning *** primary concept of SlideFU is to employ sliding window to construct the training process,where all operations are confined within the *** design a malicious detection scheme based on principal component analysis(PCA),which calculates the trust factors between compressed models in a low-cost way to eliminate unreliable *** confirming that the global model is under attack,the system activates the federated unlearning process,calibrates the gradients based on the updated direction of the calibration *** on two public datasets demonstrate that our scheme can recover a robust model with extremely high efficiency.
Steel, being a widely utilized material in industrial production, holds a pivotal role in ensuring product safety and longevity. Hence, the exploration and implementation of steel surface defect detection technology c...
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Federated Learning (FL) is vulnerable to backdoor attacks - especially distributed backdoor attacks (DBA) that are more persistent and stealthy than centralized backdoor attacks. However, we observe that the attack ef...
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Log parsing, the process of transforming raw logs into structured data, is a key step in the complex computer system's intelligent operation and maintenance and therefore has received extensive attention. Among al...
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Existing deep clustering approaches often struggle with redundant feature learning, which limits their effectiveness. The primary goal of this study is to address these issues by developing a more robust deep clusteri...
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Skin cancer is a serious and potentially life-threatening condition caused by DNA damage in the skin cells, leading to genetic mutations and abnormal cell growth. These mutations can cause the cells to divide and grow...
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Skin cancer is a serious and potentially life-threatening condition caused by DNA damage in the skin cells, leading to genetic mutations and abnormal cell growth. These mutations can cause the cells to divide and grow uncontrollably, forming a tumor on the skin. To prevent skin cancer from spreading and potentially leading to serious complications, it's critical to identify and treat it as early as possible. An innovative two-fold deep learning based skin cancer detection model is presented in this research work. Five main stages make up the proposed model: Preprocessing, segmentation, feature extraction, feature selection, and skin cancer detection. Initially, the Min–max contrast stretching and median filtering used to pre-process the collected raw image. From the pre-processed image, the Region of Intertest (ROI) is identified via optimized mask Region-based Convolutional Neural Network (R-CNN). Then, from the identified ROI areas, the texture features like Illumination-invariant Binary Gabor Pattern (II-BGP), Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM), Color feature such as Color Correlogram and Histogram Intersection, and Shape feature including Moments, Area, Perimeter, Eccentricity, Average bending energy are extracted. To choose the optimal features from the extracted ones, the Golden Eagle Mutated Leader Optimization (GEMLO) is used. The proposed Golden Eagle Mutated Leader Optimization (GEMLO) is the conceptual amalgamation of the standard Mutated Leader Algorithm (MLA) and Golden Eagle Optimizer are used to select best features (GEO). The skin cancer detection is accomplished via two-fold-deep-learning-classifiers, that includes the Fully Convolutional Neural Networks (FCNs) and Multi-Layer Perception (MLP). The final outcome is the combination of the outcomes acquired from Fully Convolutional Neural Networks (FCNs) and Multi-Layer Perception (MLP). The PYTHON platform is being used to implement the suggested model. Using the curre
This paper presents CMOS circuit designs of a ternary adder and a ternary multiplier,formulated using transmission function *** carry signals appearing in these designs allow conventional look-ahead carry techniques t...
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This paper presents CMOS circuit designs of a ternary adder and a ternary multiplier,formulated using transmission function *** carry signals appearing in these designs allow conventional look-ahead carry techniques to be *** with previous similar designs,the circuits proposed in this paper have advantages such as low dissipation,low output impedance,and simplicity of construction.
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