The Deep Neural Networks(DNN)training process is widely affected by backdoor *** backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavi...
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The Deep Neural Networks(DNN)training process is widely affected by backdoor *** backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning *** state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same *** overcome this problem,in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies,and in order to achieve this objective,we are developing an improved Convolutional Neural Network(ICNN)model optimized using a Gradient-based Optimization(GBO)(ICNN-GBO)*** the ICNN-GBO model,we are injecting the triggers via a steganography and regularization *** are generating triggers using a single-pixel,irregular shape,and different *** performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate,stealthiness,pollution index,anomaly index,entropy index,and *** the CNN-GBO model is trained with the poisoned dataset,it will map the malicious code to the target *** proposed scheme’s effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 andMSCELEB 1M *** results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.
Internet of Medical Things (IoMT) systems have brought transformative benefits to patient monitoring and remote diagnosis in healthcare. However, these systems are prone to various cyber attacks that have a high impac...
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The incorporation of artificial intelligence (AI) into power-related applications signifies a new and unexplored domain in machine learning for predicting power generation. This novel method utilizes prediction models...
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Human body segmentation is utilized in various applications as an intermediate step. This problem is best solved using supervised machine learning solutions, however, they require annotated data for training. Unfortun...
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To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the d...
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To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.
In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to s...
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Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph ...
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Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.
We present a platform for automatic assessment of technical data science skills (hard skills) and competencies that help to apply those technical skills in practice (soft skills). The platform serves so-called assessm...
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Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. It is observed that neural networks are prone to having lesser accuracy in the classification of im...
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This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization....
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