In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study in...In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism,thereby creating a more nuanced and efficient method for anomaly detection in complex networks.The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data.This is further bolstered by deep residual learning,which is employed to model intricate nonlinear connections directly from input data.A pivotal innovation in our approach is the incorporation of a residual-based attention mech-anism.This mechanism dynamically adjusts the importance of nodes based on their residual information,thereby significantly enhancing the sensitivity of the model to subtle anomalies.Furthermore,we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data.This mapping is the key to our model’s ability to pinpoint anomalies with greater precision.An extensive experimental setup was used to validate the efficacy of the proposed model.Using attributed social network datasets,we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection.The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.展开更多
The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems.This research presents an innovative hybrid framework t...The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems.This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network(QT-DNN)with Binary Bird Swarm Optimization(BBSO)to enhance resource allocation while preserving Quality of Service(QoS).In contrast to conventional approaches,the QT-DNN accurately predicts task-resource mappings using tensor-based task representation,significantly minimizing computing overhead.The BBSO allocates resources dynamically,optimizing energy efficiency and task distribution.Experimental results from extensive simulations indicate the efficacy of the suggested strategy;the proposed approach demonstrates the highest level of accuracy,reaching 98.1%.This surpasses the GA-SVM model,which achieves an accuracy of 96.3%,and the ART model,which achieves an accuracy of 95.4%.The proposed method performs better in terms of response time with 1.598 as compared to existing methods Energy-Focused Dynamic Task Scheduling(EFDTS)and Federated Energy-efficient Scheduler for Task Allocation in Large-scale environments(FESTAL)with 2.31 and 2.04,moreover,the proposed method performs better in terms of makespan with 12 as compared to Round Robin(RR)and Recurrent Attention-based Summarization Algorithm(RASA)with 20 and 14.The hybrid method establishes a new standard for sustainable and efficient administration of cloud computing resources by explicitly addressing scalability and real-time performance.展开更多
Rapid and precise diagnostic tools for Monkeypox(Mpox)lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses,like smallpox and chickenpox.The morphologic...Rapid and precise diagnostic tools for Monkeypox(Mpox)lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses,like smallpox and chickenpox.The morphological similarities between smallpox,chickenpox,and monkeypox,particularly in how they appear as rashes and skin lesions,which can sometimes make diagnosis challenging.Chickenpox lesions appear in many simultaneous phases and are more diffuse,often beginning on the trunk.In contrast,monkeypox lesions emerge progressively and are typically centralized on the face,palms,and soles.To provide accessible diagnostics,this study introduces a novel method for automated monkeypox lesion classification using the HMTNet(Hybrid Mobile Transformer Network).The convolutional layers and Vision Transformers(ViT)are combined to enhance the spatial features.In addition,we replace the classical MHSA(Multi-head self-attention)with the WMHSA(Window-based Multi-Head Self-Attention)to effectively capture long-range dependencies within image patches and depth-wise separable convolutions for local feature extraction.We trained and validated HMTNet on the two datasets for binary and multiclass classification.The model achieved 98.38% accuracy for multiclass classification using cross-validation and 99.25% accuracy for binary classification.These findings show that the model has the potential to be a useful diagnostic tool for monkeypox,especially in environments with limited resources.展开更多
文摘In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism,thereby creating a more nuanced and efficient method for anomaly detection in complex networks.The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data.This is further bolstered by deep residual learning,which is employed to model intricate nonlinear connections directly from input data.A pivotal innovation in our approach is the incorporation of a residual-based attention mech-anism.This mechanism dynamically adjusts the importance of nodes based on their residual information,thereby significantly enhancing the sensitivity of the model to subtle anomalies.Furthermore,we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data.This mapping is the key to our model’s ability to pinpoint anomalies with greater precision.An extensive experimental setup was used to validate the efficacy of the proposed model.Using attributed social network datasets,we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection.The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.
文摘The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems.This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network(QT-DNN)with Binary Bird Swarm Optimization(BBSO)to enhance resource allocation while preserving Quality of Service(QoS).In contrast to conventional approaches,the QT-DNN accurately predicts task-resource mappings using tensor-based task representation,significantly minimizing computing overhead.The BBSO allocates resources dynamically,optimizing energy efficiency and task distribution.Experimental results from extensive simulations indicate the efficacy of the suggested strategy;the proposed approach demonstrates the highest level of accuracy,reaching 98.1%.This surpasses the GA-SVM model,which achieves an accuracy of 96.3%,and the ART model,which achieves an accuracy of 95.4%.The proposed method performs better in terms of response time with 1.598 as compared to existing methods Energy-Focused Dynamic Task Scheduling(EFDTS)and Federated Energy-efficient Scheduler for Task Allocation in Large-scale environments(FESTAL)with 2.31 and 2.04,moreover,the proposed method performs better in terms of makespan with 12 as compared to Round Robin(RR)and Recurrent Attention-based Summarization Algorithm(RASA)with 20 and 14.The hybrid method establishes a new standard for sustainable and efficient administration of cloud computing resources by explicitly addressing scalability and real-time performance.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Rapid and precise diagnostic tools for Monkeypox(Mpox)lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses,like smallpox and chickenpox.The morphological similarities between smallpox,chickenpox,and monkeypox,particularly in how they appear as rashes and skin lesions,which can sometimes make diagnosis challenging.Chickenpox lesions appear in many simultaneous phases and are more diffuse,often beginning on the trunk.In contrast,monkeypox lesions emerge progressively and are typically centralized on the face,palms,and soles.To provide accessible diagnostics,this study introduces a novel method for automated monkeypox lesion classification using the HMTNet(Hybrid Mobile Transformer Network).The convolutional layers and Vision Transformers(ViT)are combined to enhance the spatial features.In addition,we replace the classical MHSA(Multi-head self-attention)with the WMHSA(Window-based Multi-Head Self-Attention)to effectively capture long-range dependencies within image patches and depth-wise separable convolutions for local feature extraction.We trained and validated HMTNet on the two datasets for binary and multiclass classification.The model achieved 98.38% accuracy for multiclass classification using cross-validation and 99.25% accuracy for binary classification.These findings show that the model has the potential to be a useful diagnostic tool for monkeypox,especially in environments with limited resources.
