The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder th...Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices.With this motivation,this study aims to investigate an effective violent activity classifier while minimizing computational complexity,attaining competitive performance,and mitigating user data privacy concerns.We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition.We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction,incorporating federated learning to address data privacy concerns.Experimental findings demonstrate the model’s effectiveness with test accuracies from 96%to above 97%on multiple datasets by incorporating the FedProx aggregation strategy.These findings underscore the potential to develop secure,efficient,and reliable solutions for violent activity recognition in real-world scenarios.展开更多
Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Mag...Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.展开更多
Convolutional neural networks(CNNs)have gained popularity for categorizing hyperspectral(HS)images due to their ability to capture representations of spatial-spectral features.However,their ability to model relationsh...Convolutional neural networks(CNNs)have gained popularity for categorizing hyperspectral(HS)images due to their ability to capture representations of spatial-spectral features.However,their ability to model relationships between data is limited.Graph convolutional networks(GCNs)have been introduced as an alternative,as they are effective in representing and analyzing irregular data beyond grid samplingconstraints.WhileGCNs have traditionally.been computationally intensive,minibatch GCNs(miniGCNs)enable minibatch training of large-scale GCNs.We have improved the classification performance by using miniGCNs to infer out-of-sample data without retraining the network.In addition,fuzing the capabilities of CNNs and GCNs,through concatenative fusion has been shown to improve performance compared to using CNNs or GCNs individually.Finally,support vector machine(SvM)is employed instead of softmax in the classification stage.These techniques were tested on two HS datasets and achieved an average accuracy of 92.80 using Indian Pines dataset,demonstrating the effectiveness of miniGCNs and fusion strategies.展开更多
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
基金Supported by the Research Chair of Online Dialogue and Cultural Communication,King Saud University,Saudi Arabia.
文摘Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices.With this motivation,this study aims to investigate an effective violent activity classifier while minimizing computational complexity,attaining competitive performance,and mitigating user data privacy concerns.We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition.We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction,incorporating federated learning to address data privacy concerns.Experimental findings demonstrate the model’s effectiveness with test accuracies from 96%to above 97%on multiple datasets by incorporating the FedProx aggregation strategy.These findings underscore the potential to develop secure,efficient,and reliable solutions for violent activity recognition in real-world scenarios.
基金This research is funded by the Researchers Supporting Project Number(RSPD2024R1027),King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.
基金supported by Research start up fund for high level talents of FuZhou University of International Studies and Trade[grant no FWKQJ202006]2022 Guiding Project of Fujian Science and Technology Department[grant no 2022H0026].
文摘Convolutional neural networks(CNNs)have gained popularity for categorizing hyperspectral(HS)images due to their ability to capture representations of spatial-spectral features.However,their ability to model relationships between data is limited.Graph convolutional networks(GCNs)have been introduced as an alternative,as they are effective in representing and analyzing irregular data beyond grid samplingconstraints.WhileGCNs have traditionally.been computationally intensive,minibatch GCNs(miniGCNs)enable minibatch training of large-scale GCNs.We have improved the classification performance by using miniGCNs to infer out-of-sample data without retraining the network.In addition,fuzing the capabilities of CNNs and GCNs,through concatenative fusion has been shown to improve performance compared to using CNNs or GCNs individually.Finally,support vector machine(SvM)is employed instead of softmax in the classification stage.These techniques were tested on two HS datasets and achieved an average accuracy of 92.80 using Indian Pines dataset,demonstrating the effectiveness of miniGCNs and fusion strategies.
