The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-ran...The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.展开更多
Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation...Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.展开更多
The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital...The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.展开更多
Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unma...Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.展开更多
Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over time.Although the original images may provide limited insights,they harbor consi...Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over time.Although the original images may provide limited insights,they harbor considerable potential for identifying clusters and patterns.The aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural activities.In a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific criteria.Our findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature selection.This study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.展开更多
Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from li...Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.展开更多
Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making ...Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making AI-based classification crucial for early detection.Therefore,automated classification using Artificial Intelligence(AI)techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages.This study developed hybrid systems integrating XGBoost(eXtreme Gradient Boosting)with multi-CNN(Convolutional Neural Networks)features based on Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS)algorithms for early classification of MRI(Magnetic Resonance Imaging)images in a multi-class and binary-class MS dataset.All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization(CLAHE).Then,the Gradient Vector Flow(GVF)algorithm was applied to select white matter(regions of interest)within the brain and segment them from the surrounding brain structures.These regions of interest were processed by CNN models(ResNet101,DenseNet201,and MobileNet)to extract deep feature maps,which were then combined into fused feature vectors of multi-CNN model combinations(ResNet101-DenseNet201,DenseNet201-MobileNet,ResNet101-MobileNet,and ResNet101-DenseNet201-MobileNet).The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features.The XGBoost classifier employed the resultant feature vectors for classification.All developed hybrid systems displayed promising outcomes.For multiclass classification,the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4%accuracy,99.45%precision,and 99.75%specificity,surpassing prior studies(93.76%accuracy).It reached 99.6%accuracy,99.65%precision,and 99.55%specificity in binary-class classification.These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.展开更多
Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learni...Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.展开更多
Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This ...Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%.展开更多
Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID...Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID-19).This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation management.These technologies can help prevent outbreaks of any disease that can disperse widely and develop into a pandemic.Initially,a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy beacon packets for smart contact tracing.Second,the connectivity of the device with Android or iOS applications using long-term evolution technology is realized to achieve mobility.Finally,mathematical formulations are proposed to measure the distance between coordinates in order to detect geo-fencing violations.These formulations are specically designed for the virtual circular and polygonal boundaries used to restrict suspected or infected persons from trespassing in predetermined areas,e.g.,at home,in a hospital,or in an isolation ward.The proposed framework outperforms existing solutions,since it is implemented on a wider scale,provides a range of functionalities,and is cost-effective.展开更多
Background:In medical image analysis,the diagnosis of skin lesions remains a challenging task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the...Background:In medical image analysis,the diagnosis of skin lesions remains a challenging task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer.Challenges:Many computerized methods have been introduced in the literature to classify skin cancers.However,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant features.Proposed Work:In this study,a new technique is proposed based on the conventional and deep learning framework.The proposed framework consists of two major tasks:lesion segmentation and classification.In the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination.Subsequently,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding function.In the lesion classification task,two pre-trained CNN models were modified and trained using transfer learning.Deep features were extracted from both models and fused using canonical correlation analysis.During the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this issue.The features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final classification.Results:The experimental process was conducted on two datasets:ISIC 2017 and HAM10000.The ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification task.The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques.展开更多
Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity.Several shortcomings of a network system can be leveraged by...Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity.Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic.Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage.Currently,many automated systems can detect malicious activity,however,the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems.The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic,respectively.Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks,with high accuracy.Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis(PCA).The proposed model incorporates stacked ensemble model extra boosting forest(EBF)which is a combination of tree-based models such as extra tree classifier,gradient boosting classifier,and random forest using a stacked ensemble approach.Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes,respectively.展开更多
Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization.During the last few years,an alarming increase is observed worldwide with a 70%rise in the dise...Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization.During the last few years,an alarming increase is observed worldwide with a 70%rise in the disease since 2000 and an 80%rise in male deaths.If untreated,it results in complications of many vital organs of the human body which may lead to fatality.Early detection of diabetes is a task of significant importance to start timely treatment.This study introduces a methodology for the classification of diabetic and normal people using an ensemble machine learning model and feature fusion of Chi-square and principal component analysis.An ensemble model,logistic tree classifier(LTC),is proposed which incorporates logistic regression and extra tree classifier through a soft voting mechanism.Experiments are also performed using several well-known machine learning algorithms to analyze their performance including logistic regression,extra tree classifier,AdaBoost,Gaussian naive Bayes,decision tree,random forest,and k nearest neighbor.In addition,several experiments are carried out using principal component analysis(PCA)and Chi-square(Chi-2)fea-tures to analyze the influence of feature selection on the performance of machine learning classifiers.Results indicate that Chi-2 features show high performance than both PCA features and original features.However,the highest accuracy is obtained when the proposed ensemble model LTC is used with the proposed fea-ture fusion framework-work which achieves a 0.85 accuracy score which is the highest of the available approaches for diabetes prediction.In addition,the statis-tical T-test proves the statistical significance of the proposed approach over other approaches.展开更多
Requirement gathering for software development project is the most crucial stage and thus requirement engineering (RE) occupies the chief position in the software development. Countless techniques concerning the RE pr...Requirement gathering for software development project is the most crucial stage and thus requirement engineering (RE) occupies the chief position in the software development. Countless techniques concerning the RE processes exist to make sure the requirements are coherent, compact and complete in all respects. In this way different aspects of RE are dissected and detailed upon. A comparison of RE in Agile and RE in Waterfall is expatiated and on the basis of the literature survey the overall Agile RE process is accumulated. Agile being a technique produces high quality software in relatively less time as compared to the conventional waterfall methodology. The paramount objective of this study is to take lessons from RE that Agile method may consider, if quality being the cardinal concern. The study is patterned on the survey of the previous research reported in the coexisting literature and the practices which are being pursued in the area.展开更多
The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations.Using the New York City dataset,which provides us with location tagged crime statistics;we are im...The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations.Using the New York City dataset,which provides us with location tagged crime statistics;we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one.The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results.Moreover,a comparative analysis has been performed among various clustering techniques to obtain best results.we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users.The successful implementation would hopefully aid us to curb the ever-increasing crime rates;as it aims to provide the user with a beforehand knowledge of the route they are about to take.A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer.Thus,addressing a social problem which needs to be eradicated from our modern era.展开更多
Pandemics have always been a nightmare for humanity,especially in developing countries.Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics.Still,developing countries cannot ...Pandemics have always been a nightmare for humanity,especially in developing countries.Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics.Still,developing countries cannot afford such solutions because these may severely damage the country’s econ-omy.Therefore,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly.The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled technologies.Compared to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is proposed.The use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding solutions.Therefore,this study also emphasizes information security,end-to-end solution,and experimental results.Firstly,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing data.Thirdly,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism.Lastly,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine management.The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management.Unlike existing research studies,the security aspect is also considered in the realm of blockchain.The practical implementation of the proposed solution also obtains the results.The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques,which could help battle pandemic situations.Researchers can also consider the 5G-enabled narrowband internet of things(NB-IoT)technologies to implement contact tracing solutions.展开更多
Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational compl...Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.展开更多
Distributed Hash Tables (DHTs) were originated from the design of structured peer-to-peer (P2P) systems. A DHT provides a key-based lookup service similar to a hash table. In this paper, we present the detailed design...Distributed Hash Tables (DHTs) were originated from the design of structured peer-to-peer (P2P) systems. A DHT provides a key-based lookup service similar to a hash table. In this paper, we present the detailed design of a new DHT protocol, Tambour. The novelty of the protocol is that it uses parallel lookup to reduce retrive latency and bounds communication overhead to a dynamically adjusted routing table. Tambour estimates the probabilities of routing entries' liveness based on statistics of node lifetime history and evicts dead entries after lookup failures. When the network is unstable, more routing entries will be evicted in a given period of time, and the routing tables will be getting smaller which minimize the number of timeouts for later lookup requests. An experimental prototype of Tambour has been simulated and compared against two popular DHT protocols. Results show that Tambour outperforms the compared systems in terms of bandwith cost, lookup latency and the overall efficiency.展开更多
Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and tech-no...Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and tech-nologies.Digital twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of care.During the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical images.When collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and patients.Methods To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/system.Furthermore,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of COVID-19.The designed system is based on the cascade recurrent convolution neural network(RCNN)architecture.In this architecture,the detector stages are deeper and more sequentially selective against small and close false positives.This architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the other.At each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different stages.In this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of overfitting.We train the model by using X-ray images as the model was previously trained on another dataset.Results The developed system achieves good accuracy during the detection phase of COVID-19.The experimental outcomes reveal the efficiency of the detection architecture,which yields a mean average precision rate of 0.94.展开更多
Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presen...Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.展开更多
基金funded by King Saud University Researchers Supporting Project Number(RSPD2025R1007),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid expansion of the Internet of Things(IoT)has led to the widespread adoption of sensor networks,with Long-Range Wide-Area Networks(LoRaWANs)emerging as a key technology due to their ability to support long-range communication while minimizing power consumption.However,optimizing network performance and energy efficiency in dynamic,large-scale IoT environments remains a significant challenge.Traditional methods,such as the Adaptive Data Rate(ADR)algorithm,often fail to adapt effectively to rapidly changing network conditions and environmental factors.This study introduces a hybrid approach that leverages Deep Learning(DL)techniques,namely Long Short-Term Memory(LSTM)networks,and Machine Learning(ML)techniques,namely Artificial Neural Networks(ANNs),to optimize key network parameters such as Signal-to-Noise Ratio(SNR)and Received Signal Strength Indicator(RSSI).LSTM-ANN model trained on the“LoRaWAN Path Loss Dataset including Environmental Variables”from Medellín,Colombia,and the model demonstrated exceptional predictive accuracy,achieving an R2 score of 0.999,Mean Squared Error(MSE)of 0.041,Root Mean Squared Error(RMSE)of 0.203,and Mean Absolute Error(MAE)of 0.167,significantly outperforming traditional regression-based approaches.These findings highlight the potential of combining advanced ML and DL techniques to address the limitations of traditional optimization strategies in LoRaWAN.By providing a scalable and adaptive solution for large-scale IoT deployments,this work lays the foundation for real-world implementation,emphasizing the need for continuous learning frameworks to further enhance energy efficiency and network resilience in dynamic environments.
文摘Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.
文摘The rapid convergence of Information and Communication Technologies(ICT),driven by advancements in 5G/6G networks,cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT),is reshaping modern digital ecosystems.As massive,distributed data streams are generated across edge devices and network layers,there is a growing need for intelligent,privacy-preserving AI solutions that can operate efficiently at the network edge.Federated Learning(FL)enables decentralized model training without transferring sensitive data,addressing key challenges around privacy,bandwidth,and latency.Despite its benefits in enhancing efficiency,real-time analytics,and regulatory compliance,FL adoption faces challenges,including communication overhead,heterogeneity,security vulnerabilities,and limited edge resources.While recent studies have addressed these issues individually,the literature lacks a unified,cross-domain perspective that reflects the architectural complexity and application diversity of Convergence ICT.This systematic review offers a comprehensive,cross-domain examination of FL within converged ICT infrastructures.The central research question guiding this review is:How can FL be effectively integrated into Convergence ICT environments,and what are the main challenges in implementing FL in such environments,along with possible solutions?We begin with a foundational overview of FL concepts and classifications,followed by a detailed taxonomy of FL architectures,learning strategies,and privacy-preserving mechanisms.Through in-depth case studies,we analyse FL’s application across diverse verticals,including smart cities,healthcare,industrial automation,and autonomous systems.We further identify critical challenges—such as system and data heterogeneity,limited edge resources,and security vulnerabilities—and review state-of-the-art mitigation strategies,including edge-aware optimization,secure aggregation,and adaptive model updates.In addition,we explore emerging directions in FL research,such as energy-efficient learning,federated reinforcement learning,and integration with blockchain,quantum computing,and self-adaptive networks.This review not only synthesizes current literature but also proposes a forward-looking road map to support scalable,secure,and sustainable FL deployment in future ICT ecosystems.
文摘Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.
文摘Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over time.Although the original images may provide limited insights,they harbor considerable potential for identifying clusters and patterns.The aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural activities.In a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific criteria.Our findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature selection.This study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.
文摘Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.
文摘Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making AI-based classification crucial for early detection.Therefore,automated classification using Artificial Intelligence(AI)techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages.This study developed hybrid systems integrating XGBoost(eXtreme Gradient Boosting)with multi-CNN(Convolutional Neural Networks)features based on Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS)algorithms for early classification of MRI(Magnetic Resonance Imaging)images in a multi-class and binary-class MS dataset.All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization(CLAHE).Then,the Gradient Vector Flow(GVF)algorithm was applied to select white matter(regions of interest)within the brain and segment them from the surrounding brain structures.These regions of interest were processed by CNN models(ResNet101,DenseNet201,and MobileNet)to extract deep feature maps,which were then combined into fused feature vectors of multi-CNN model combinations(ResNet101-DenseNet201,DenseNet201-MobileNet,ResNet101-MobileNet,and ResNet101-DenseNet201-MobileNet).The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features.The XGBoost classifier employed the resultant feature vectors for classification.All developed hybrid systems displayed promising outcomes.For multiclass classification,the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4%accuracy,99.45%precision,and 99.75%specificity,surpassing prior studies(93.76%accuracy).It reached 99.6%accuracy,99.65%precision,and 99.55%specificity in binary-class classification.These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.
基金funded by Ongoing Research Funding Program for Project number(ORF-2025-648),King Saud University,Riyadh,Saudi Arabia.
文摘Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.
基金funded by King Fahd University of Petroleum&Minerals,Saudi Arabia under IRC-SES grant#INRE 2217.
文摘Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%.
基金supported by Data and Articial Intelligence Scientic Chair at Umm Al-Qura University,Makkah City,Saudi Arabia。
文摘Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID-19).This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation management.These technologies can help prevent outbreaks of any disease that can disperse widely and develop into a pandemic.Initially,a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy beacon packets for smart contact tracing.Second,the connectivity of the device with Android or iOS applications using long-term evolution technology is realized to achieve mobility.Finally,mathematical formulations are proposed to measure the distance between coordinates in order to detect geo-fencing violations.These formulations are specically designed for the virtual circular and polygonal boundaries used to restrict suspected or infected persons from trespassing in predetermined areas,e.g.,at home,in a hospital,or in an isolation ward.The proposed framework outperforms existing solutions,since it is implemented on a wider scale,provides a range of functionalities,and is cost-effective.
文摘Background:In medical image analysis,the diagnosis of skin lesions remains a challenging task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer.Challenges:Many computerized methods have been introduced in the literature to classify skin cancers.However,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant features.Proposed Work:In this study,a new technique is proposed based on the conventional and deep learning framework.The proposed framework consists of two major tasks:lesion segmentation and classification.In the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination.Subsequently,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding function.In the lesion classification task,two pre-trained CNN models were modified and trained using transfer learning.Deep features were extracted from both models and fused using canonical correlation analysis.During the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this issue.The features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final classification.Results:The experimental process was conducted on two datasets:ISIC 2017 and HAM10000.The ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification task.The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques.
文摘Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity.Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic.Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage.Currently,many automated systems can detect malicious activity,however,the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems.The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic,respectively.Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks,with high accuracy.Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis(PCA).The proposed model incorporates stacked ensemble model extra boosting forest(EBF)which is a combination of tree-based models such as extra tree classifier,gradient boosting classifier,and random forest using a stacked ensemble approach.Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes,respectively.
基金supported by the Florida Center for Advanced Analytics and Data Science funded by Ernesto.Net(under the Algorithms for Good Grant).
文摘Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization.During the last few years,an alarming increase is observed worldwide with a 70%rise in the disease since 2000 and an 80%rise in male deaths.If untreated,it results in complications of many vital organs of the human body which may lead to fatality.Early detection of diabetes is a task of significant importance to start timely treatment.This study introduces a methodology for the classification of diabetic and normal people using an ensemble machine learning model and feature fusion of Chi-square and principal component analysis.An ensemble model,logistic tree classifier(LTC),is proposed which incorporates logistic regression and extra tree classifier through a soft voting mechanism.Experiments are also performed using several well-known machine learning algorithms to analyze their performance including logistic regression,extra tree classifier,AdaBoost,Gaussian naive Bayes,decision tree,random forest,and k nearest neighbor.In addition,several experiments are carried out using principal component analysis(PCA)and Chi-square(Chi-2)fea-tures to analyze the influence of feature selection on the performance of machine learning classifiers.Results indicate that Chi-2 features show high performance than both PCA features and original features.However,the highest accuracy is obtained when the proposed ensemble model LTC is used with the proposed fea-ture fusion framework-work which achieves a 0.85 accuracy score which is the highest of the available approaches for diabetes prediction.In addition,the statis-tical T-test proves the statistical significance of the proposed approach over other approaches.
文摘Requirement gathering for software development project is the most crucial stage and thus requirement engineering (RE) occupies the chief position in the software development. Countless techniques concerning the RE processes exist to make sure the requirements are coherent, compact and complete in all respects. In this way different aspects of RE are dissected and detailed upon. A comparison of RE in Agile and RE in Waterfall is expatiated and on the basis of the literature survey the overall Agile RE process is accumulated. Agile being a technique produces high quality software in relatively less time as compared to the conventional waterfall methodology. The paramount objective of this study is to take lessons from RE that Agile method may consider, if quality being the cardinal concern. The study is patterned on the survey of the previous research reported in the coexisting literature and the practices which are being pursued in the area.
基金This research was supported by X-mind Corps program of National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(No.2019H1D8A1105622)the Soonchunhyang University Research Fund.
文摘The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations.Using the New York City dataset,which provides us with location tagged crime statistics;we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one.The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results.Moreover,a comparative analysis has been performed among various clustering techniques to obtain best results.we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users.The successful implementation would hopefully aid us to curb the ever-increasing crime rates;as it aims to provide the user with a beforehand knowledge of the route they are about to take.A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer.Thus,addressing a social problem which needs to be eradicated from our modern era.
文摘Pandemics have always been a nightmare for humanity,especially in developing countries.Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics.Still,developing countries cannot afford such solutions because these may severely damage the country’s econ-omy.Therefore,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly.The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled technologies.Compared to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is proposed.The use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding solutions.Therefore,this study also emphasizes information security,end-to-end solution,and experimental results.Firstly,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing data.Thirdly,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism.Lastly,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine management.The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management.Unlike existing research studies,the security aspect is also considered in the realm of blockchain.The practical implementation of the proposed solution also obtains the results.The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques,which could help battle pandemic situations.Researchers can also consider the 5G-enabled narrowband internet of things(NB-IoT)technologies to implement contact tracing solutions.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2022-RG-0104).
文摘Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.
文摘Distributed Hash Tables (DHTs) were originated from the design of structured peer-to-peer (P2P) systems. A DHT provides a key-based lookup service similar to a hash table. In this paper, we present the detailed design of a new DHT protocol, Tambour. The novelty of the protocol is that it uses parallel lookup to reduce retrive latency and bounds communication overhead to a dynamically adjusted routing table. Tambour estimates the probabilities of routing entries' liveness based on statistics of node lifetime history and evicts dead entries after lookup failures. When the network is unstable, more routing entries will be evicted in a given period of time, and the routing tables will be getting smaller which minimize the number of timeouts for later lookup requests. An experimental prototype of Tambour has been simulated and compared against two popular DHT protocols. Results show that Tambour outperforms the compared systems in terms of bandwith cost, lookup latency and the overall efficiency.
文摘Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and tech-nologies.Digital twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of care.During the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical images.When collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and patients.Methods To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/system.Furthermore,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of COVID-19.The designed system is based on the cascade recurrent convolution neural network(RCNN)architecture.In this architecture,the detector stages are deeper and more sequentially selective against small and close false positives.This architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the other.At each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different stages.In this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of overfitting.We train the model by using X-ray images as the model was previously trained on another dataset.Results The developed system achieves good accuracy during the detection phase of COVID-19.The experimental outcomes reveal the efficiency of the detection architecture,which yields a mean average precision rate of 0.94.
文摘Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.
