Vendor lock-in can occur at any layer of the cloud stack-Infrastructure,Platform,and Software-as-a-service.This paper covers the vendor lock-in issue at Platform as a Service(PaaS)level where applications can be creat...Vendor lock-in can occur at any layer of the cloud stack-Infrastructure,Platform,and Software-as-a-service.This paper covers the vendor lock-in issue at Platform as a Service(PaaS)level where applications can be created,deployed,and managed without worrying about the underlying infrastructure.These applications and their persisted data on one PaaS provider are not easy to port to another provider.To overcome this issue,we propose a middleware to abstract and make the database services as cloud-agnostic.The middleware supports several SQL and NoSQL data stores that can be hosted and ported among disparate PaaS providers.It facilitates the developers with data portability and data migration among relational and NoSQL-based cloud databases.NoSQL databases are fundamental to endure Big Data applications as they support the handling of an enormous volume of highly variable data while assuring fault tolerance,availability,and scalability.The implementation of the middleware depicts that using it alleviates the efforts of rewriting the application code while changing the backend database system.A working protocol of a migration tool has been developed using this middleware to facilitate the migration of the database(move existing data from a database on one cloud to a new database even on a different cloud).Although the middleware adds some overhead compared to the native code for the cloud services being used,the experimental evaluation on Twitter(a Big Data application)data set,proves this overhead is negligible.展开更多
During its growth stage,the plant is exposed to various diseases.Detection and early detection of crop diseases is amajor challenge in the horticulture industry.Crop infections can harmtotal crop yield and reduce farm...During its growth stage,the plant is exposed to various diseases.Detection and early detection of crop diseases is amajor challenge in the horticulture industry.Crop infections can harmtotal crop yield and reduce farmers’income if not identified early.Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves.This is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.An alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without pre-processing.To automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of DenseNet.To date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves automatically.Themodelswere trained on a dataset obtained from the Plant Village repository.The dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training components.Therefore,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced.展开更多
The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compound...The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.展开更多
In this paper, the problems of the stability analysis and BIBO stabilization for the switched systems are considered. Applying a stabilizing local state feedback to each subsystem, the sufficient conditions of the asy...In this paper, the problems of the stability analysis and BIBO stabilization for the switched systems are considered. Applying a stabilizing local state feedback to each subsystem, the sufficient conditions of the asymptotically stable and BIBO stabilization for the switched systems are obtained by means of the method of Lyapunov function and the method of inequality analysis.展开更多
In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuse...In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some flaws.To overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image.Recently,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital watermarking.This research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host image.This way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark images.Lifting Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking methodologies.Furthermore,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the image.By integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional methods.It achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Similarity Index Measure(SSIM)of 0.98,and a Number of Pixels Change Rate(NPCR)of 99.79%,respectively.These results reflect the model’s effectiveness in delivering superior quality and security.Consequently,our proposed approach offers accurate results,exceptional invisibility,and enhanced robustness compared to the existing digital image watermarking techniques.展开更多
In object detection,spatial knowledge assisted systems are effective.Object detection is a main and challenging issue to analyze object-related information.Several existing object detection techniques were developed t...In object detection,spatial knowledge assisted systems are effective.Object detection is a main and challenging issue to analyze object-related information.Several existing object detection techniques were developed to consider the object detection problem as a classification problem to perform feature selection and classification.But these techniques still face,less computational efficiency and high time consumption.This paper resolves the above limitations using the Fuzzy Tversky index Ontology-based Multi-Layer Perception method which improves the accuracy of object detection with minimum time.The proposed method uses a multilayer forfinding the similarity score.A fuzzy membership function is used to validate the score for predicting the burned and non-burned zone.Experimental assessment is performed with different factors such as classification rate,time complexity,error rate,space complexity,and precision by using the forestfire dataset.The results show that this novel technique can help to improve the classification rate and reduce the time and space complexity as well as error rate than the conventional methods.展开更多
This study presents a multi-scale deep-learning framework that integrates several advanced neural models to optimize hybrid three dimensional(3D)printed self-sensing nano-carbon cementitious composites.The first step ...This study presents a multi-scale deep-learning framework that integrates several advanced neural models to optimize hybrid three dimensional(3D)printed self-sensing nano-carbon cementitious composites.The first step was undertaken by Multi-Scale Graph Neural Network,where special conductive pathways were taught ensuring the uniform work on nano-carbon learning patterns,improving electrical conductivity by 25%–35%.four-dimensional Spatiotemporal Transformer Network decoded printing parameters achievements with an interlayer conductivity improvement of 40%–50%,avoiding anisotropic print by aiming for defects prediction on print Induced anisotropic behavior.High-fidelity artificial microstructures have been generated with Physics Informed Generative Adversarial Networks;these synthetic methods realize an experimental cost-cutting of about 50%while conserving about 98%fidelity to the characteristics of real microstructures.Fifth,Self-Supervised Contrastive Learning automatically classifies small macro and microdefects with over 95%detection reliability.There has been reduction of as much as 35%in the number of false positives.Predicted kinetics of hydration and long-term electrical stability can now be predicted with speed improvements of 15%and resistance drift reduction by 20%over six months.This approach for the first time combines different hybrid models of deep learning for the self-sensing cementitious composites,thus significantly increasing percolation of electrical networks,accuracy in crack detection,and predictions on long-term durability.The developed framework creates a new paradigm in the real-time structural health monitoring world,providing enhanced reliability in structures while also reducing costs at a level for the next generation of smart infrastructure sets.展开更多
Due to their open,expansive,and resource-constrained character,Wireless Sensor Networks(WSNs)face significant energy,efficiency,and security issues.With a fair level of energy and resource consumption,several lightwei...Due to their open,expansive,and resource-constrained character,Wireless Sensor Networks(WSNs)face significant energy,efficiency,and security issues.With a fair level of energy and resource consumption,several lightweight cryptographic techniques are introduced to increase the security as well as effectiveness of WSNs.Still,they have problems with scalability,key distribution,security,and power management.This work proposes a novel light weighted key distribution mechanism for safe and energy efficient communication in WSN.The proposed model includes stages like optimal Cluster Head Selection(CHS),improved Elliptic Curve Cryptography(ECC)-based encryption,and lightweight key management,respectively.In the first phase,a hybrid optimization strategy is proposed,termed as Coot updated Butterfly algorithm with Logistic Solution Space algorithm(CUBA-LSS)for optimal clustering via selecting the optimal CH.This selection process relies on the consideration of the Received Signal Strength Indicator(RSSI),energy,delay,and distance.Data transmission is the subsequent process,where the proposed algorithm ensures secured transmission through Improved ECC.At last,a lightweight key management system is determined via session key generation to protect the encryption key.展开更多
文摘Vendor lock-in can occur at any layer of the cloud stack-Infrastructure,Platform,and Software-as-a-service.This paper covers the vendor lock-in issue at Platform as a Service(PaaS)level where applications can be created,deployed,and managed without worrying about the underlying infrastructure.These applications and their persisted data on one PaaS provider are not easy to port to another provider.To overcome this issue,we propose a middleware to abstract and make the database services as cloud-agnostic.The middleware supports several SQL and NoSQL data stores that can be hosted and ported among disparate PaaS providers.It facilitates the developers with data portability and data migration among relational and NoSQL-based cloud databases.NoSQL databases are fundamental to endure Big Data applications as they support the handling of an enormous volume of highly variable data while assuring fault tolerance,availability,and scalability.The implementation of the middleware depicts that using it alleviates the efforts of rewriting the application code while changing the backend database system.A working protocol of a migration tool has been developed using this middleware to facilitate the migration of the database(move existing data from a database on one cloud to a new database even on a different cloud).Although the middleware adds some overhead compared to the native code for the cloud services being used,the experimental evaluation on Twitter(a Big Data application)data set,proves this overhead is negligible.
基金funded by UKRI EPSRC Grant EP/W020408/1 Project SPRITE+2:The Security,Privacy,Identity,and Trust Engagement Network plus(phase 2)for this studyfunded by PhD project RS718 on Explainable AI through the UKRI EPSRC Grant-funded Doctoral Training Centre at Swansea University.
文摘During its growth stage,the plant is exposed to various diseases.Detection and early detection of crop diseases is amajor challenge in the horticulture industry.Crop infections can harmtotal crop yield and reduce farmers’income if not identified early.Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves.This is an excellent use case for Community Assessment and Treatment Services(CATS)due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.An alternative to conventional Machine Learning(ML)methods,which require manual identification of parameters for exact results,is to develop a prototype that can be classified without pre-processing.To automatically diagnose tomato leaf disease,this research proposes a hybrid model using the Convolutional Auto-Encoders(CAE)network and the CNN-based deep learning architecture of DenseNet.To date,none of the modern systems described in this paper have a combined model based on DenseNet,CAE,and ConvolutionalNeuralNetwork(CNN)todiagnose the ailments of tomato leaves automatically.Themodelswere trained on a dataset obtained from the Plant Village repository.The dataset consisted of 9920 tomato leaves,and the model-tomodel accuracy ratio was 98.35%.Unlike other approaches discussed in this paper,this hybrid strategy requires fewer training components.Therefore,the training time to classify plant diseases with the trained algorithm,as well as the training time to automatically detect the ailments of tomato leaves,is significantly reduced.
文摘The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.
文摘In this paper, the problems of the stability analysis and BIBO stabilization for the switched systems are considered. Applying a stabilizing local state feedback to each subsystem, the sufficient conditions of the asymptotically stable and BIBO stabilization for the switched systems are obtained by means of the method of Lyapunov function and the method of inequality analysis.
文摘In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some flaws.To overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image.Recently,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital watermarking.This research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host image.This way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark images.Lifting Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking methodologies.Furthermore,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the image.By integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional methods.It achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Similarity Index Measure(SSIM)of 0.98,and a Number of Pixels Change Rate(NPCR)of 99.79%,respectively.These results reflect the model’s effectiveness in delivering superior quality and security.Consequently,our proposed approach offers accurate results,exceptional invisibility,and enhanced robustness compared to the existing digital image watermarking techniques.
文摘In object detection,spatial knowledge assisted systems are effective.Object detection is a main and challenging issue to analyze object-related information.Several existing object detection techniques were developed to consider the object detection problem as a classification problem to perform feature selection and classification.But these techniques still face,less computational efficiency and high time consumption.This paper resolves the above limitations using the Fuzzy Tversky index Ontology-based Multi-Layer Perception method which improves the accuracy of object detection with minimum time.The proposed method uses a multilayer forfinding the similarity score.A fuzzy membership function is used to validate the score for predicting the burned and non-burned zone.Experimental assessment is performed with different factors such as classification rate,time complexity,error rate,space complexity,and precision by using the forestfire dataset.The results show that this novel technique can help to improve the classification rate and reduce the time and space complexity as well as error rate than the conventional methods.
文摘This study presents a multi-scale deep-learning framework that integrates several advanced neural models to optimize hybrid three dimensional(3D)printed self-sensing nano-carbon cementitious composites.The first step was undertaken by Multi-Scale Graph Neural Network,where special conductive pathways were taught ensuring the uniform work on nano-carbon learning patterns,improving electrical conductivity by 25%–35%.four-dimensional Spatiotemporal Transformer Network decoded printing parameters achievements with an interlayer conductivity improvement of 40%–50%,avoiding anisotropic print by aiming for defects prediction on print Induced anisotropic behavior.High-fidelity artificial microstructures have been generated with Physics Informed Generative Adversarial Networks;these synthetic methods realize an experimental cost-cutting of about 50%while conserving about 98%fidelity to the characteristics of real microstructures.Fifth,Self-Supervised Contrastive Learning automatically classifies small macro and microdefects with over 95%detection reliability.There has been reduction of as much as 35%in the number of false positives.Predicted kinetics of hydration and long-term electrical stability can now be predicted with speed improvements of 15%and resistance drift reduction by 20%over six months.This approach for the first time combines different hybrid models of deep learning for the self-sensing cementitious composites,thus significantly increasing percolation of electrical networks,accuracy in crack detection,and predictions on long-term durability.The developed framework creates a new paradigm in the real-time structural health monitoring world,providing enhanced reliability in structures while also reducing costs at a level for the next generation of smart infrastructure sets.
文摘Due to their open,expansive,and resource-constrained character,Wireless Sensor Networks(WSNs)face significant energy,efficiency,and security issues.With a fair level of energy and resource consumption,several lightweight cryptographic techniques are introduced to increase the security as well as effectiveness of WSNs.Still,they have problems with scalability,key distribution,security,and power management.This work proposes a novel light weighted key distribution mechanism for safe and energy efficient communication in WSN.The proposed model includes stages like optimal Cluster Head Selection(CHS),improved Elliptic Curve Cryptography(ECC)-based encryption,and lightweight key management,respectively.In the first phase,a hybrid optimization strategy is proposed,termed as Coot updated Butterfly algorithm with Logistic Solution Space algorithm(CUBA-LSS)for optimal clustering via selecting the optimal CH.This selection process relies on the consideration of the Received Signal Strength Indicator(RSSI),energy,delay,and distance.Data transmission is the subsequent process,where the proposed algorithm ensures secured transmission through Improved ECC.At last,a lightweight key management system is determined via session key generation to protect the encryption key.
