Cloud computing has drastically changed the delivery and consumption of live streaming content.The designs,challenges,and possible uses of cloud computing for live streaming are studied.A comprehensive overview of the...Cloud computing has drastically changed the delivery and consumption of live streaming content.The designs,challenges,and possible uses of cloud computing for live streaming are studied.A comprehensive overview of the technical and business issues surrounding cloudbased live streaming is provided,including the benefits of cloud computing,the various live streaming architectures,and the challenges that live streaming service providers face in delivering high‐quality,real‐time services.The different techniques used to improve the performance of video streaming,such as adaptive bit‐rate streaming,multicast distribution,and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is underlined.Issues such as improving user experience and live streaming service performance using cutting‐edge technology,like artificial intelligence and machine learning are discussed.In addition,the legal and regulatory implications of cloud‐based live streaming,including issues with network neutrality,data privacy,and content moderation are addressed.The future of cloud computing for live streaming is examined in the section that follows,and it looks at the most likely new developments in terms of trends and technology.For technology vendors,live streaming service providers,and regulators,the findings have major policy‐relevant implications.Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector,as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.展开更多
Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognit...Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed a 92.1%Equal Error Rate.展开更多
This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits ...This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.展开更多
Despite the advances mobile devices have endured,they still remain resource-restricted computing devices,so there is a need for a technology that supports these devices.An emerging technology that supports such resour...Despite the advances mobile devices have endured,they still remain resource-restricted computing devices,so there is a need for a technology that supports these devices.An emerging technology that supports such resource-con-strained devices is called fog computing.End devices can offload the task to close-by fog nodes to improve the quality of service and experience.Since com-putation offloading is a multiobjective problem,we need to consider many factors before taking offloading decisions,such as task length,remaining battery power,latency,communication cost,etc.This study uses the multiobjective grey wolf optimization(MOGWO)technique for optimizing offloading decisions.This is thefirst time MOGWO has been applied for computation offloading in fog com-puting.A gravity reference point method is also integrated with MOGWO to pro-pose an enhanced multiobjective grey wolf optimization(E-MOGWO)algorithm.Itfinds the optimal offloading target by taking into account two parameters,i.e.,energy consumption and computational time in a heterogeneous,scalable,multi-fog,multi-user environment.The proposed E-MOGWO is compared with MOG-WO,non-dominated sorting genetic algorithm(NSGA-II)and accelerated particle swarm optimization(APSO).The results showed that the proposed algorithm achieved better results than existing approaches regarding energy consumption,computational time and the number of tasks successfully executed.展开更多
On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains.Mobile cloud computing is one of these domains where complex computation tasks are offloa...On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains.Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’cognitive capacity.However,the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines(VMs).Furthermore,any delays at the cloud end would further aggravate the miseries of real-time tasks.To resolve these issues,this paper proposes an auto-scaling framework(ACF)that strives to maintain the quality of service(QoS)for the end users as per the service level agreement(SLA)negotiated assurance level for service availability.In addition,it also provides an innovative solution for dealing with the VM startup overheads without truncating the running tasks.Unlike the waiting cost and service cost tradeoff-based systems or threshold-rule-based systems,it does not require strict tuning in the waiting costs or in the threshold rules for enhancing the QoS.We explored the design space of the ACF system with the CloudSim simulator.The extensive sets of experiments demonstrate the effectiveness of the ACF system in terms of good reduction in energy dissipation at the mobile devices and improvement in the QoS.At the same time,the proposed ACF system also reduces the monetary costs of the service providers.展开更多
IC(Image Captioning)is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements.However,in existing works,because of the complexity in ima...IC(Image Captioning)is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements.However,in existing works,because of the complexity in images,neglecting major relation between the object in an image,poor quality image,labelling it remains a big problem for researchers.Hence,the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC.So in this research work the main contribution deals with the framework consists of three phases that is image understanding,textual understanding and decoding.Initially,the image understanding phase is initiated with image pre-pro-cessing to enhance image quality.Thereafter,object has been detected using IYV3MMDs(Improved YoloV3 Multishot Multibox Detectors)in order to relate the interrelation between the image and the object,and then it is followed by MBFOCNNs(Modified Bacterial Foraging Optimization in Convolution Neural Networks),which encodes and providesfinal feature vectors.Secondly,the tex-tual understanding phase is performed based on an image which is initiated with preprocessing of text where unwanted words,phrases,punctuations are removed in order to provide a healthy text.It is then followed by MGloVEs(Modified Glo-bal Vectors for Word Representation),which provides a word embedding of fea-tures with the highest priority towards the object present in an image.Finally,the decoding phase has been performed,which decodes the image whether it may be a normal or complex scene image and provides an accurate text by its learning ability using MDAA(Modified Deliberate Adaptive Attention).The experimental outcome of this work shows better accuracy of shows 96.24%when compared to existing and similar methods while generating captions for images.展开更多
NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of servic...NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.展开更多
In this paper, we develop a decentralized algorithm to coord inate a group of mobile robots to search for unknown and transient radio sources. In addition to limited mobility and ranges of communication and sensing, t...In this paper, we develop a decentralized algorithm to coord inate a group of mobile robots to search for unknown and transient radio sources. In addition to limited mobility and ranges of communication and sensing, the robot team has to deal with challenges from signal source anonymity, short transmission duration, and variable transmission power. We propose a two-step approach: First, we decentralize belief functions that robots use to track source locations using checkpoint-based synchronization, and second, we propose a decentralized planning strategy to coordinate robots to ensure the existence of checkpoints. We analyze memory usage, data amount in communication, and searching time for the proposed algorithm. We have implemented the proposed algorithm and compared it with two heuristics. The experimental results show that our algorithm successfully trades a modest amount of memory for the fastest searching time among the three methods.展开更多
Cloud computing has gained widespread popularity over the last decade.Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users.Meta-heuristic techniques in cloud computing have exhibi...Cloud computing has gained widespread popularity over the last decade.Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users.Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms.This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm(NAGCSA)to address the scheduling issue in cloud computing.Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation.The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient,while the global search is performed by using levy flights.The amalgamation of NAG and CSA helps in cost reduction and time-saving for users.The simulation has been carried out on the CloudSim tool on three different real datasets;NASA,HPC2N,and SDSC.The results of the proposed hybrid algorithm have been compared with state-of-art scheduling algorithms(GA,PSO,and CSA),and statistical significance is carried on mean,standard deviation,and best for each algorithm.It has been established that the proposed algorithm minimizes the execution cost and makespan,hence enhancing the quality of service for users.展开更多
Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras avai...Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.展开更多
Heterogeneous Wireless Network is currently a major area of focus in communication engineering.But the important issue in recent communication is the approachability to the wireless networks while maintaining the qual...Heterogeneous Wireless Network is currently a major area of focus in communication engineering.But the important issue in recent communication is the approachability to the wireless networks while maintaining the quality of service.Today,all the wireless access networks are working in tandem to keep the users always connected to the internet cloud that matches the price affordability and performance goals.In order to achieve seamless connectivity,due consideration has to be given to handoff precision and a smaller number of handoffs.Several researchers have used heuristic approaches to solve this issue.In the present work,a hybrid intelligent algorithm has been suggested for vertical handoff decisions.This hybrid intelligent algorithm is based on dual optimization approach which uses“Particle Swarm Optimization(PSO)”and“Mobile Robustness Optimization(MRO)”techniques for improving the quality of services.This approach performs well even in the failure network conditions and gives the best results in terms of connectivity.The results at the last has been compared with the conventional techniques and it has been observed that the proposed methodology outperforms the existing one.展开更多
Power consumption is the bottleneck of system performance. Power reduction has become an important issue in digital circuit design, especially for high performance portable devices (such as cell phones, PDAs, etc.). M...Power consumption is the bottleneck of system performance. Power reduction has become an important issue in digital circuit design, especially for high performance portable devices (such as cell phones, PDAs, etc.). Many power reduction techniques have also been proposed from the system level down to the circuit level. High-speed computation has thus become the expected norm from the average user, instead of being the province of the few with access to a powerful mainframe. Power must be added to the portable unit, even when power is available in non-portable applications, the issue of low-power design is becoming critical. Thus, it is evident that methodologies for the design of high-throughput, low-power digital systems are needed. Techniques for low-power operation are shown in this paper, which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations. The threshold vol-tages of the MTCMOS devices for both low and high Vth are constructed as the low threshold Vth is approximately 150 - 200 mv whereas the high threshold Vth is managed by varying the thickness of the oxide Tox. Hence we are using different threshold voltages with minimum voltages and hence considered this project as ultra-low power designing.展开更多
Nowadays,due to the increase in information resources,the number of parameters and complexity of feature vectors increases.Optimizationmethods offermore practical solutions instead of exact solutions for the solution ...Nowadays,due to the increase in information resources,the number of parameters and complexity of feature vectors increases.Optimizationmethods offermore practical solutions instead of exact solutions for the solution of this problem.The Emperor PenguinOptimizer(EPO)is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor penguins.It shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence features.Although traditional EPO overcomes the optimization problems in continuous search space,many problems today shift to the binary search space.Therefore,in this study,using the power of traditional EPO,binary EPO(BEPO)is presented for the effective solution of binary-nature problems.BEPO algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search space.For this purpose,the sigmoidal functions are preferred in determining the emperor positions.In addition,the boundaries of the search space remain constant by choosing binary operators.BEPO’s performance is evaluated over twenty-nine benchmarking functions.Statistical evaluations are made to reveal the superiority of the BEPO algorithm.In addition,the performance of the BEPO algorithm was evaluated for the binary feature selection problem.The experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.展开更多
Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare ind...Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally.However,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures.Objective:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems.Methodology:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger network.Individual patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric architecture.Results:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the network.The proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare stakeholders.It ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.展开更多
This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the in...This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.展开更多
Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision impairment.Symptoms of retinopathy are blurred vision,changes in color perception,red spots,and...Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision impairment.Symptoms of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked eye.In this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses.The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy.The fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent layers.The feature loss factor increases the label value to identify the patterns with the kernel-based matching.The performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and SVM.Experimental results show that the proposed CNN performs better.展开更多
An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective ...An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.展开更多
Because of its on-demand servicing and scalability features in cloud computing,security and confidentiality have converted to key concerns.Maintaining transaction information on thirdparty servers carries significant ...Because of its on-demand servicing and scalability features in cloud computing,security and confidentiality have converted to key concerns.Maintaining transaction information on thirdparty servers carries significant dangers so that malicious individuals trying for illegal access to information data security architecture.This research proposes a security-aware information transfer in the cloud-based on the blowfish algorithm(BFA)to address the issue.The user is verified initially with the identification and separate the imported data using pattern matching technique.Further,BFA is utilised to encrypt and save the data in cloud.This can safeguard the data and streamline the proof so that client cannot retrieve the information without identification which makes the environment secure.The suggested approach’s performance is evaluated using several metrics,including encryption time,decryption time,memory utilisation,and runtime.Compared to the existing methodology,the investigational findings clearly show that the method takes the least time to data encryption.展开更多
The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.Thes...The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.These algorithms are often trained on the transaction behavior and,in some cases,trained on the vulnerabilities that exist in the system.In our approach,we study the feasibility of using the Domain Name(DN)associated with the account in the blockchain and identify whether an account should be tagged malicious or not.Here,we leverage the temporal aspects attached to the DN.Our approach achieves 89.53%balanced-accuracy in detecting malicious blockchain DNs.While our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over time.Nonetheless,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.展开更多
文摘Cloud computing has drastically changed the delivery and consumption of live streaming content.The designs,challenges,and possible uses of cloud computing for live streaming are studied.A comprehensive overview of the technical and business issues surrounding cloudbased live streaming is provided,including the benefits of cloud computing,the various live streaming architectures,and the challenges that live streaming service providers face in delivering high‐quality,real‐time services.The different techniques used to improve the performance of video streaming,such as adaptive bit‐rate streaming,multicast distribution,and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is underlined.Issues such as improving user experience and live streaming service performance using cutting‐edge technology,like artificial intelligence and machine learning are discussed.In addition,the legal and regulatory implications of cloud‐based live streaming,including issues with network neutrality,data privacy,and content moderation are addressed.The future of cloud computing for live streaming is examined in the section that follows,and it looks at the most likely new developments in terms of trends and technology.For technology vendors,live streaming service providers,and regulators,the findings have major policy‐relevant implications.Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector,as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.
基金funded by Researchers Supporting Program at King Saud University (RSPD2023R809).
文摘Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed a 92.1%Equal Error Rate.
基金funded by University Grant Commission with UGC-Ref.No.:3364/(NET-JUNE 2015).
文摘This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.
文摘Despite the advances mobile devices have endured,they still remain resource-restricted computing devices,so there is a need for a technology that supports these devices.An emerging technology that supports such resource-con-strained devices is called fog computing.End devices can offload the task to close-by fog nodes to improve the quality of service and experience.Since com-putation offloading is a multiobjective problem,we need to consider many factors before taking offloading decisions,such as task length,remaining battery power,latency,communication cost,etc.This study uses the multiobjective grey wolf optimization(MOGWO)technique for optimizing offloading decisions.This is thefirst time MOGWO has been applied for computation offloading in fog com-puting.A gravity reference point method is also integrated with MOGWO to pro-pose an enhanced multiobjective grey wolf optimization(E-MOGWO)algorithm.Itfinds the optimal offloading target by taking into account two parameters,i.e.,energy consumption and computational time in a heterogeneous,scalable,multi-fog,multi-user environment.The proposed E-MOGWO is compared with MOG-WO,non-dominated sorting genetic algorithm(NSGA-II)and accelerated particle swarm optimization(APSO).The results showed that the proposed algorithm achieved better results than existing approaches regarding energy consumption,computational time and the number of tasks successfully executed.
基金This research work is funded by TEQIP-III under Assistantship Head:1.3.2.2 in PFMS dated 29.06.2021.
文摘On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains.Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’cognitive capacity.However,the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines(VMs).Furthermore,any delays at the cloud end would further aggravate the miseries of real-time tasks.To resolve these issues,this paper proposes an auto-scaling framework(ACF)that strives to maintain the quality of service(QoS)for the end users as per the service level agreement(SLA)negotiated assurance level for service availability.In addition,it also provides an innovative solution for dealing with the VM startup overheads without truncating the running tasks.Unlike the waiting cost and service cost tradeoff-based systems or threshold-rule-based systems,it does not require strict tuning in the waiting costs or in the threshold rules for enhancing the QoS.We explored the design space of the ACF system with the CloudSim simulator.The extensive sets of experiments demonstrate the effectiveness of the ACF system in terms of good reduction in energy dissipation at the mobile devices and improvement in the QoS.At the same time,the proposed ACF system also reduces the monetary costs of the service providers.
文摘IC(Image Captioning)is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements.However,in existing works,because of the complexity in images,neglecting major relation between the object in an image,poor quality image,labelling it remains a big problem for researchers.Hence,the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC.So in this research work the main contribution deals with the framework consists of three phases that is image understanding,textual understanding and decoding.Initially,the image understanding phase is initiated with image pre-pro-cessing to enhance image quality.Thereafter,object has been detected using IYV3MMDs(Improved YoloV3 Multishot Multibox Detectors)in order to relate the interrelation between the image and the object,and then it is followed by MBFOCNNs(Modified Bacterial Foraging Optimization in Convolution Neural Networks),which encodes and providesfinal feature vectors.Secondly,the tex-tual understanding phase is performed based on an image which is initiated with preprocessing of text where unwanted words,phrases,punctuations are removed in order to provide a healthy text.It is then followed by MGloVEs(Modified Glo-bal Vectors for Word Representation),which provides a word embedding of fea-tures with the highest priority towards the object present in an image.Finally,the decoding phase has been performed,which decodes the image whether it may be a normal or complex scene image and provides an accurate text by its learning ability using MDAA(Modified Deliberate Adaptive Attention).The experimental outcome of this work shows better accuracy of shows 96.24%when compared to existing and similar methods while generating captions for images.
基金the Taif University Researchers Supporting Project number(TURSP-2020/36),Taif University,Taif,Saudi Arabiafundedby Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia。
文摘NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.
基金supported in part by the National Science Foundation (IIS1318638 and IIS1426752)the Shenzhen Science and Technology Project (ZDS Y20120617113312191)
文摘In this paper, we develop a decentralized algorithm to coord inate a group of mobile robots to search for unknown and transient radio sources. In addition to limited mobility and ranges of communication and sensing, the robot team has to deal with challenges from signal source anonymity, short transmission duration, and variable transmission power. We propose a two-step approach: First, we decentralize belief functions that robots use to track source locations using checkpoint-based synchronization, and second, we propose a decentralized planning strategy to coordinate robots to ensure the existence of checkpoints. We analyze memory usage, data amount in communication, and searching time for the proposed algorithm. We have implemented the proposed algorithm and compared it with two heuristics. The experimental results show that our algorithm successfully trades a modest amount of memory for the fastest searching time among the three methods.
基金This research work is funded by University Grant Commission under National Fellowship Programme,Grant No.2017-18/29146.
文摘Cloud computing has gained widespread popularity over the last decade.Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users.Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms.This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm(NAGCSA)to address the scheduling issue in cloud computing.Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation.The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient,while the global search is performed by using levy flights.The amalgamation of NAG and CSA helps in cost reduction and time-saving for users.The simulation has been carried out on the CloudSim tool on three different real datasets;NASA,HPC2N,and SDSC.The results of the proposed hybrid algorithm have been compared with state-of-art scheduling algorithms(GA,PSO,and CSA),and statistical significance is carried on mean,standard deviation,and best for each algorithm.It has been established that the proposed algorithm minimizes the execution cost and makespan,hence enhancing the quality of service for users.
基金This work was supported by the Deanship of Scientific Research at King Khalid University through a General Research Project under Grant Number GRP/41/42.
文摘Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.
基金Deepak Dahiya would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-74.
文摘Heterogeneous Wireless Network is currently a major area of focus in communication engineering.But the important issue in recent communication is the approachability to the wireless networks while maintaining the quality of service.Today,all the wireless access networks are working in tandem to keep the users always connected to the internet cloud that matches the price affordability and performance goals.In order to achieve seamless connectivity,due consideration has to be given to handoff precision and a smaller number of handoffs.Several researchers have used heuristic approaches to solve this issue.In the present work,a hybrid intelligent algorithm has been suggested for vertical handoff decisions.This hybrid intelligent algorithm is based on dual optimization approach which uses“Particle Swarm Optimization(PSO)”and“Mobile Robustness Optimization(MRO)”techniques for improving the quality of services.This approach performs well even in the failure network conditions and gives the best results in terms of connectivity.The results at the last has been compared with the conventional techniques and it has been observed that the proposed methodology outperforms the existing one.
文摘Power consumption is the bottleneck of system performance. Power reduction has become an important issue in digital circuit design, especially for high performance portable devices (such as cell phones, PDAs, etc.). Many power reduction techniques have also been proposed from the system level down to the circuit level. High-speed computation has thus become the expected norm from the average user, instead of being the province of the few with access to a powerful mainframe. Power must be added to the portable unit, even when power is available in non-portable applications, the issue of low-power design is becoming critical. Thus, it is evident that methodologies for the design of high-throughput, low-power digital systems are needed. Techniques for low-power operation are shown in this paper, which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations. The threshold vol-tages of the MTCMOS devices for both low and high Vth are constructed as the low threshold Vth is approximately 150 - 200 mv whereas the high threshold Vth is managed by varying the thickness of the oxide Tox. Hence we are using different threshold voltages with minimum voltages and hence considered this project as ultra-low power designing.
基金This work was supported by the Deanship of Scientific Research at King Khalid University through the Research Groups Program under Grant Number RGP.1/95/42.
文摘Nowadays,due to the increase in information resources,the number of parameters and complexity of feature vectors increases.Optimizationmethods offermore practical solutions instead of exact solutions for the solution of this problem.The Emperor PenguinOptimizer(EPO)is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor penguins.It shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence features.Although traditional EPO overcomes the optimization problems in continuous search space,many problems today shift to the binary search space.Therefore,in this study,using the power of traditional EPO,binary EPO(BEPO)is presented for the effective solution of binary-nature problems.BEPO algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search space.For this purpose,the sigmoidal functions are preferred in determining the emperor positions.In addition,the boundaries of the search space remain constant by choosing binary operators.BEPO’s performance is evaluated over twenty-nine benchmarking functions.Statistical evaluations are made to reveal the superiority of the BEPO algorithm.In addition,the performance of the BEPO algorithm was evaluated for the binary feature selection problem.The experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally.However,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures.Objective:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems.Methodology:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger network.Individual patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric architecture.Results:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the network.The proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare stakeholders.It ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.
文摘This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.
基金the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision impairment.Symptoms of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked eye.In this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses.The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy.The fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent layers.The feature loss factor increases the label value to identify the patterns with the kernel-based matching.The performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and SVM.Experimental results show that the proposed CNN performs better.
文摘An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.
文摘Because of its on-demand servicing and scalability features in cloud computing,security and confidentiality have converted to key concerns.Maintaining transaction information on thirdparty servers carries significant dangers so that malicious individuals trying for illegal access to information data security architecture.This research proposes a security-aware information transfer in the cloud-based on the blowfish algorithm(BFA)to address the issue.The user is verified initially with the identification and separate the imported data using pattern matching technique.Further,BFA is utilised to encrypt and save the data in cloud.This can safeguard the data and streamline the proof so that client cannot retrieve the information without identification which makes the environment secure.The suggested approach’s performance is evaluated using several metrics,including encryption time,decryption time,memory utilisation,and runtime.Compared to the existing methodology,the investigational findings clearly show that the method takes the least time to data encryption.
基金partially funded by the National Blockchain Project(grant number NCSC/CS/2017518)at Indian Institute of Technology KanpurIndia sponsored by the National Cyber Security Coordinator's office of the Government of India and partially by the C3i Center funding from the Science and Engineering Research Board of the Government of India(grant number SERB/CS/2016466).
文摘The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.These algorithms are often trained on the transaction behavior and,in some cases,trained on the vulnerabilities that exist in the system.In our approach,we study the feasibility of using the Domain Name(DN)associated with the account in the blockchain and identify whether an account should be tagged malicious or not.Here,we leverage the temporal aspects attached to the DN.Our approach achieves 89.53%balanced-accuracy in detecting malicious blockchain DNs.While our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over time.Nonetheless,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.
