American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired perso...American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.展开更多
In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization...In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.展开更多
In the Internet of Things(IoT)based system,the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems(...In the Internet of Things(IoT)based system,the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems(UCS).The UCS necessitates heterogeneity,management level,and data transmission for distributed users.Simultaneously,security remains a major issue in the IoT-driven UCS.Besides,energy-limited IoT devices need an effective clustering strategy for optimal energy utilization.The recent developments of explainable artificial intelligence(XAI)concepts can be employed to effectively design intrusion detection systems(IDS)for accomplishing security in UCS.In this view,this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System(BXAI-IDCUCS)model.The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment.The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization(EADSO)algorithm to accomplish this.Besides,deep neural network(DNN)is employed for detecting and classifying intrusions in the IoT network.Lastly,blockchain technology is exploited for secure inter-cluster data transmission processes.To ensure the productive performance of the BXAI-IDCUCS model,a comprehensive experimentation study is applied,and the outcomes are assessed under different aspects.The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%,a packet loss rate of 0.71%,a throughput of 92.95 Mbps,energy consumption of 0.0891 mJ,a lifetime of 3529 rounds,and accuracy of 99.38%.展开更多
The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,flexibility,and seamless services.Software defined networking(SDN)technology plays a critical role...The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,flexibility,and seamless services.Software defined networking(SDN)technology plays a critical role in network administration in the future generation of VANET withfifth generation(5G)networks.Regardless of the benefits of VANET,energy economy and traffic control are significant architectural challenges.Accurate and real-time trafficflow prediction(TFP)becomes critical for managing traffic effectively in the VANET.SDN controllers are a critical issue in VANET,which has garnered much interest in recent years.With this objective,this study develops the SDNTFP-C technique,a revolutionary SDN controller-based real-time trafficflow forecasting technique for clustered VANETs.The proposed SDNTFP-C technique combines the SDN controller’s scalability,flexibility,and adaptability with deep learning(DL)mod-els.Additionally,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a VANET.The TFP procedure is then performed using a hybrid convolutional neural network model with atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.展开更多
Food security and sustainable development is making a mandatory move in the entire human race.The attainment of this goal requires man to strive for a highly advanced state in thefield of agriculture so that he can pro...Food security and sustainable development is making a mandatory move in the entire human race.The attainment of this goal requires man to strive for a highly advanced state in thefield of agriculture so that he can produce crops with a minimum amount of water and fertilizer.Even though our agricultural methodol-ogies have undergone a series of metamorphoses in the process of a present smart-agricultural system,a long way is ahead to attain a system that is precise and accurate for the optimum yield and profitability.Towards such a futuristic method of cultivation,this paper proposes a novel method for monitoring the efficientflow of a small quantity of water through the conventional irrigation system in cultiva-tion using Clustered Wireless Sensor Networks(CWSN).The performance measure is simulated the creation of edge-fixed geodetic clusters using Mat lab’s Cup-carbon tool in order to evaluate the suggested irrigation process model’s performance.Thefindings of blocks 1 and 2 are assessed.Each signal takes just a little amount of energy to communicate,according to the performance.It is feasible to save energy while maintaining uninterrupted communication between nodes and cluster chiefs.However,the need for proper placement of a dynamic control station in WSN still exists for maintaining connectivity and for improving the lifetime fault tolerance of WSN.Based on the minimum edgefixed geodetic sets of the connected graph,this paper offers an innovative method for optimizing the placement of control stations.The edge-fixed geodetic cluster makes the network fast,efficient and reliable.Moreover,it also solves routing and congestion problems.展开更多
密度峰值聚类(clustering by fast search and find of density peaks,DPC)算法是一种基于密度的聚类算法,它可以发现任意形状和维度的类簇,是具有里程碑意义的聚类算法。然而,DPC算法的样本局部密度定义不适用于同时发现数据集的稠密...密度峰值聚类(clustering by fast search and find of density peaks,DPC)算法是一种基于密度的聚类算法,它可以发现任意形状和维度的类簇,是具有里程碑意义的聚类算法。然而,DPC算法的样本局部密度定义不适用于同时发现数据集的稠密簇和稀疏簇;此外,DPC算法的一步分配策略使得一旦有一个样本分配错误,将导致更多样本的错误分配,产生“多米诺骨牌效应”。针对这些问题,提出一种新的样本局部密度定义,采用局部标准差指数定义样本局部密度,克服DPC的密度定义缺陷;采用两步分配策略代替DPC的一步分配策略,克服DPC的“多米诺骨牌效应”,得到ESDTS-DPC算法。与DPC及其改进算法KNN-DPC、FKNN-DPC、DPC-CE和经典密度聚类算法DBSCAN的实验比较显示,提出的ESDTS-DPC算法具有更好的聚类准确性。展开更多
基金This research was supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT and Future Planning(NRF-2019R1A2C1084308).
文摘American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.
基金supported by Ulsan Metropolitan City-ETRI joint cooperation project[21AS1600,Development of intelligent technology for key industriesautonomous human-mobile-space autonomous collaboration intelligence technology].
文摘In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,etc.At the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks.This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless networks.The proposed IM-EECNL technique involves two major processes namely node localization and clustering.Firstly,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the nodes.Secondly,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the network.Besides,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and load.The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.
基金This research work was funded by Institutional Fund Projects under grant no.(IFPIP:624-611-1443)。
文摘In the Internet of Things(IoT)based system,the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems(UCS).The UCS necessitates heterogeneity,management level,and data transmission for distributed users.Simultaneously,security remains a major issue in the IoT-driven UCS.Besides,energy-limited IoT devices need an effective clustering strategy for optimal energy utilization.The recent developments of explainable artificial intelligence(XAI)concepts can be employed to effectively design intrusion detection systems(IDS)for accomplishing security in UCS.In this view,this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System(BXAI-IDCUCS)model.The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment.The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization(EADSO)algorithm to accomplish this.Besides,deep neural network(DNN)is employed for detecting and classifying intrusions in the IoT network.Lastly,blockchain technology is exploited for secure inter-cluster data transmission processes.To ensure the productive performance of the BXAI-IDCUCS model,a comprehensive experimentation study is applied,and the outcomes are assessed under different aspects.The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%,a packet loss rate of 0.71%,a throughput of 92.95 Mbps,energy consumption of 0.0891 mJ,a lifetime of 3529 rounds,and accuracy of 99.38%.
文摘The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,flexibility,and seamless services.Software defined networking(SDN)technology plays a critical role in network administration in the future generation of VANET withfifth generation(5G)networks.Regardless of the benefits of VANET,energy economy and traffic control are significant architectural challenges.Accurate and real-time trafficflow prediction(TFP)becomes critical for managing traffic effectively in the VANET.SDN controllers are a critical issue in VANET,which has garnered much interest in recent years.With this objective,this study develops the SDNTFP-C technique,a revolutionary SDN controller-based real-time trafficflow forecasting technique for clustered VANETs.The proposed SDNTFP-C technique combines the SDN controller’s scalability,flexibility,and adaptability with deep learning(DL)mod-els.Additionally,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a VANET.The TFP procedure is then performed using a hybrid convolutional neural network model with atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.
文摘Food security and sustainable development is making a mandatory move in the entire human race.The attainment of this goal requires man to strive for a highly advanced state in thefield of agriculture so that he can produce crops with a minimum amount of water and fertilizer.Even though our agricultural methodol-ogies have undergone a series of metamorphoses in the process of a present smart-agricultural system,a long way is ahead to attain a system that is precise and accurate for the optimum yield and profitability.Towards such a futuristic method of cultivation,this paper proposes a novel method for monitoring the efficientflow of a small quantity of water through the conventional irrigation system in cultiva-tion using Clustered Wireless Sensor Networks(CWSN).The performance measure is simulated the creation of edge-fixed geodetic clusters using Mat lab’s Cup-carbon tool in order to evaluate the suggested irrigation process model’s performance.Thefindings of blocks 1 and 2 are assessed.Each signal takes just a little amount of energy to communicate,according to the performance.It is feasible to save energy while maintaining uninterrupted communication between nodes and cluster chiefs.However,the need for proper placement of a dynamic control station in WSN still exists for maintaining connectivity and for improving the lifetime fault tolerance of WSN.Based on the minimum edgefixed geodetic sets of the connected graph,this paper offers an innovative method for optimizing the placement of control stations.The edge-fixed geodetic cluster makes the network fast,efficient and reliable.Moreover,it also solves routing and congestion problems.
文摘密度峰值聚类(clustering by fast search and find of density peaks,DPC)算法是一种基于密度的聚类算法,它可以发现任意形状和维度的类簇,是具有里程碑意义的聚类算法。然而,DPC算法的样本局部密度定义不适用于同时发现数据集的稠密簇和稀疏簇;此外,DPC算法的一步分配策略使得一旦有一个样本分配错误,将导致更多样本的错误分配,产生“多米诺骨牌效应”。针对这些问题,提出一种新的样本局部密度定义,采用局部标准差指数定义样本局部密度,克服DPC的密度定义缺陷;采用两步分配策略代替DPC的一步分配策略,克服DPC的“多米诺骨牌效应”,得到ESDTS-DPC算法。与DPC及其改进算法KNN-DPC、FKNN-DPC、DPC-CE和经典密度聚类算法DBSCAN的实验比较显示,提出的ESDTS-DPC算法具有更好的聚类准确性。
