In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the...In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.展开更多
The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environme...The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military environments.They make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the area.The data have to be picked up by the sensor,and then sent to the sink node where they may be processed.The nodes of the WSNs are powered by batteries,therefore they eventually run out of power.This energy restriction has an effect on the network life span and environmental sustainability.The objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of energy.The lifespan of WSNs is being extended often using clustering and routing strategies.The Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do clustering.The cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node centrality.Based on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS receives.The overall experimentation is carried out under the MATLAB environment.From the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.展开更多
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493)by the NRF grant funded by the Korea government(MSIT)(NRF-2022R1A2C1004401).
文摘In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.
基金supported via funding from Prince Sattam Bin Abdulaziz University(No.PSAU/2023/R/1444).
文摘The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human beings.The widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military environments.They make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the area.The data have to be picked up by the sensor,and then sent to the sink node where they may be processed.The nodes of the WSNs are powered by batteries,therefore they eventually run out of power.This energy restriction has an effect on the network life span and environmental sustainability.The objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of energy.The lifespan of WSNs is being extended often using clustering and routing strategies.The Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do clustering.The cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node centrality.Based on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS receives.The overall experimentation is carried out under the MATLAB environment.From the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.
