Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the problem.Technique for determining order prefer...Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the problem.Technique for determining order preference by similarity to the ideal solution(TOPSIS)is an established DM process.The objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data,in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices,each of which is defined by Hesitancy fuzzy numbers.Findings:we represent analytical results,such as designing a satellite communication network and assessing reservoir operation methods,to demonstrate that our suggested thoughts may be used in DM.Aim:We studied a new testing method for the arti-ficial communication system to give proof of the future construction of satellite earth stations.We aim to identify the best one from the different testing places.We are alsofinding the best operation schemes in the reservoir.In this article,we present the concepts of Laplacian energy(LE)in Hesitancy fuzzy graphs(HFGs),the weight function of LE of HFGs,and the TOPSIS method technique is used to produce the hesitancy fuzzy weighted-average(HFWA).Also,consider practical examples to illustrate the applicability of thefinest design of satellite communication systems and also evaluation of reservoir schemes.展开更多
Integrated CloudIoT is an emergingfield of study that integrates the Cloud and the Internet of Things(IoT)to make machines smarter and deal with real-world objects in a distributed manner.It collects data from various ...Integrated CloudIoT is an emergingfield of study that integrates the Cloud and the Internet of Things(IoT)to make machines smarter and deal with real-world objects in a distributed manner.It collects data from various devices and analyses it to increase efficiency and productivity.Because Cloud and IoT are complementary technologies with distinct areas of application,integrating them is difficult.This paper identifies various CloudIoT issues and analyzes them to make a relational model.The Interpretive Structural Modeling(ISM)approach establishes the interrelationship among the problems identified.The issues are categorised based on driving and dependent power,and a hierarchical model is presented.The ISM analysis shows that scheduling is an important aspect and has both(driving and dependence)power to improve the performance of the CloudIoT model.Therefore,existing CloudIoT job scheduling algorithms are ana-lysed,and a cloud-centric scheduling mechanism is proposed to execute IoT jobs on a suitable cloud.The cloud implementation using an open-source framework to simulate Cloud Computing(CloudSim),based on the job’s workload,is pre-sented.Simulation results of the proposed scheduling model indicate better per-formance in terms of Average Waiting Time(AWT)and makespan than existing cloud-based scheduling approaches.展开更多
The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its effects.Thus,developing surveillance systems for predict...The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its effects.Thus,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives.In this study,a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus.The Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death cases.The real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely Saudi Arabia,Spain and Italy.The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients.Standard measure performance Mean squared Error(MSE),Root Mean Squared Error(RMSE),Mean error and correlation are employed to estimate the results of the proposed models.The empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three countries.As far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to Saudi Arabia,Italy and Spain respectively.Similarly,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the Saudi Arabia,Italy and Spain respectively.The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries.Such findings provide better insights regarding the future of Covid-19 this pandemic in general.The results were obtained by applying time series models,which need to be considered for the sake of saving the lives of many people.展开更多
文摘Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the problem.Technique for determining order preference by similarity to the ideal solution(TOPSIS)is an established DM process.The objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data,in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices,each of which is defined by Hesitancy fuzzy numbers.Findings:we represent analytical results,such as designing a satellite communication network and assessing reservoir operation methods,to demonstrate that our suggested thoughts may be used in DM.Aim:We studied a new testing method for the arti-ficial communication system to give proof of the future construction of satellite earth stations.We aim to identify the best one from the different testing places.We are alsofinding the best operation schemes in the reservoir.In this article,we present the concepts of Laplacian energy(LE)in Hesitancy fuzzy graphs(HFGs),the weight function of LE of HFGs,and the TOPSIS method technique is used to produce the hesitancy fuzzy weighted-average(HFWA).Also,consider practical examples to illustrate the applicability of thefinest design of satellite communication systems and also evaluation of reservoir schemes.
文摘Integrated CloudIoT is an emergingfield of study that integrates the Cloud and the Internet of Things(IoT)to make machines smarter and deal with real-world objects in a distributed manner.It collects data from various devices and analyses it to increase efficiency and productivity.Because Cloud and IoT are complementary technologies with distinct areas of application,integrating them is difficult.This paper identifies various CloudIoT issues and analyzes them to make a relational model.The Interpretive Structural Modeling(ISM)approach establishes the interrelationship among the problems identified.The issues are categorised based on driving and dependent power,and a hierarchical model is presented.The ISM analysis shows that scheduling is an important aspect and has both(driving and dependence)power to improve the performance of the CloudIoT model.Therefore,existing CloudIoT job scheduling algorithms are ana-lysed,and a cloud-centric scheduling mechanism is proposed to execute IoT jobs on a suitable cloud.The cloud implementation using an open-source framework to simulate Cloud Computing(CloudSim),based on the job’s workload,is pre-sented.Simulation results of the proposed scheduling model indicate better per-formance in terms of Average Waiting Time(AWT)and makespan than existing cloud-based scheduling approaches.
文摘The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its effects.Thus,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives.In this study,a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus.The Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death cases.The real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely Saudi Arabia,Spain and Italy.The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients.Standard measure performance Mean squared Error(MSE),Root Mean Squared Error(RMSE),Mean error and correlation are employed to estimate the results of the proposed models.The empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three countries.As far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to Saudi Arabia,Italy and Spain respectively.Similarly,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the Saudi Arabia,Italy and Spain respectively.The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries.Such findings provide better insights regarding the future of Covid-19 this pandemic in general.The results were obtained by applying time series models,which need to be considered for the sake of saving the lives of many people.
