Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant fina...Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies.In this study,the convoluted situation of scholarship eligibility criteria,including parental income,responsibilities,and academic achievements,is addressed.In an attempt to maximize the scholarship selection process,numerous machine learning algorithms,including Support Vector Machines,Neural Networks,K-Nearest Neighbors,and the C4.5 algorithm,were applied.The C4.5 algorithm,owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors,was capable of predicting a phenomenal 95.62%of predictions using extensive data of a well-esteemed government sector university from Pakistan.This percentage is 4%and 15%better than the remainder of the methods tested,and it depicts the extent of the potential for the technique to enhance the scholarship selection process.The Decision Support Systems(DSS)would not only save the administrative cost but would also create a fair and transparent process in place.In a world where accessibility to education is the key,this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect.展开更多
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
文摘Education is the base of the survival and growth of any state,but due to resource scarcity,students,particularly at the university level,are forced into a difficult situation.Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies.In this study,the convoluted situation of scholarship eligibility criteria,including parental income,responsibilities,and academic achievements,is addressed.In an attempt to maximize the scholarship selection process,numerous machine learning algorithms,including Support Vector Machines,Neural Networks,K-Nearest Neighbors,and the C4.5 algorithm,were applied.The C4.5 algorithm,owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors,was capable of predicting a phenomenal 95.62%of predictions using extensive data of a well-esteemed government sector university from Pakistan.This percentage is 4%and 15%better than the remainder of the methods tested,and it depicts the extent of the potential for the technique to enhance the scholarship selection process.The Decision Support Systems(DSS)would not only save the administrative cost but would also create a fair and transparent process in place.In a world where accessibility to education is the key,this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect.
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.
