Grading of writers in perspective of their handwriting is a challenging task owing to various writing styles of different individuals.This paper presents a framework for grading of Devanagari writers in perspective of...Grading of writers in perspective of their handwriting is a challenging task owing to various writing styles of different individuals.This paper presents a framework for grading of Devanagari writers in perspective of their handwriting.This framework of grading can be useful in conducting the handwriting competitions and then deciding the winners on the basis of an automated process.Selecting the set of features is a challenging task for implementing a handwriting grading system of particular language.In this paper,curvature features,namely,parabola curve fitting and power curve fitting have been considered for extracting the vital information of writers,which can be helpful in grading the writers.For obtaining the classification score,k-NN classifier has been considered in the present work.Four printed Devanagari font styles,namely,Devlys,Krishna,Krutidev,and Utsaah have been considered for train the proposed model of handwriting evaluation.For evaluating the effectiveness of the proposed framework,we have conducted a mock test of 75 Devanagari writers(Left handed and Right handed)and noticed that the proposed framework performing well suitable for conducting the handwriting competition of Devanagari text writers.This work is also newly in the ground of Devanagari text recognition.展开更多
In the age of online workload explosion,cloud users are increasing exponentialy.Therefore,large scale data centers are required in cloud environment that leads to high energy consumption.Hence,optimal resource utiliza...In the age of online workload explosion,cloud users are increasing exponentialy.Therefore,large scale data centers are required in cloud environment that leads to high energy consumption.Hence,optimal resource utilization is essential to improve energy efficiency of cloud data center.Although,most of the existing literature focuses on virtual machine(VM)consolidation for increasing energy efficiency at the cost of service level agreement degradation.In order to improve the existing approaches,load aware three-gear THReshold(LATHR)as well as modified best fit decreasing(MBFD)algorithm is proposed for minimizing total energy consumption while improving the quality of service in terms of SLA.It offers promising results under dynamic workload and variable number of VMs(1-290)allocated on individual host.The outcomes of the proposed work are measured in terms of SLA,energy consumption,instruction energy ratio(IER)and the number of migrations against the varied numbers of VMs.From experimental results it has been concluded that the proposed technique reduced the SLA violations(55%,26%and 39%)and energy consumption(17%,12%and 6%)as compared to median absolute deviation(MAD),inter quartile range(IQR)and double threshold(THR)overload detection policies,respectively.展开更多
文摘Grading of writers in perspective of their handwriting is a challenging task owing to various writing styles of different individuals.This paper presents a framework for grading of Devanagari writers in perspective of their handwriting.This framework of grading can be useful in conducting the handwriting competitions and then deciding the winners on the basis of an automated process.Selecting the set of features is a challenging task for implementing a handwriting grading system of particular language.In this paper,curvature features,namely,parabola curve fitting and power curve fitting have been considered for extracting the vital information of writers,which can be helpful in grading the writers.For obtaining the classification score,k-NN classifier has been considered in the present work.Four printed Devanagari font styles,namely,Devlys,Krishna,Krutidev,and Utsaah have been considered for train the proposed model of handwriting evaluation.For evaluating the effectiveness of the proposed framework,we have conducted a mock test of 75 Devanagari writers(Left handed and Right handed)and noticed that the proposed framework performing well suitable for conducting the handwriting competition of Devanagari text writers.This work is also newly in the ground of Devanagari text recognition.
文摘In the age of online workload explosion,cloud users are increasing exponentialy.Therefore,large scale data centers are required in cloud environment that leads to high energy consumption.Hence,optimal resource utilization is essential to improve energy efficiency of cloud data center.Although,most of the existing literature focuses on virtual machine(VM)consolidation for increasing energy efficiency at the cost of service level agreement degradation.In order to improve the existing approaches,load aware three-gear THReshold(LATHR)as well as modified best fit decreasing(MBFD)algorithm is proposed for minimizing total energy consumption while improving the quality of service in terms of SLA.It offers promising results under dynamic workload and variable number of VMs(1-290)allocated on individual host.The outcomes of the proposed work are measured in terms of SLA,energy consumption,instruction energy ratio(IER)and the number of migrations against the varied numbers of VMs.From experimental results it has been concluded that the proposed technique reduced the SLA violations(55%,26%and 39%)and energy consumption(17%,12%and 6%)as compared to median absolute deviation(MAD),inter quartile range(IQR)and double threshold(THR)overload detection policies,respectively.
