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 this work,a system for recognition of newspaper printed in Gurumukhi script is presented.Four feature extraction techniques,namely,zoning features,diagonal features,parabola curve fitting based features,and power c...In this work,a system for recognition of newspaper printed in Gurumukhi script is presented.Four feature extraction techniques,namely,zoning features,diagonal features,parabola curve fitting based features,and power curve fitting based features are considered for extracting the statistical properties of the characters printed in the newspaper.Different combinations of these features are also applied to improve the recognition accuracy.For recognition,four classification techniques,namely,k-NN,linear-SVM,decision tree,and random forest are used.A database for the experiments is collected from three major Gurumukhi script newspapers which are Ajit,Jagbani and Punjabi Tribune.Using 5-fold cross validation and random forest classifier,a recognition accuracy of 96.19%with a combination of zoning features,diagonal features and parabola curve fitting based features has been reported.A recognition accuracy of 95.21%with a partitioning strategy of data set(70%data as training data and remaining 30%data as testing data)has been achieved.展开更多
Diabetes mellitus has become one of the most common chronic diseases,thereby posing a major challenge to global health.Characterized by high levels of blood glucose(hyperglycemia),diabetes usually results from a loss ...Diabetes mellitus has become one of the most common chronic diseases,thereby posing a major challenge to global health.Characterized by high levels of blood glucose(hyperglycemia),diabetes usually results from a loss of insulin-producing β-cells in the pancreas,leading to a deficiency of insulin(type 1 diabetes),or loss of insulin sensitivity(type 2 diabetes).Both types of diabetes have serious secondary complications,such as microvascular abnormalities,cardiovascular dysfunction,and kidney failure.Various complex factors,such as genetic and environmental factors,are associated with the pathophysiology of diabetes.Over the past two decades,the role of small,single-stranded noncoding microRNAs in various metabolic disorders,especially diabetes mellitus and its complications,has gained widespread attention in the scientific community.Discovered first as an endogenous regulator of development in the nematode Caenorhabditis elegans,these small RNAs post-transcriptionally suppress mRNA target expression.In this review,we discuss the potential roles of different microRNAs in diabetes and diabetes-related complications.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 this work,a system for recognition of newspaper printed in Gurumukhi script is presented.Four feature extraction techniques,namely,zoning features,diagonal features,parabola curve fitting based features,and power curve fitting based features are considered for extracting the statistical properties of the characters printed in the newspaper.Different combinations of these features are also applied to improve the recognition accuracy.For recognition,four classification techniques,namely,k-NN,linear-SVM,decision tree,and random forest are used.A database for the experiments is collected from three major Gurumukhi script newspapers which are Ajit,Jagbani and Punjabi Tribune.Using 5-fold cross validation and random forest classifier,a recognition accuracy of 96.19%with a combination of zoning features,diagonal features and parabola curve fitting based features has been reported.A recognition accuracy of 95.21%with a partitioning strategy of data set(70%data as training data and remaining 30%data as testing data)has been achieved.
文摘Diabetes mellitus has become one of the most common chronic diseases,thereby posing a major challenge to global health.Characterized by high levels of blood glucose(hyperglycemia),diabetes usually results from a loss of insulin-producing β-cells in the pancreas,leading to a deficiency of insulin(type 1 diabetes),or loss of insulin sensitivity(type 2 diabetes).Both types of diabetes have serious secondary complications,such as microvascular abnormalities,cardiovascular dysfunction,and kidney failure.Various complex factors,such as genetic and environmental factors,are associated with the pathophysiology of diabetes.Over the past two decades,the role of small,single-stranded noncoding microRNAs in various metabolic disorders,especially diabetes mellitus and its complications,has gained widespread attention in the scientific community.Discovered first as an endogenous regulator of development in the nematode Caenorhabditis elegans,these small RNAs post-transcriptionally suppress mRNA target expression.In this review,we discuss the potential roles of different microRNAs in diabetes and diabetes-related complications.
