A strategy to significantly improve the classification accuracy of LIBS data:application for the determination of heavy metals in Tegillarca granosa

Tegillarca granosa,as a popular seafood among consumers,is easily susceptible to pollution from heavy metals.Thus,it is essential to develop a rapid detection method for Tegillarca granosa.For this issue,five categories of Tegillarca granosa samples consisting of a healthy group;Zn,Pb,and Cd polluted groups;and a mixed pollution group of all three metals were used to detect heavy metal pollution by combining laser-induced breakdown spectrometry(LIBS)and the newly proposed linear regression classification-sum of rank difference(LRC-SRD)algorithm.As the comparison models,least regression classification(LRC),support vector machine(SVM),and k-nearest neighbor(KNN)and linear discriminant analysis were also utilized.Satisfactory accuracy(0.93)was obtained by LRC-SRD model and which performs better than other models.This demonstrated that LIBS coupled with LRC-SRD is an efficient framework for Tegillarca granosa heavy metal detection and provides an alternative to replace traditional methods.

Classification and reduction manipulation of fractures in Chinese traditional Mongolian osteopathy

OBJECTIVE:To explore the concept of classification and reduction manipulation of fractures in Chinese traditional Mongolian osteopathy.METHODS:Based on the linear classification of fractures in Chinese traditional Mongolian osteopathy and the practice of reduction manipulation,a dynamic classification and reduction manipulation concept of fractures was established with the use of modern biomechanical principles and methods.RESULTS:We classified the linear classification and reduction manipulation of fractures in Chinese traditional Mongolian osteopathy based on the achievement of fracture line and used the cause of the formation of the fracture line for our dynamic classification and reduction manipulation of fractures concept.CONCLUSION:The etiology of the formation of fracture lines can be used to decrease diagnostic error,increase therapeutic effects of manipulation,and further provide a new concept and method for the development of the reduction concept of fractures.

Linearly and Quadratically Separable Classifiers Using Adaptive Approach

This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in R n.In each iteration,a subset of the sampling data （n-points,where n is the number of features） is adaptively chosen and a hyperplane is constructed such that it separates the chosen n-points at a margin and best classifies the remaining points.The classification problem is formulated and the details of the algorithm are presented.Further,the algorithm is extended to solving quadratically separable classification problems.The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.Numerical illustrations show that few iteration steps are sufficient for convergence when classes are linearly separable.For nonlinearly separable data,given a specified maximum number of iteration steps,the algorithm returns the best hyperplane that minimizes the number of misclassified points occurring through these steps.Comparisons with other machine learning algorithms on practical and benchmark datasets are also presented,showing the performance of the proposed algorithm.