Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.

EVALUATION OF THE GEOLOGICAL RELIABILITY OF SURFACE MINING DEPOSIT

The optimal deeision theory and exploitation reliability of large scale mining area have been studied China since the late 1980’s. Researches have been earried out under the thought of mannature - machine system , and eneouraging results have been obtained. The reliability evaluation of mining engineering ineludes the rellability exaluation of natural resources, mining technology and mine economics ete. And the geological reliability of deposit, as a natural condition, is the basis of all other evaiuation works. The paper summarizes the method for evaluating the geological reliaoility of deposit, diseusces the reliability of estimating methods for coal reserves and coai quality, evaiuates the natural resourees rellability of the Pit 1 of Antaibao Surface Coai Mine, Pingsuo, Shanxi Province.which was taken as a case study.

Discriminant Analysis of the Linear Separable Data - Japanese 44 Cars

There are four serious problems in the discriminant analysis. We developed an optimal linear discriminant function （optimal LDF） based on the minimum number of misclassification （minimum NM） using integer programming （IP）. We call this LDF as Revised IP-OLDF. Only this LDF can discriminate the cases on the discriminant hyperplane （Probleml）. This LDF and a hard-margin SVM （H-SVM） can discriminate the lineary separable data （LSD） exactly. Another LDFs may not discriminate the LSD theoretically （Problem2）. When Revised IP-OLDF discriminate the Swiss banknote data with six variables, we find MNM of two-variables model such as （X4, X6） is zero. Because MNMk decreases monotounusly （MNMk 〉= MNM（k＋1））, sixteen MNMs including （X4, X6） are zero. Until now, because there is no research of the LSD, we surveyed another three linear separable data sets such as： 18 exam scores data sets, the Japanese 44 cars data and six microarray datasets. When we discriminate the exam scores with MNM=0, we find the generalized inverse matrix technique causes the serious Problem3 and confirmed this fact by the cars data. At last, we claim the discriminant analysis is not the inferential statistics because there is no standard errors （SEs） of error rates and discriminant coefficients （Problem4）. Therefore, we poroposed the ＂100-fold cross validation for the small sample＂ method （the method）. By this break-through, we can choose the best model having minimum mean of error rate （M2） in the validation sample and obtaine two 95% confidence intervals （CIs） of error rate and discriminant coefficients. When we discriminate the exam scores by this new method, we obtaine the surprising results seven LDFs except for Fisher＇s LDF are almost the same as the trivial LDFs. In this research, we discriminate the Japanese 44 cars data because we can discuss four problems. There are six independent variables to discriminate 29 regular cars and 15 small cars. This data is linear separable by the emission rate （X1） and the number of seats （X3）. We examine the validity of the new model selection procedure of the discriminant analysis. We proposed the model with minimum mean of error rates （M2） in the validation samples is the best model. We had examined this procedure by the exam scores, and we obtain good results. Moreover, the 95% CI of eight LDFs offers us real perception of the discriminant theory. However, the exam scores are different from the ordinal data. Therefore, we apply our theory and procedure to the Japanese 44 cars data and confirmed the same conclution.

Model-averaging-based semiparametric modeling for conditional quantile prediction

In real data analysis,the underlying model is frequently unknown.Hence,the modeling strategy plays a key role in the success of data analysis.Inspired by the idea of model averaging,we propose a novel semiparametric modeling strategy for the conditional quantile prediction,without assuming that the underlying model is any specific parametric or semiparametric model.Due to the optimality of the weights selected by leaveone-out cross-validation,the proposed modeling strategy provides a more precise prediction than those based on some commonly used semiparametric models such as the varying coefficient and additive models.Asymptotic properties are established in the proposed modeling strategy along with its estimation procedure.We conducted extensive simulations to compare our method with alternatives across various scenarios.The results show that our method provides more accurate predictions.Finally,we applied our approach to the Boston housing data,yielding more precise quantile predictions of house prices compared with commonly used methods,and thus offering a clearer picture of the Boston housing market.

Robust Error Density Estimation in Ultrahigh Dimensional Sparse Linear Model

This paper focuses on error density estimation in ultrahigh dimensional sparse linear model,where the error term may have a heavy-tailed distribution.First,an improved two-stage refitted crossvalidation method combined with some robust variable screening procedures such as RRCS and variable selection methods such as LAD-SCAD is used to obtain the submodel,and then the residual-based kernel density method is applied to estimate the error density through LAD regression.Under given conditions,the large sample properties of the estimator are also established.Especially,we explicitly give the relationship between the sparsity and the convergence rate of the kernel density estimator.The simulation results show that the proposed error density estimator has a good performance.A real data example is presented to illustrate our methods.

Predicting microRNA-disease association based on microRNA structural and functional similarity network

Background:Increasing evidences indicate that microRNAs (miRNAs) are functionally related to the development and progression of various human diseases.Inferring disease-related miRNAs can be helpful in promoting disease biomarker detection for the treatment,diagnosis,and prevention of complex diseases.Methods:To improve the prediction accuracy of miRNA-disease association and capture more potential diseaserelated miRNAs,we constructed a precise miRNA global similarity network (MSFSN) via calculating the miRNA similarity based on secondary structures,families,and functions.Results:We tested the network on the classical algorithms:WBSMDA and RWRMDA through the method of leaveone- out cross-validation.Eventually,AUCs of 0.8212 and 0.9657 are obtained,respectively.Also,the proposed MSFSN is applied to three cancers for breast neoplasms,hepatocellular carcinoma,and prostate neoplasms.Consequently,82%,76%,and 82% of the top 50 potential miRNAs for these diseases are respectively validated by the miRNA-disease associations database miR2Disease and oncomiRDB.Conclusion:Therefore,MSFSN provides a novel miRNA similarity network combining precise function network with global structure network of miRNAs to predict the associations between miRNAs and diseases in various models.