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Background: Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis infection. Methods: A total of 220 subjects, including active TB patients (ATB, n = 97) and latent TB patients (LTB, n = 113), were recruited in this study.46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model's predictive performance for dentifying active and latent tuberculosis infection. Results: After verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing set. Conclusions: The machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence has developed rapidly and has been increasingly used in teaching, which has brought about earth-shaking changes in the education of various subjects. To further promote the intelligent development of college online courses, this paper studies the mathematical models and algorithms in the learning management system based on the knowledge forgetting curve. Through the analysis of the current situation of mathematical modeling and application of "knowledge forgetting curve," the method of fuzzy mathematical knowledge and differential modeling is used, the mathematical model and algorithm design of a new "knowledge forgetting curve" is proposed. And the English word review program test is designed. The results show that the use of the program can overcome the oblivion of the words. It can be seen that the "integrated intelligent teaching network platform" has great application prospects and potential benefits. [ABSTRACT FROM AUTHOR]