5 results about "Nonlinear classifier" patented technology

The invention discloses a pulsed electric field ablation system electrode attachment detection method based on a support vector machine, and relates to the technical field of cardiac ablation, and the method comprises the following specific steps: 1, data set collection and preprocessing: recording the current frequency, the impedance amplitude difference and the phase difference between every two adjacent electrodes under different power-on frequencies, step 2, support vector machine SVM training and testing: training the support vector machine SVM through the data set to obtain an SVM nonlinear classifier; and step 3, performing electrode attaching detection. According to the method, the support vector machine SVM is trained through the multiple groups of training set sample data and the corresponding labels, the trained SVM nonlinear classifier is obtained, new data are detected by means of the trained SVM nonlinear classifier, and due to the fact that the SVM has high accuracy for the nonlinear classification problem, the accuracy of the SVM is improved. The contact state of the electrode and the tissue can be accurately judged according to a predicted value output by the trained model, and failure or misjudgment of a model detection result caused by nonlinear change of human tissue impedance is avoided.

The invention provides a method and a system TGrKCMI for predicting interaction between circRNA (Ribonucleic Acid) and miRNA (Micro Ribonucleic Acid). According to the method, a pre-training model is used for extracting sequence features of circRNA and miRNA, and redundant information is reduced through PCA dimension reduction; then, a multi-head attention module with a gating mechanism is introduced to encode sequence features, and the expression ability of key information is enhanced; on the basis, a circRNA-miRNA interaction diagram is constructed, and robust diagram feature learning is realized in combination with diagram attention convolution of random feature masks. And finally, a kernel-based adaptive nonlinear classifier is adopted to carry out modeling on the fusion features, and high-precision and high-robustness interaction prediction is realized.

This application relates to a method and system for risk early warning of geotechnical infrastructure based on a multi-scale damage model. The method includes: acquiring environmental monitoring data of a target geotechnical area based on a preset monitoring scale; transforming the environmental monitoring data into a multi-scale damage model through multi-scale coupling analysis modeling; extracting spatiotemporal features of damage from the multi-scale damage model using a pre-trained convolutional neural network, and determining risk patterns by matching with a preset risk pattern library; inputting the spatiotemporal features of damage and risk patterns into a pre-trained risk diffusion probability prediction model to obtain risk propagation prediction results; constructing a multi-dimensional risk decision boundary using a nonlinear classifier based on the risk patterns and risk propagation prediction results; and generating graded early warning instructions through preset dynamic risk decision rules. This method can achieve accurate and dynamic early warning of risks to geotechnical infrastructure, preventing safety accidents caused by multi-scale damage evolution.

A system (100) for the early detection of polycystic ovary syndrome (PCOS), wherein the system (100) comprises the following: an input unit (102) configured to capture PCOS records of a patient from a data set; a preprocessing unit (104) connected to the input unit (102), which is configured to identify instances with missing values ​​in the data set by eliminating the PCOS data sets with missing data; a classification unit (106) that is operationally connected to the preprocessing unit (104) and is configured to classify instances with missing values, wherein the classification unit (106) comprises a plurality of classifiers that include the following: a variety of linear classifiers (106a), including Gaussian Naive Bayes, Logistic Regression, Support Vector Machine and Linear Discriminant Analysis, configured to separate a variety of classes by constructing linear decision boundaries in the feature space; a variety of nonlinear classifiers (106b), including K-Nearest Neighbors, Support Vector Machine with a polynomial kernel, quadratic discriminant analysis and decision tree, configured to capture complex relationships between features and classes; where output predictions from the multitude of classifiers are chained together to improve accuracy and generalization; a cross-validation unit (108) that is operationally integrated with the classification unit (106) and is configured to perform a 10-fold cross-validation of the classifiers to evaluate the performance of the classifiers in detecting PCOS; a performance evaluation unit (110) connected to the cross-validation unit (108) and configured to calculate evaluation measures including accuracy and F1 score for each classifier and to identify the best-performing classifier based on the evaluation measures; and an output interface (112) which is integrated into a display (112a) and is configured to display the diagnostic results including the classifier with the highest accuracy and the highest F1 score.

The present invention discloses a prediction method, system, device and storage medium for product attributes. The prediction method includes obtaining first image data of a first product to be trained and obtaining a target training set; using a residual network to extract attribute features of the target training set to obtain a first attribute prediction vector and a first feature vector; using a pooling method to perform pooling on the first feature vector to obtain a second feature vector; using a linear classifier and a nonlinear classifier to classify the second feature vector to obtain an intermediate attribute prediction vector, and then establishing a target attribute prediction model for predicting product attributes; obtaining second image data of a target product to be predicted, and predicting the target attribute of the target product through the target attribute prediction model. The present invention overcomes the defects of serious overfitting and high prediction errors in existing prediction methods, improves the prediction accuracy of product attributes, and the entire method has good robustness and practical application capabilities.