7results about How to "Accurate classification effect" patented technology

The present invention relates to the field of artificial intelligence technology, and discloses a network security situation awareness method based on artificial intelligence, including obtaining network security data; using a fully connected neural network based on dynamic population evolution optimization to extract features from network security data to obtain a first feature; using an autoencoder neural network based on contrast entropy to reduce the first feature to obtain a second feature; using a fractional-order neural network based on complexity perception to construct a classifier model, and using the second feature to train the classifier model; and using the trained classifier model to classify network security situation awareness. The present invention improves the ability to distinguish complex network security situations, can achieve more accurate classification effects, and enhances the applicability of the model in network security data and the accuracy of classification.

Embodiments of the present application disclose a decision tree model generation method and a data recommendation method based on the decision tree model. The method comprises: obtaining a training data set formed by feature information of a plurality of training samples, the training samples having known category attributes; in a process of generating a decision tree model according to the training data set, iteratively calculating information gain of each feature attribute under each node, and dividing a data set contained by a current node according to a feature attribute corresponding to maximum information gain until a category attribute can be determined according to the data set contained by the node; if information gains of a plurality of feature attributes under the current node are equal and are maximum information gain, then calculating respective correction information gains of the plurality of feature attributes, and determining a feature attribute for dividing the data set contained by the current node according to the calculated correction information gains; and outputting the decision tree model formed according to the training data set. The decision tree model generated by the present application has more accurate classification effect.

The embodiment of the present invention belongs to the field of artificial intelligence, and in particular, relates to a text classification method, device, equipment and storage medium, wherein the method comprises: obtaining a text to be analyzed, segmenting the text to form a segmentation set, and obtaining the subject words of the text according to a subject model to form a subject word set; obtaining a word embedding vector of each word in the subject word set, reducing the dimension of the word embedding vector and mapping it to a plane, and constructing a Voronoi diagram according to the mapping points on the plane; calculating the semantic distance between non-subject words and subject words, and adding the non-subject words to the Voronoi diagram; identifying the word node type of each word in the Voronoi diagram, and calculating the semantic distance between the word nodes by a corresponding algorithm according to the word node type; inputting the semantic distance between the word nodes into a pre-constructed graph convolutional neural network output graph latent vector, and performing text classification according to the graph latent vector. The present invention improves the accuracy of text classification.

The invention discloses an object classification method, device and equipment, a medium and a product. The method comprises the steps of obtaining at least one piece of financial associated information corresponding to a to-be-classified object; for at least one piece of financial association information, determining financial association features corresponding to the financial association information according to the data type corresponding to the financial association information; and processing the at least one financial association feature according to an object classification model obtained by pre-training, and obtaining an object type corresponding to the to-be-classified object. According to the technical scheme, the effect of accurately classifying the category of the object by comprehensively fitting the financial association features based on the neural network model is achieved, and the prediction accuracy of the financial service period stage where the object is located is improved.

The application discloses a bearing fault classification method and system based on an intelligent inspection robot, and belongs to the technical field of sensing technology and digital signal processing technology. The technical problem to be solved by the application is how to use an intelligent inspection robot to classify bearing faults according to sound, and then accurately know the bearing fault position. The technical scheme adopted is as follows: S1, collecting acoustic signals: collecting the fault sound of a bearing inner ring raceway, a bearing outer ring raceway, a bearing rolling element and a bearing assembly based on an intelligent inspection robot's collection card and microphone, and simultaneously collecting the sound information of a normal bearing working; S2, constructing a time-frequency signal: constructing a time-frequency signal to express a data-increased signal feature; S3, feature extraction: extracting features through a self-encoding network; S4, feature classification: classifying features through a convolutional neural network; and S5, optimizing classification: training and testing by using an Adam optimization algorithm, continuously adjusting the parameter settings in the network, and then obtaining more accurate classification results.

Embodiments of the present application disclose a decision tree model generation method and a data recommendation method based on the decision tree model. The method comprises: obtaining a training data set formed by feature information of a plurality of training samples, the training samples having known category attributes; in a process of generating a decision tree model according to the training data set, iteratively calculating information gain of each feature attribute under each node, and dividing a data set contained by a current node according to a feature attribute corresponding to maximum information gain until a category attribute can be determined according to the data set contained by the node; if information gains of a plurality of feature attributes under the current node are equal and are maximum information gain, then calculating respective correction information gains of the plurality of feature attributes, and determining a feature attribute for dividing the data set contained by the current node according to the calculated correction information gains; and outputting the decision tree model formed according to the training data set. The decision tree model generated by the present application has more accurate classification effect.

The invention aims to provide the cross-linked polyethylene cable insulation aging feature extraction and service life evaluation method, which not only can fully solve the problem of power distribution cable defect operation and maintenance in the prior art, but also can prolong the service life cycle of the power distribution cable and further reduce the intelligent degree and the operation and maintenance cost of intelligent routing inspection of the power distribution cable.