706 results about "Feature engineering" patented technology

The present disclosure is directed towards systems and methods for detecting anomalous network traffic. Network traffic corresponding to an application executed by a server can be received. Application characteristics of the application can be identified to select an anomaly detection profile. The anomaly detection profile can be selected based on the identified application characteristics. The anomaly detection profile can include a set of detection features for the anomaly and one or more predetermined threshold values of the detection features. One or more feature values of the set of one or more detection features can be determined. An anomaly in the network traffic can be detected responsive to comparing the feature values and the predetermined threshold values of the detection features.

The invention proposes an extraction method of an entity relationship of an electronic medical record, based on a BLSTM and an attention mechanism. The method comprises the following steps: mapping anatural sentence of the electronic medical record into basic feature vector through a word2vec toolkit, coding the basic feature vector into an upper layer feature vector through the BLSTM, then capturing an important text content representing the entity relationship through the attention mechanism based on word and sentence levels, so as to form a higher level of feature vector, finally inputtingthe obtained feature vector into a softmax classifier, and extracting the entity relationship among all entity pairs from the sentence. In addition, the method does not depend on any knowledge base and terminological dictionary to produce the basic feature, reduces the dependence on artificial feature engineering by a model, and provides a technological approach for automatically learning electronic medical record information.

The invention relates to a knowledge graph construction method based on deep learning. The method comprises the steps that a target text statement is given, and a two-way long short term memory recurrent neural network model and a conditional random field model are used to recognize target entities in the target text statement; a context sensitive two-way long short term memory recurrent neural network model and a feedforward neural network model are used to extract the relation between every two target entities; a vector space model is used to normalize the target entities, and the normalized target entities are mapped to a concept; and a knowledge graph is constructed according to the target entities, the relation between the target entities and the concept. According to the method, the deep learning technology is applied to construction of the knowledge graph, entity recognition models of a two-way recurrent neural network and a conditional random field are adopted to recognize the target entities in the target text statement, feature engineering in the entity recognition process and the relation extraction process is reduced, the burden and trouble brought by manual design and feature adjustment are relieved, and knowledge in a text is mined precisely.

The present invention discloses a product quality control method based on integration learning. For prediction of product key quality indexes (yield) in different progresses in a production flow, themethod comprises the following steps of: (1) data analysis based on injection molding data; (2) feature engineering analysis and construction; (3) model design based on integration learning; (4) unbalance processing of data; and (5) multi-model fusion processing scheme. For recommendation of the optimal preset value of process parameters in the production process, the method comprises the following steps of: (6) whole process adjustable parameter recommendation; and (7) for un-adjustable parameters of a special process, recommendation of process adjustable parameters. The product quality control method is suitable for the processing unbalance data in the industrial data to break through a traditional product quality control single parameter analysis mode, and constructs and excavates internal feature relations between parameters through the feature engineering of the machine learning to discover the abnormality of the production process and improve the product quality control.