79results about How to "Automated Extraction" patented technology

Embodiments of the present invention are directed to quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.

Embodiments of the present invention are directed to quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.

The invention discloses a concept relation label drawing method based on a semantic co-occurrence model. The concept relation label drawing method includes obtaining co-occurrence context phrase of a concept relation pair through inverted searching with offset in a relation label drawing process based on the semantic co-occurrence model; obtaining a candidate relation label by combining shallow parsing and weighting word window filtering; and choosing a cluster label largest in weight after semantic relativity cluster is performed to serve as a concept relation label. By means of the concept relation label drawing method based on the semantic co-occurrence model, accuracy rate and recalling rate of concept relation drawing are improved, automatic drawing of few and scattered semantic relation concept relation labels among large number of concepts is achieved, and quality of concept label drawing is improved.

The invention discloses an automatic processing method, apparatus and device for web interface elements, and a medium, and relates to the technical field of financial. The automatic processing methodfor the web interface elements comprises the following steps: when a web interface detects an input completion instruction of a URL address, obtaining a full page corresponding to the URL address; analyzing the htlm text in the full page through a third-party tool of preset communication, and traversing all page elements corresponding to the htlm text so as to screen target elements from all the page elements according to preset configuration information; and automatically naming, positioning, storing and processing the target element. The automatic processing method for the web interface elements solves the technical problem that in the prior art, web interface elements are processed in a recording and playback mode, and manpower and material resources are consumed too much.

The embodiment of the invention provides an electronic medical record structured method and system and related equipment, which are used for improving medical record information extraction efficiency.The method comprises the steps: obtaining a target electronic medical record text, and performing word segmentation on the target electronic medical record text according to a preset corpus library and a preset word segmentation rule to form a word segmentation set; matching preset keywords with segmented words in the segmented word set, and determining a data element corresponding to a successfully matched target keyword as a target type data element, each keyword at least corresponding to one type of data element; extracting a data element value of the target type data element from the target electronic medical record text according to an extraction rule of the target type data element; and associatively storing all target type data elements of the target electronic medical record text.

The embodiment of the invention provides a webpage information extraction method and device. The webpage information extraction method comprises the steps of determining candidate elements and text information of a webpage according to a DOM tree of the webpage; determining candidate position information of the webpage according to the display page screenshot of the webpage; determining a first probability that each candidate element serves as a target extraction element and a second probability that each candidate position serves as a target extraction position according to the candidate position information and the text information of each candidate element; determining a target extraction element and a target extraction position from each candidate element and each candidate position according to the first probability and the second probability; and performing information extraction on the webpage according to the candidate elements determined to be the target extraction elements and the candidate positions determined to be the target extraction positions. The text information and the candidate position information of the candidate elements of the webpage are extracted, and theneural network model and the spatial probability distribution model are used for positioning the extraction position, so that the positioning precision and the fault-tolerant rate of webpage information extraction are improved, and automatic extraction of webpage information is realized.

The invention relates to the technical field of artificial intelligence and data processing. The invention provides a video analysis method and a device based on artificial intelligence and a medium.According to the method, an original video can be split to obtain video segments; the dining condition of each table is judged in a targeted manner; feature extraction on each video segment is carriedout by utilizing the model; automatic feature extraction is realized by combining an artificial intelligence means; analysis of features is carried out, analysis data is obtained, connection with a specified sensor is adopted, calling of target data of each video segment collected by the specified sensor is carried out, and calculation is carried out according to the analysis data and the targetdata to obtain a video analysis result, so that comprehensive and targeted analysis can be performed on a video based on an artificial intelligence means, and the analysis efficiency is higher. The invention also relates to the blockchain technology. A plurality of pre-trained models are stored in the blockchain.

The invention discloses a mobile application APK (Android Package) file embedded privacy policy extraction method and belongs to the field of Android mobile terminal application software analysis and detection. The method specifically comprises the following steps of firstly, selecting a to-be-detected APK file for decompiling and rule matching, obtaining all URL (Uniform Resource Locator) links, respectively crawling each webpage content, and extracting feature words in a privacy policy text; meanwhile, collecting feature words of a plurality of webpages to train a dichotomy model in advance; inputting feature words of the to-be-detected APK file into the trained dichotomy model one by one, judging whether a privacy policy page exists in an output result or not, and if yes, outputting a privacy policy and ending; otherwise, performing automatic dynamic testing, monitoring request addresses in the traffic, extracting corresponding URL links, crawling contents of pages to extract feature words, inputting the feature words into a dichotomy model for judgment, and ending till a privacy policy page is found or a set traversal depth is exceeded. According to the method, dynamic and static tests are combined, so extraction efficiency and the success rate of privacy policies are improved.

The embodiment of the invention provides a Sea ice extraction method and device based on superpixel segmentation, and equipment. The method comprises the following steps: carrying out superpixel segmentation on a non-land part of an original remote sensing image by adopting an SLIC superpixel segmentation algorithm; according to the super-pixel segmentation result, based on the gray level co-occurrence matrix, calculating the mean value of the contrast and entropy of each super-pixel, and generating a feature image based on the super-pixels; and calculating a contrast threshold and an entropythreshold by adopting an Otsu threshold detection method, classifying the seawater area and the sea ice area, and extracting the sea ice area. In this way, automatic extraction of the sea ice can be achieved, a reference basis is provided for marine activity extraction in the industries of marine shipping, marine oil exploitation and the like, the sea ice serves as an important participant of global climate change, and sea ice extraction further provides technical support for climate change research.

The invention discloses a Pakistani land cover information extraction method based on multi-source spatial data. The method mainly comprises the following two steps of: 1, multi-source spatial data collection and processing: firstly, collecting multi-source spatial data such as optics, SAR (Synthetic Aperture Radar), light and digital elevation covering the Pakistani state, secondly, carrying outrelevant processing such as seasonal synthesis and geographic reference unification, and finally, superposing and merging the multi-source spatial data into a comprehensive data set; and 2, land coverinformation extraction and mapping: firstly formulating a Pakistani land cover classification system, secondly collecting training data and test data, constructing a land cover classifier again, andfinally automatically extracting the land cover information. According to the method provided by the invention, respective advantages of various spatial data can be well and comprehensively utilized to realize automatic extraction of the Pakistani land cover information, and the method has very high operability and practicability.

The invention discloses a voice personality prediction method based on the convolutional neural network, comprising the following steps: S1, establishing a training sample set, including: converting voice data files into a spectrogram, performing big five personality questionnaire tests on acquisition subjects, calculating average value and standard deviation of personality dimension scores, and marking the personality dimension scores which meet the preset requirements; S2, training a convolutional neural network model, including: training the neural network model by the BP algorithm, calculating network errors and updating weights during iteration, training until accuracy of a validation is no longer increased and then stopping training, and saving neural network parameters as the neuralnetwork model; and S3, performing personality prediction on voice data to be tested by the utilization of the convolutional neural network model. Through the technical scheme, the disadvantage that atraditional method requires strong prior knowledge to assist extraction of features with distinguishing degree is overcome, and automatic extraction of phonetic features is realized.

The invention discloses a concrete structure surface microcrack feature extraction method based on a convolutional neural network, and belongs to the field of concrete structure surface damage feature extraction. The method includes following steps of: establishing an image classification data set containing microcracks and backgrounds on the surface of the concrete structure; training and verifying a convolutional neural network used for concrete structure surface microcrack area identification; testing the trained and verified convolutional neural network; performing, by adopting a trained and verified convolutional neural network, microcrack area identification in the concrete structure surface image; skeletonizing the identified micro-crack area in the image; and extracting the characteristics of the microcracks according to the microcrack region skeleton. Compared with a traditional image processing method, the method has better robustness and generalization ability. Compared with a concrete structure crack feature extraction method based on a deep learning semantic segmentation algorithm, the method has the advantages of being simpler in data set manufacturing, smaller in calculation amount and higher in precision. The precision and efficiency of concrete structure surface microcrack feature extraction are improved.