2206results about How to "Accurate classification" patented technology

A display device includes: a data acquisition section which acquires plural data with which dates and/or times are associated; and a date and/or time acquisition section which acquires dates and/or times associated with the plurality of data. The display device further includes: a data classification section which classifies the plural data to plural groups which belong to plural time regions which do not overlap with each other, based on the length of intervals between the dates and/or times acquired by the date and/or time acquisition section; and a display section which classifies plural icons which show the plural data to the groups and displays the plural icons.

To provide a system for processing program information in which the display structure of a program table can be freely changed in accordance with a requirement of or the taste of an audience, the system for processing program information is structured to include program information storage means 1 for storing program information, and program table display means 4 for two-dimensionally displaying a program table employing as the two axes, two attributes 102 and 103 selected from among all of the attributes that have been given to each piece of program information, to make the program table 101. For the audience, program tables of various constructions can be displayed on the display means at will and in respective program retrieval operations, a program table that is easy to use to search for a program can be obtained. An specific desired program can be found in a short time by selecting key word(s) related to each program and extracting them from the program or the like. This is useful for an audience member to make a plan of desired broadcasts for himself.

The invention relates to the field of computer vision recognition and transfer learning, and provides a small sample and zero sample image classification method based on metric learning and meta-learning, which comprises the following steps of: constructing a training data set and a target task data set; selecting a support set and a test set from the training data set; respectively inputting samples of the test set and the support set into a feature extraction network to obtain feature vectors; sequentially inputting the feature vectors of the test set and the support set into a feature attention module and a distance measurement module, calculating the category similarity of the test set sample and the support set sample, and updating the parameters of each module by utilizing a loss function; repeating the above steps until the parameters of the networks of the modules converge, and completing the training of the modules; and enabling the to-be-tested picture and the training picture in the target task data set to sequentially pass through a feature extraction network, a feature attention module and a distance measurement module, and outputting a category label with the highestcategory similarity with the test set to obtain a classification result of the to-be-tested picture.

A computer automated method that applies supervised pattern recognition to classify whether voxels in a medical image data set correspond to a tissue type of interest is described. The method comprises a user identifying examples of voxels which correspond to the tissue type of interest and examples of voxels which do not. Characterizing parameters, such as voxel value, local averages and local standard deviations of voxel value are then computed for the identified example voxels. From these characterizing parameters, one or more distinguishing parameters are identified. The distinguishing parameter are those parameters having values which depend on whether or not the voxel with which they are associated corresponds to the tissue type of interest. The distinguishing parameters are then computed for other voxels in the medical image data set, and these voxels are classified on the basis of the value of their distinguishing parameters. The approach allows tissue types which differ only slightly to be distinguished according to a user's wishes.

The invention discloses a method and a system for identifying moving objects. The method for identifying the moving objects comprises the following steps: detecting the objects, namely extracting interesting areas from a video image; tracking the objects, namely establishing the association and consistency between the extracted areas between the frames to form the objects; and classifying the objects, namely classifying the areas and the objects by a classification method, wherein the classification method comprises an area classification method and an object classification method. The method and the system realize detection, tracking and classification of the moving objects, and solve the problems of image jitter, brightness change, shadow, leaf swing and the like.

The invention provides methods and devices for detecting, identifying, classifying and characterizing particles in a fluid sample. Optical analyzers are provided having a rotating and/or translating sample container for measuring the concentrations of fluorescent particles present in very low concentrations and for characterizing fluorescent particles on the basis of size, shape, diffusion constant and/or composition. Scanning optical analyzers are provided using pattern recognitions data analysis techniques and multichannel detection.

The invention discloses a semi-supervised image classification method based on a generative adversarial network, which mainly solves the problems that the existing unsupervised learning classificationprecision is low and semi-supervised learning needs a large number of accurate labels, and comprises the following implementation steps of: 1) selecting and downloading a standard image training sample and a test sample; 2) setting relevant parameters of network supervised learning, and establishing a generative adversarial network consisting of a generator network, a discriminator network and anauxiliary classifier in parallel; 3) training the generative adversarial network by using a random gradient descent method; and 4) inputting the test sample to be classified into the trained generative adversarial network model, and outputting the category of the image to be detected. The method improves the image classification precision of unsupervised learning, can obtain a very good image classification effect on a sample set only containing a small amount of accurate annotation samples, and can be used for target classification in an actual scene.

A system and method employs a categorized list of business names to identify a category corresponding to transaction information, such as credit card transaction data.

An approach for improving the chances of survival of an individual who has received a trauma including, for example, hemorrhage or blunt injury, by providing more relevant information regarding the individual to first responders including at least one of heart rate variability index value, a baroreflex sensitivity value, and a pulse pressure. This information being used in at least one implementation to provide medical treatment to injured individuals including dispatching assistance and/or prioritizing in a triage situation increasing the speed at which these decisions can be made. In one exemplary embodiment, the heart rate variability index value is determined based on the relative power of the high frequencies versus the relative power of the low frequencies. In one exemplary embodiment, the pulse pressure is determined based on the difference between systolic pressure and diastolic pressure.

The invention discloses a log management and fault diagnosis method and device under multiple hosts. The method comprises fault log collection, fault log analysis and fault log correlation analysis. Fault log collection comprises collecting fault logs of all hardware and software in the cluster and storing the logs in a log server uniformly. Fault log analysis comprises filtering the fault logs, extracting log template information and classifying the logs according to log types. Fault log correlation analysis comprises performing fault reason analysis by using log analysis results and combining time windows, clustering the related faults caused by the same fault into a category and trying to find the root of the type of the faults. The method and the device are capable of analyzing the system operation condition effectively, assisting administrators to process system faults and improving the fault type determination accuracy.

The present invention relates to a method for the real-time identification of seizures in an Electroencephalogram (EEG) signal. The method provides for patient-independent seizure identification by use of a multi-patient trained generic Support Vector Machine (SVM) classifier. The SVM classifier is operates on a large feature vector combining features from a wide variety of signal processing and analysis techniques. The method operates sufficiently accurately to be suitable for use in a clinical environment. The method may also be combined with additional classifiers, such a Gaussian Mixture Model (GMM) classifier, for improved robustness, and one or more dynamic classifiers such as an SVM using sequential kernels for improved temporal analysis of the EEG signal.

The invention discloses a method for quick vehicle type recognition under video monitoring mode, which is implemented according to the following steps: setting up a road monitoring device and classifying the vehicles into cars, taxis marked by special color, microbuses, middle-sized vehicles, public buses and large trucks; step 1, initialization, conducting training to the video monitoring device; step2, extracting the area of the vehicle target area and the length and the width of the boundary rectangle of the area, constructing corresponding characteristics and roughly classifying the vehicles into minitype vehicles, middle-sized vehicles and large vehicles; step 3, extracting the main colors of bodyworks of a plurality of minitype vehicle targets to recognize the taxis, then extracting the characteristic parameter of window position of the minitype vehicles, and furthermore, determining the microbuses or cars; and step 4, extracting the characteristic parameters of roof brightness and roof texture to determine whether the large vehicles are public buses or not. Through the method, a variety of vehicles can be recognized quickly and accurately according to the classification characteristics.

An artificial neural network system implemented on a computer for cell segmentation and classification of biological images. It includes a deep convolutional neural network as a feature extraction network, a first branch network connected to the feature extraction network to perform cell segmentation, and a second branch network connected to the feature extraction network to perform cell classification using the cell segmentation map generated by the first branch network. The feature extraction network is a modified VGG network where each convolutional layer uses multiple kernels of different sizes. The second branch network takes feature maps from two levels of the feature extraction network, and has multiple fully connected layers to independently process multiple cropped patches of the feature maps, the cropped patches being located at a centered and multiple shifted positions relative to the cell being classified; a voting method is used to determine the final cell classification.

The invention discloses a traffic scene multi-target classification method, to be specific, discloses a traffic scene classification method based on a multi-scale convolution neural network. The traffic scene classification method is characterized in that recessive characteristics based on the multi-scale convolution neural network are extracted; and an optimal covering segmentation tree is acquired. During the realizing of the traffic scene classification, the multi-scale convolution neural network is adopted, and the excellent recessive characteristics having the invariance property are effectively extracted from an original image in different scales, and by comparing with the single-scale convolution neural network, the acquisition of the abundant and effective characteristic information of the image is realized. The effective information extracted by the convolution neural network is combined with the original segmentation tree of the image to form an optimal purity price tree, and the covering having the optimal purity is carried out, and therefore a clearer target contour is acquired, and the classification accuracy is increased. The RGB-D is used as the convolution neural network input, and by comparing with the conventional RGB convolution neural network input, the training characteristic is additionally provided with the depth information, and the classification of the input image is more accurate.

A system and method for detecting malware. The system and method is designed to detect malware without the requirement of malware signatures. The process relies upon converting a binary code file to an image. One or more machine learning techniques are used to classify the code as benign or malicious software.

The invention provides a system and methods for the determination of the pharmacogenomic phenotype of any individual or group of individuals, ideally classified to a discrete, specific and defined pharmacogenomic population(s) using machine learning and population structure. Specifically, the invention provides a system that integrates several subsystems, including (1) a system to classify an individual as to pharmacogenomic cohort status using properties of underlying structural elements of the human population based on differences in the variations of specific genes that encode proteins and enzymes involved in the absorption, distribution, metabolism and excretion (ADME) of drugs and xenobiotics, (2) the use of a pre-trained learning machine for classification of a set of electronic health records (EHRs) as to pharmacogenomic phenotype in lieu of genotype data contained in the set of EHRs, (3) a system for prediction of pharmacological risk within an inpatient setting using the system of the invention, (4) a method of drug discovery and development using pattern-matching of previous drugs based on pharmacogenomic phenotype population clusters, and (5) a method to build an optimal pharmacogenomics knowledge base through derivatives of private databases contained in pharmaceutical companies, biotechnology companies and academic research centers without the risk of exposing raw data contained in such databases. Embodiments include pharmacogenomic decision support for an individual patient in an inpatient setting, and optimization of clinical cohorts based on pharmacogenomic phenotype for clinical trials in drug development.

The present invention provides methods, systems, and code for accurately classifying whether a sample from an individual is associated with irritable bowel syndrome (IBS). In particular, the present invention is useful for classifying a sample from an individual as an IBS sample using a statistical algorithm and/or empirical data. The present invention is also useful for ruling out one or more diseases or disorders that present with IBS-like symptoms and ruling in IBS using a combination of statistical algorithms and/or empirical data. Thus, the present invention provides an accurate diagnostic prediction of IBS and prognostic information useful for guiding treatment decisions.

There is provided a method for classifying and counting leukocytes with abnormal DNA amount, which comprises: (1) a step of staining cells in a sample obtained from a hematological sample by treatment with a hemolytic agent to lyse erythrocytes, with a fluorescent dye which can make a difference in the fluorescence intensity at least among mature leukocytes, leukocytes with abnormal DNA amount and immature leukocytes; (2) a step of introducing the sample containing the stained cells into a flow cytometer to measure scattered light and fluorescence of the respective cells; (3) a step of classifying leukocytes and coincidence cells/platelet clumps utilizing a difference in the intensity of a scattered light peak and a difference in the scattered light width; (4) a step of classifying and counting mature leukocytes, leukocytes with abnormal DNA amount and immature leukocytes, utilizing a difference in the scattered light intensity and a difference in the fluorescence intensity of leukocytes classified in the step (3).

The invention discloses audio signal processing equipment and an audio signal processing method as well as electronic equipment. The audio signal processing equipment comprises a microphone array, an audio localization device, a camera, an image localization device and a sound source classifier, wherein the microphone array comprises a plurality of directional microphones having different sound pickup areas; the audio localization device is used for identifying a first group of sound sources and for determining position of each sound source in an audio coordinate system; the camera is used for capturing scene images of a current scene, wherein the current scene at least covers the sound pickup areas of the plurality of directional microphones; the image localization device is used for identifying a second group of sound sources and for determining position of each sound source in an image coordinate system; and the sound source classifier is used for classifying each sound source in the first and second groups of sound sources in accordance with a registration relation between audio and the image coordinate system, the position of each sound source in the audio coordinate system as well as the position of each sound source in the image coordinate system. Therefore, the precise classification of the sound sources can be achieved on the basis of double localization of the directional microphones and the camera.