1497results about How to "Improve predictive performance" patented technology

The present invention relates to a system and methodology to facilitate extraction of information from a large unstructured corpora such as from the World Wide Web and/or other unstructured sources. Information in the form of answers to questions can be automatically composed from such sources via probabilistic models and cost-benefit analyses to guide resource-intensive information-extraction procedures employed by a knowledge-based question answering system. The analyses can leverage predictions of the ultimate quality of answers generated by the system provided by Bayesian or other statistical models. Such predictions, when coupled with a utility model can provide the system with the ability to make decisions about the number of queries issued to a search engine (or engines), given the cost of queries and the expected value of query results in refining an ultimate answer. Given a preference model, information extraction actions can be taken with the highest expected utility. In this manner, the accuracy of answers to questions can be balanced with the cost of information extraction and analysis to compose the answers.

Dynamic management of the health care, nutrition and well-being of pet animal, such as a dogs and cats. Data relating to the species of the animal, a selected group of the species, and the animal, including diagnostic laboratory test data of the animal are related by a computer. A management regimen for the animal is based on this. Updating the data picks up drifts in different populations of the animals, groups and species over time to enhance the management.

The present invention generally relates to the monitoring and controlling of a semiconductor manufacturing environment and, more particularly, to methods and systems for virtual meteorology (VM) applications based on data from multiple tools having heterogeneous relatedness. The methods and systems leverage the natural relationship of the multiple tools and take advantage of the relationship embedded in process variables to improve the prediction performance of the VM predictive wafer quality modeling. The prediction results of the methods and systems can be used as a substitute for or in conjunction with actual metrology samples in order to monitor and control a semiconductor manufacturing environment, and thus reduce delays and costs associated with obtaining actual physical measurements.

A time to event data analysis method and system. The present invention relates to the analysis of data to identify relationships between the input data and one or more conditions. One method of analysing such data is by the use of neural networks which are non-linear statistical data modelling tools, the structure of which may be changed based on information that is passed through the network during a training phase. A known problem that affects neural networks is the issue of overtraining which arises in overcomplex or overspecified systems when the capacity of the network significantly exceeds the needed parameters. The present invention provides a method of analysing data, such as bioinformatics or pathology data, using a neural network with a constrained architecture and providing a continuous output that can be used in various contexts and systems including prediction of time to an event, such as a specified clinical event.

The invention discloses a federal model training method, a federal model training system, federal model training equipment and a computer readable storage medium, and the method comprises the steps: obtaining a weight coefficient corresponding to each model parameter according to a preset obtaining rule after a cooperative terminal receives the model parameters respectively sent by a plurality ofclient terminals; according to the plurality of model parameters and the weight coefficient corresponding to each model parameter, aggregating to obtain a second global model; detecting whether the second global model converges or not; and if it is detected that the second global model is in the convergence state, determining the second global model as a final result of federated model training, and issuing the second global model with encrypted parameters to a plurality of client terminals. According to the invention, the cooperative terminal updates and obtains the new global model accordingto the model parameter and the weight coefficient of each client terminal, and the prediction effect of the federal model is improved.

The invention discloses a neural network-based chat robot dialogue semantic intention prediction method and a learning training method. The prediction method is used for a chat robot to predict a possibly continuous dialogue intention or point of interest (one of multiple dialogue intentions or points of interest) of a dialogist according to a sequence (said) statement eigenvector of the human dialogist; and by heuristically selecting an output dialogue of the chat robot and taking an answer of the dialogist as a verification feedback of a previous intention prediction result, online reinforcement learning and self-updating of an algorithm are realized, the accuracy of dialogue intention prediction is continuously improved, the round number of chat can be increased, and the interest of thedialogist can be enhanced.

The invention relates to a method for optimizing combustion of a boiler. The combustion optimization of the prior boiler mainly depends on debugging stuffs to do experiments, thereby taking time and labor and obtaining limited parameter combinations. The method includes the following steps: collecting working parameters of the boiler and corresponding indexes characterizing the combustion characters of the boiler and building a real-time database; adopting an integrated modeling method supporting a vector machine to carry out modeling under the condition that the real work load is 60 percent smaller than the design load of the boiler and adopting a radial basis function neural network integrated modeling method to carry out modeling under the condition that the real work load is60 percent larger than or equal to the design load of the boiler to build boiler combustion models with different indexes; and utilizing the particle swarm optimization algorithm and combining with the built models to optimize the combustion parameter setting of the boiler according to different combustion indexes or index combinations of the boiler. The invention improves the predictive ability of the integral model, greatly improves the predictive ability of the models, and carries out one-line optimization and off-line optimization.

The present invention discloses an integrated similarity measurement and bi-directional random walk based pharmaceutical relocation method. When calculating pharmaceutical similarity and disease similarity, other than taking advantage of pharmaceutical characteristics information and disease characteristics information respectively, an integrated similarity measurement method further takes full account of effects on similarity measurement due to pharmaceutical-disease related information in a current data set, so that a calculated similarity value can better reflect similarity between pharmaceuticals and similarity between diseases. On this basis, a pharmaceutical-disease heterogeneous network is built, and based on the heterogeneous network, a bi-directional random walk algorithm is taken to predict a candidate disease for all pharmaceuticals. The method disclosed by the present invention is simple and effective, and compared with other methods, tests on multiple data sets prove that the method disclosed by the present invention has better prediction performance in pharmaceutical relocation.

A method of controlling an engine system, the method comprising: receiving data relating to engine operation; calculating in an engine model a combustion parameter and an injection parameter required to operate the engine system in accordance with the received engine data; controlling the engine system based on the calculated injection parameter wherein the method further comprises adjusting the engine model over time based on a comparison between the calculated engine combustion parameter and a corresponding measured engine combustion parameter

The invention relates to a soft sensing method for load parameters of a ball mill. The method is that a hardware supporting platform is used to obtain vibration signals, vibration sound signals and current signals of a ball mill cylinder to soft sense ball mill internal parameters (ratio of material to ball, pulp density and filling ratio) characterizing ball mill load. The method comprises the following steps that: the vibration, the vibration sound, the current data and the time-domain filtering of the ball mill cylinder are acquired, time frequency conversion is conducted to the vibration and the vibration sound data, kernel principal component analysis based nonlinear features of the sub band of the vibration and the vibration sound data in frequency domain are extracted, nonlinear features of the time domain current data are extracted, feature selection is conducted to the fused nonlinear feature data and a soft sensing model based on a least squares support vector machine is established. The soft sensing method of the invention has the advantages that the sensitivity is high, the sensed results are accurate, the practical value and the popularization prospect are very good, and the realization of the stability control, the optimization control, the energy saving and the consumption reduction of the grinding production process is facilitated.

Biomarkers and methods using the biomarkers for the prediction of the recurrence risk of cancer in a patient are provided.

The present invention discloses a short-term load prediction method based on variable selection and Gaussian process regression. The method includes the following steps that: 1) bad data elimination, supplementation and normalization pre-processing are performed on sample data; 2) candidate input variables are selected from the perspectives of historical load, temperature and humidity, and the date type of a prediction date, and the scores of the importance of the variables are calculated through a random forest algorithm, and the scores of the importance of the variables are sequenced; 3) an optimal variable set is determined through adopting a sequence forward search strategy and based on a Gaussian process regression model; 4) the Gaussian process regression model is trained based on the determined optimal variable set, and the parameters of the model are optimized based on improved particle swarm optimization; and 5) the predictive performance of the model is verified in a test set. With the method provided by the invention adopted, prediction accuracy can be effectively improved, and the load prediction problem of a power system can be solved.

Systems and methods are provided for handling time-stamped data. The one or more GUIs may be used to define properties of an event and to associate the event with time-stamped data. The properties of the event defined using the one or more GUIs may include one or more statistical properties that indicate how the event statistically affects the time-stamped data. A time series analysis program may use the time-stamped data and the associated event to generate a mathematical model.

An intelligent dialectics based on the probabilistic reasoning model of Bayes network and computing technology for traditional Chinese medicine to diagnose diseases is disclosed. The signal processing technique and image recognizing technique are used to automatically recognize the pulse condition and the tongue picture, analyze them and provide the result. In its interrogation interface, after the disease signs of a patient are input, the dialectic result can be output. It has also self-learning, storage and print function.

A system, method, and apparatus is provided for computerized management of the databases relating to phenotypic health assessment and genomic mapping and genetic screening of animals. Multiple remote users can access a computer network connected with a central database processing resource for managing the data There is appropriate security and payment of appropriate fees for accessing and retrieving data Users may input data relating to animal health, lifespan, and genetic background, and obtain reports relating to phenotypic health assessment of a particular animal subject or animal group, and genotypic characteristics of a particular subject or animal group to which this subject belongs. The central computer database processing resource stores phenotypic data and genotypic data relating to animals and analyzes their relationship according to predetermined criteria. The input of data and reporting of data is preferably electronic, or through fax or voice communication. The analysis of the health assessment database and genetic database can be automatic and/or in part manually interpreted by experts related to the central database processing resources.

Diffusion weighted images and perfusion weighted images are acquired with an MRI system and used to produce low b, DWI, ADC, CBV, CBF, and MTT images of brain tissues following an ischemic event. These MRI physiological measurements are input along with a spatial location measurement to a generalized linear model that predicts the outcome of tissues surrounding a lesion.

The invention discloses a physical simulation testing device used in graff contaminant transmission under the action of temperature-hydraulic coupling. The testing device comprises a simulation container, an air pressure control device, a temperature control device, a circulating water system and an online control and monitoring system. The testing system can realize physical simulation test of transmission dynamic characteristics of contaminants in rivers, aqueducts, sewage trenches and surrounding soil and ground water, and integrate monitoring, control and data acquisition; moreover, the testing system realizes real-time display and intelligent control of water level, temperature and pressure variations in the testing system, and carries out overall process acquisition, analysis and visualized input and output on testing data through a data acquisition program. The testing system has the characteristics of reasonable structure, convenient operation, high precision of testing data, quick automatic data acquisition, short testing cycle as well as ideal modularization, openness and expansibility, and the like. In addition, the testing system provides parameter basis to disclose the transmission mechanism of contaminants in soil and ground water under complicated environmental conditions and analyze migration and transformation rule, and also provides technical support for appraising pollution control and treatment technical proposals.

The invention discloses a drug repositioning method based on multi-information fusion and a random walk model. According to the method, disease-target-drug heterogeneous network is constructed through integrating existing disease data, drug data, target data, disease-drug associated data, disease-gene associated data and drug-target associated data; the basic random walk model is extended to the constructed heterogeneous network; and candidate therapeutic drugs are recommended for diseases through effectively utilizing global network information. The method disclosed by the invention is simple and effective; and compared with other methods and proved by tests on a standard data set, the method has good prediction performance in the aspect of drug repositioning.

The invention relates to an immune gene prognosis model for predicting hepatocellular carcinoma tumor immune infiltration and postoperative survival time, and belongs to the technical field of biological medicines. The model can be used for evaluating the infiltration degree of immune cells in a tumor in clinical practice by detecting the expression levels of 22 specific immune related genes of ahepatocellular carcinoma patient, so that the model can be used for predicting hepatocellular carcinoma tumor immune infiltration in clinical practice and improve the prediction capability of the liver cancer immunotherapy response. The model can be used for judging the postoperative overall survival risk of a patient and guiding the formulation of a postoperative treatment strategy, and the corresponding microarray chip kit can realize the standardization and convenience of detection. Meanwhile, the immune gene prognosis model provided by the invention can increase the prediction accuracy andthe clinical net income of a hepatocellular carcinoma TNM staging system on the total survival time of three years and five years after operation. As a molecular marker for objectively and accuratelyevaluating the tumor immune state and poor prognosis risk of hepatocellular carcinoma, the model can realize accurate implementation of hepatocellular carcinoma immunotherapy and accurate prognosis prediction.