27 results about "Generalized linear model" patented technology

Systems, methods, and computer-readable media are provided for determining matches between records of different systems based on aggregate record data, and graphically marking potentially matched groups of data along with predicted confidence levels. Preliminary matching tools may allow allow users to define various rules based on which a majority of the transactions can be matched and reconciled. However, remaining transactions are disposed of in an interactive matching process. The matches may be determined unidirectionally from a source transaction to transactions from a target ledger, or bidirectionally from transactions in the target ledger to transactions other than the source transaction. Transactions may be matched many-to-many, one-to-many, or many-to-one, and a proposed order of match selections may be presented in a user interface. Match metadata or insights may be displayed to show a confidence of the match, reasons for the confidence, and / or a confidence of other matches that may be more beneficial than a match with a source transaction. The confidence and match insights may be generated by a machine learning model with access to transactions from a source transaction ledger and a target transaction ledger. The machine learning model may be trained on manual activity for prior matches that have been made. Matches may be performed using a hybrid machine learning model that accounts for random forests, decision trees, neural networks, naïve bayes algorithm, and / or a generalized linear model. Machine learning models also incorporate ongoing feedback from the users who can either accept or reject suggested matches and hence the models undergo an evolution process and constantly update from user patterns.

This invention relates to apparatus and methods for selecting a soil microbiome having increasing plant productivity. The apparatus and methods relate to combining an output from a machine learning model, a generalized linear model, and a distance-based multivariate model to execute a donor forest selection tool that is configured to select from a plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

The invention provides a time-space geographically weighted regression method and device for counting data, equipment and a storage medium, and relates to the technical field of time-space information data analysis. The method comprises the following steps: acquiring multi-source space-time observation data of a target area; constructing a geographically weighted Poisson regression model or a geographically weighted logistic regression model fused with space-time non-stationarity based on a generalized linear model framework so as to fit a local space-time variation relationship between a numeric type or binary type response variable and an independent variable; solving a regression coefficient of the spatio-temporal change through an iterative algorithm; and finally, model output is converted into early warning information, a dredging scheme or a planning decision. According to the method, the data discrete characteristics and the process spatial-temporal heterogeneity are described in a unified manner, so that the problems of modeling error and insufficient precision caused by neglecting the spatial-temporal coupling effect and distribution mismatch when a traditional model is used for coping with spatial-temporal data with discrete distribution characteristics are solved; the accuracy and decision support capability of dynamic risk early warning and refined resource allocation in the fields of environmental monitoring, traffic safety, land planning and the like are remarkably improved.

The invention provides a method and a system for predicting a potential suitable growth area of alien invasive species in the technical field of risk management of invasive species. The method comprises the following steps: S1, acquiring a large amount of environment variable data and species distribution sample data; s2, downscaling the environment variable data through a spatial regression model to construct a high-resolution environment data set, performing spatial refinement on the species distribution sample data through a Moran index to construct a sample data subset, and constructing a data set based on the high-resolution environment data set and the sample data subset; s3, creating a species distribution model based on a maximum entropy model, a random forest, a support vector machine, a generalized linear model, an XGBOOST or a deep learning model, and training the species distribution model through the data set; and S4, predicting the suitable growth area of the alien invasive species through the trained species distribution model. The method has the advantages that the prediction precision and generalization ability of the potential suitable growth area of the alien invasive species are greatly improved.

The present disclosure relates to an application of gene markers in multi-cancer early detection, a method for constructing an early detection model, and a detection device. In the present disclosure, low-coverage whole-genome sequencing is conducted on cell-free DNAs (cfDNAs) from a plasma sample, and according to high-throughput sequencing results, six differential features of the cfDNA fragments are analyzed for each cancer. Then the training and modeling are conducted with a convolutional neural network to allow the early detection of a plurality of cancers at a low sequencing depth. Then the training and modeling are conducted with a generalized linear model (GLM), a gradient boosting machine, a random forest model, a deep learning model, and an extreme gradient boosting model, and staking is conducted with a GLM to construct a multi-feature algorithm, to allow the tissue-of-origin-based detection of cancers.

The invention discloses a machine learning-based tumor early screening expiration VOCs marker identification and verification method. The method comprises the steps of obtaining and processing high-dimensional full-spectrum expiration VOCs data; a generalized linear model is adopted to evaluate the influence of the queue correlation difference on the expiratory VOCs data, and VOCs with significant interaction are removed; screening potential expired VOCs biomarkers with significant difference between an experimental group and a control group by adopting a difference volcano plot method in which a kernel weighting function is introduced; a Boruta feature screening algorithm based on a random forest is adopted, and an expiration VOCs biomarker panel most related to the tumor is constructed; and evaluating the tumor diagnosis and grading performance of the expired VOCs biomarker panel by adopting a machine learning algorithm based on multiple underlying logics. According to the method, a marker panel construction process based on a machine learning algorithm is created, and a biomarker panel suitable for various algorithms is accurately and efficiently captured from massive expired VOCs.

The application discloses a method for predicting eel fry resource quantity, comprising the following steps: obtaining initial factors, screening the initial factors to obtain influence factors, obtaining historical data according to the influence factors, and constructing a generalized linear model and a nonlinear model; optimizing and verifying the generalized linear model and the nonlinear model through the historical data, obtaining current data according to the influence factors, predicting the current data through the verified generalized linear model and the nonlinear model, and combining the prediction results by weighting to generate an eel fry resource quantity prediction result. Through the technical scheme, the eel fry resource quantity in an estuary can be effectively and universally predicted.

Systems, methods, and computer-readable media are provided for determining matches between records of different systems based on aggregate record data, and graphically marking potentially matched groups of data along with predicted confidence levels. Preliminary matching tools may allow allow users to define various rules based on which a majority of the transactions can be matched and reconciled. However, remaining transactions are disposed of in an interactive matching process. The matches may be determined unidirectionally from a source transaction to transactions from a target ledger, or bidirectionally from transactions in the target ledger to transactions other than the source transaction. Transactions may be matched many-to-many, one-to-many, or many-to-one, and a proposed order of match selections may be presented in a user interface. Match metadata or insights may be displayed to show a confidence of the match, reasons for the confidence, and / or a confidence of other matches that may be more beneficial than a match with a source transaction. The confidence and match insights may be generated by a machine learning model with access to transactions from a source transaction ledger and a target transaction ledger. The machine learning model may be trained on manual activity for prior matches that have been made. Matches may be performed using a hybrid machine learning model that accounts for random forests, decision trees, neural networks, naïve bayes algorithm, and / or a generalized linear model. Machine learning models also incorporate ongoing feedback from the users who can either accept or reject suggested matches and hence the models undergo an evolution process and constantly update from user patterns.

The present application belongs to the technical field of cosmetic safety evaluation, and discloses a human patch test result typing method for cosmetics suitable for sensitive skin, which comprises the following steps: collecting comment data and performing deduplication and noise reduction processing, analyzing the comment data set using a trained RoBERTa-large language model, identifying adverse reaction comments, and calculating adverse reaction scores and adverse reaction rates; performing a patch test and setting a typing line according to the patch test results; extracting product characteristics of the cosmetics; establishing a generalized linear model; testing the likelihood ratio chi-square and significance of each generalized linear model, and screening the best typing of the cosmetics corresponding to the patch test. The method of the present application can effectively evaluate the safety risk of cosmetics for target users.

The application discloses a monochrome sensor mixed coding illumination and image reconstruction method and an imaging system, relates to the technical field of computational imaging, and comprises the following steps: first, at least three RGB mixed illumination modes are designed, and a coding matrix is constructed; then, a plurality of frames of gray scale images under the same target state are collected through a global shutter monochrome sensor according to sequence exposure; subsequently, a generalized linear model is constructed based on the coding matrix, an equation set is solved, and a color image is reconstructed; finally, a standard format image is output after post-processing. The application can eliminate dynamic color smearing in principle, has frame-level self-calibration capability, is compatible in algorithm and strong in robustness, keeps the system small and low in cost, and is suitable for dynamic imaging high requirement scenes such as medical and industrial scenes.

The invention discloses a hospital quality inspection management system and method based on AI intelligent analysis, and the method comprises the steps: collecting quality inspection data, and carrying out the preprocessing of the quality inspection data to generate a standardized data set; carrying out feature coding and format conversion, and constructing a quality inspection feature matrix with a unified format; sparse feature selection, feature importance evaluation and key feature screening; constructing an improved generalized linear model, and mapping key features and a target index function; performing parameter fitting and tuning to obtain a generalized linear model with optimal performance; performing generalized linear model prediction, and outputting a comprehensive quality analysis result; and generating a structured analysis report, intelligent early warning, task assignment and closed-loop management. According to the invention, by introducing the improved generalized linear model and the feature optimization mechanism, intelligent analysis and closed-loop management of hospital quality inspection data are realized, and the accuracy of quality early warning and the rectification response efficiency are improved.

The invention relates to the technical field of spine assessment and treatment, in particular to a children spine form assessment and detection method based on multi-source data fusion. Comprising the following steps: S1, obtaining observation indexes of a to-be-detected child and performing preprocessing; s2, constructing a potential section analysis model based on the standardized value, obtaining a fitting index of each model, and carrying out weighted discrimination on the fitting indexes to determine an optimal potential category number; and S3, performing variance analysis on each potential category among the standardized values of the observation indexes to check differences, and summarizing phenotypic features of each potential category according to difference directions and effect sizes of the indexes among different potential categories and naming the phenotypic features. According to the method, the adjusted generalized linear model is adopted on single pollutant analysis, and weighted quantiles, regression and multi-interpolation expansion thereof are introduced on mixed exposure analysis, so that the method is more targeted in risk identification and intervention target selection.

The application discloses a kind of method and system of joint parameter estimation and signal reconstruction under distributed antenna system, method includes: S1: constructing the generalized linear model of parameter estimation and signal reconstruction under distributed antenna system;S2: noise variance parameter and signal estimation value initialization, set iteration stop condition and obtain the input value required by preset algorithm;S3: respectively obtain the input parameter required by the noise variance estimator of each cluster antenna end and the approximate posterior probability of signal;And the estimated value of signal is solved;S4: the noise variance parameter corresponding to signal is estimated independently by minimizing variational bet free energy;S5: whether the preset iteration stop condition is reached, if yes, then flow to step S6;If not, then return to step S3 and carry out next round iteration;S6: iteration ends, and the estimated value of signal and the estimated value of corresponding noise variance of each cluster antenna end are output.The application realizes the joint estimation of noise variance and signal under different environments, and improves signal reconstruction performance.

The invention discloses a data-driven demand response feature recognition and uncertainty quantification method in a power system, and the method comprises the steps: firstly collecting the historical demand response data of a user, and constructing a data set comprising a user feature vector X, a response intention label Y and a response potential label Z; and establishing a mapping relation between the user feature vector X and the response will probability by using a generalized linear model, and assuming that Y obeys Bernoulli distribution. Aiming at a user with a response intention, establishing a mapping relation between conditional expectation of a response potential label Z and X through a generalized linear model framework, and constructing a parameterized probability distribution model of the Z; on the basis, joint probability distribution of Y and Z is constructed. And based on a sample independent identically distributed hypothesis, constructing a logarithm joint likelihood function, obtaining optimal parameter estimation by adopting a gradient descent algorithm, and determining an optimal model through AIC and BIC criteria. And finally, quantifying the uncertainty of response willingness and potential, and constructing a comprehensive index to evaluate the reliability degree of user response.

The invention discloses a cascade hydropower station surrounding environment effect evaluation integration method and a related device, and belongs to the technical field of environmental science and engineering. The method comprises the following steps: acquiring NDVI data and related climate data before and after a cascade hydropower station is built, and calculating to obtain annual vegetation coverage time sequence raster data and related annual climate time sequence raster data; converting the time sequence raster data into numerical data in a text form; calculating according to the numerical data to obtain a partial correlation coefficient of the vegetation coverage on each related climate factor, performing T test, and analyzing the influence of each related climate factor on the vegetation coverage; constructing a generalized linear model, obtaining a reservoir construction state, inputting the numerical data and the reservoir construction state into the generalized linear model for fitting, and evaluating contribution conditions of each factor to vegetation; and constructing a path model, inputting the numerical data and the reservoir construction state into the path model, and quantifying the influence degree of each factor on the NDVI.

The present invention provides a method for monitoring column performance and operating a chromatography column by applying a generalized linear model to system suitability parameters (SSPs) to assess how quickly the column is changing over time and whether the column stationary phase needs to be replaced. The method leads to faster identification of column failures, helping to maintain high separation quality and consistent analytical results for analytical and preparative chromatography methods. Columns evaluated and / or monitored by the method, as well as products resulting from the use of these columns and methods, are also provided.

The invention provides a respiratory tract epidemiological social contact gathering point identification and action range calculation method, and relates to the technical field of public health and epidemiology, and the method comprises the steps: obtaining infection case data and city POI data, carrying out the preprocessing of the infection case data, carrying out the spatial pairing of the preprocessed infection case data and a research region, carrying out the arrangement of the POI data, and selecting a social contact gathering point; a basic space unit is determined, the number of infected cases and the social contact gathering point density are counted and preprocessed, and multi-collinearity testing is carried out; establishing a generalized linear model of Poisson connection, and optimizing the linear model in combination with stepwise regression to obtain key social aggregation points; and constructing a multi-scale geographic weighting model, and calculating the optimal bandwidth of each key social aggregation point. According to the method, the key social contact gathering points influencing the transmission of respiratory tract infectious diseases can be accurately identified, the spatial action range of the key social contact gathering points is quantified, and a scientific basis is provided for formulating differentiated prevention and control strategies.

The invention belongs to the technical field of cosmetic safety evaluation, and discloses a cosmetic human body patch test result typing method suitable for sensitive skin, which comprises the following steps: collecting comment data and carrying out de-weighting and noise reduction processing, analyzing a comment data set by using a trained RoBERTa-large language model, identifying adverse reaction comments, and classifying the adverse reaction comments. Calculating an adverse reaction score and an adverse reaction rate; performing a patch test, and setting a parting line according to a patch test result; extracting product characteristics of the cosmetics; establishing a generalized linear model; and checking the likelihood bichi square and significance of each generalized linear model, and screening the optimal classification of the cosmetics corresponding to the patch test. According to the method, the safety risk of cosmetics to the target user can be effectively evaluated.

The invention discloses a false data injection attack real-time protection method for a dynamic line rating system, which belongs to the technical field of intelligent power grid network security, and comprises the following steps of: 1, collecting environmental data and preprocessing the data; the method comprises the steps of (1) obtaining a historical data matrix, (2) detecting daily environment data through a z-score method to obtain daily data, and updating the historical data matrix according to the daily data, (4) constructing a training data set and a test data set, (5) constructing a z-score-BGLM-LR model through a machine learning classification algorithm of binary generalized linear model logistic regression, (6) training the model by using the training data set, and (7) obtaining a z-score-BGLM-LR model. And 7, training the model, testing the model by using the test data set after the evaluation is completed, and detecting the real-time protection capability of the model, the detection model is constructed by adopting the method, and the method is suitable for network security protection in the dynamic line rating operation environment of the power system.

The application discloses a pet intelligent delivery room, which comprises a medical box for accommodating pets, and the medical box is provided with a database, a collection module, a processing module and a warning module; the risk assessment process of the pet intelligent delivery room comprises the following steps: screening and classifying extraction of historical data of pet delivery processes previously imported into the database to determine risk related factors in the delivery process; the risk related factors are valued to obtain corresponding quantitative data, the quantitative data are preprocessed, and multiple ordered logistic regression analysis is performed on the processed data to obtain a logistic regression model; generalized linear model analysis is performed on the quantitative data, and the regression model is evaluated; physiological indexes of the pet delivery process are collected in real time, and a risk category is predicted; and when the risk assessment reaches a warning condition, an alarm information is sent through the warning module. The application can effectively predict and monitor the risk in the pet delivery process.

The present disclosure relates to an application of gene markers in multi-cancer early detection, a method for constructing an early detection model, and a detection device. In the present disclosure, low-coverage whole-genome sequencing is conducted on cell-free DNAs (cfDNAs) from a plasma sample, and according to high-throughput sequencing results, six differential features of the cfDNA fragments are analyzed for each cancer. Then the training and modeling are conducted with a convolutional neural network to allow the early detection of a plurality of cancers at a low sequencing depth. Then the training and modeling are conducted with a generalized linear model (GLM), a gradient boosting machine, a random forest model, a deep learning model, and an extreme gradient boosting model, and staking is conducted with a GLM to construct a multi-feature algorithm, to allow the tissue-of-origin-based detection of cancers.

The application provides a personalized book recommendation method based on a Wide&Deep model. Previous studies mainly rely on manual analysis of readers' reading history, book classification and feedback to provide recommendation services for users, but there are problems of insufficient personalization and low efficiency. In order to solve this problem, a recommendation system combining generalized linear models and deep neural networks is proposed. The Wide part helps the model capture common behavior patterns of users by processing handcrafted features, while the Deep part is used to process high-dimensional sparse features and capture complex relationships between user interests. After preprocessing the user data, the Wide&Deep model is input to generate personalized recommendation results.

The invention provides an operation pressure assessment method and system based on an energy storage and new energy power station. According to the method, multi-source data of an energy storage system, new energy power generation and a power market are collected, after cleaning and standardization processing, a single-index approximate optimal active regression model is constructed, and a sparse generalized linear model is constructed and optimized by adopting an iterative hard threshold learning method. And finally, performing operation pressure evaluation on the real-time operation data by using the optimized model, and outputting an evaluation result and early warning information. According to the method, the problems of high calculation complexity, model over-fitting and incapability of effectively processing sparse correlation characteristics of an existing evaluation technology are solved, and the evaluation efficiency and accuracy are improved.

This invention discloses a data-driven method for demand response feature identification and uncertainty quantification in power systems. First, historical demand response data from users is collected to construct a dataset containing user feature vectors X, response intention labels Y, and response potential labels Z. A generalized linear model is used to establish a mapping relationship between the user feature vector X and the probability of response intention, assuming Y follows a Bernoulli distribution. For users with response intentions, a mapping relationship between the conditional expectation of the response potential label Z and X is established using the generalized linear model framework, and a parameterized probability distribution model of Z is constructed. Based on this, a joint probability distribution of Y and Z is constructed. Based on the assumption of independent and identically distributed samples, a log-likelihood function is constructed, and the optimal parameter estimate is obtained using the gradient descent algorithm. The optimal model is determined using the AIC and BIC criteria. Finally, the uncertainty of response intention and potential is quantified, and a comprehensive index is constructed to evaluate the reliability of user responses.

The invention relates to the technical field of medicine recommendation, in particular to a personalized medicine recommendation method and system based on causal inference, and the method comprises the steps: obtaining entity data based on medical sample data; analyzing electronic medical record information in the entity data to obtain medical entity distribution, and generating a medical entity causal relationship graph by adopting a greedy intervention equivalent search algorithm based on the medical entity distribution; analyzing the association of diseases, operations and drugs in the medical entity causal relationship graph, calculating the quantitative influence of the drugs on the diseases and programs by adopting a generalized linear model adjusted by a backdoor standard, and constructing a causal effect matrix; adopting a dynamic self-adaptive attention mechanism to obtain corrected entity data, adopting a point-to-point relation method to obtain refined disease, operation and drug embedding vectors, and accumulating the disease, operation and drug embedding vectors together to obtain a clinical comprehensive representation vector; and outputting a drug recommendation score and a recommended drug list based on the clinical comprehensive representation vector. The method provides auxiliary decision-making reference for diagnosis and medicine preparation of doctors.

The present application relates to the technical field of spinal evaluation treatment, and particularly relates to a children's spinal form evaluation and detection method based on multi-source data fusion. The method comprises the following steps: S1: obtaining observation indexes of a child to be measured and performing pretreatment; S2: constructing a latent profile analysis model based on standardized values, obtaining a fitting index of each model, and performing weighted discrimination on the fitting index to determine an optimal latent category number; S3: performing variance analysis test difference between standardized values of each observation index of each latent category, and inducing a phenotype characteristic of each latent category and performing naming according to a difference direction and effect size of each index between different latent categories. The present application can be more targeted in risk identification and intervention target selection by using an adjusted generalized linear model on single pollutant analysis and introducing weighted quantile regression and its multiple imputation expansion on mixed exposure analysis.

The application discloses a water ecological damage evaluation identification method based on high-throughput environmental DNA sequencing, and belongs to the technical field of environmental damage investigation and identification, and comprises the following steps: collating a species list of a target area, searching and downloading species sequence information, constructing a local database, and supplementing the local database information through specimen sequencing; collecting benthic animal samples of multiple target area sample points, selecting test samples therefrom, extracting sample DNA, amplifying and performing high-throughput sequencing, performing species comparison according to the sequencing results, presetting multiple read number thresholds, judging whether species in a random mixed species sample and a quantitative proportion mixed species sample are detected, constructing an environmental DNA sequencing and morphological mapping relationship model by using a generalized linear model, optimizing the environmental DNA sequencing and morphological mapping relationship model, calculating the population resource quantity of a specific species in a natural sample, performing water ecological damage indicator species resource quantity evaluation and identification, and improving the water ecological damage identification accuracy and efficiency based on environmental DNA.