13 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.

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 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 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 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 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 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 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 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.