140 results about "Demographic data" patented technology

A system for the dynamic optimization of sponsored product advertising using real-time data and machine learning, which includes the following: a) a data collection and integration module configured to collect real-time data from advertising platforms, e-commerce websites and user interactions, and to integrate key performance indicators such as impressions, click-through rates (CTR), conversion rates and competitors' bidding strategies; b) a data preprocessing and filtering module configured to clean, normalize and structure the collected data by removing inconsistencies, duplicates and irrelevant information; c) a machine learning machine configured to analyze historical and real-time data using predictive models, including deep learning and regression algorithms, to identify patterns in user behavior and optimize bidding strategies; d) a real-time trend analysis module configured to detect emerging market trends, seasonal fluctuations and changes in consumer sentiment to enable adaptive adjustments to ad placement; e) a bid management and optimization module configured to dynamically adjust ad bids in real time based on auction data, competitor activity and performance metrics to ensure cost-effective ad placements; f) a module for targeting and segmenting audiences, configured to classify users based on demographic data, browsing behavior, purchase history and engagement patterns, thus ensuring optimal ad delivery to relevant consumers; (g) an ad creative optimization module configured to analyze ad performance using A / B testing and natural language processing (NLP) and automatically make recommendations or changes to ad creatives to improve engagement and conversion rates; h) a performance monitoring and analytics dashboard configured to provide real-time visual insights into ad performance metrics, optimization recommendations, and budget usage; (i) an automated decision and control system configured to make bid adjustments, budget reallocations, and target audience refinements based on machine learning insights without manual intervention; and j) a compliance and fraud detection module configured to monitor ad interactions, detect fraudulent activities such as click fraud and bot traffic, and ensure compliance with platform policies; with the system continuously optimizing sponsored product advertising by using real-time data and machine learning to maximize return on investment and improve advertising efficiency.

The invention discloses a spatial matching method and system based on natural resource monitoring and demographic data, and the method comprises the steps: obtaining residential land and house building data in regional natural resource survey monitoring data, employing a spatial position and attribute matching technology, and combining a random forest algorithm to carry out the spatial matching of the residential land and the house building, thereby achieving the spatial matching of the residential land and the house building. Extracting residential building pattern spots and attribute information thereof; establishing attribute feature mapping between a residential building pattern spot level scale and an administrative division unit scale, and converting administrative division demographic data to residential building pattern spots by using a random forest hyper-parameter search method; further integrating multi-source data in the natural resource survey monitoring data, constructing a population geographic blot index taking a water area as a mask, and correcting the population of the residential building; and finally, calculating a population distribution fitting coefficient, iteratively correcting a population distribution error, and realizing spatial accurate matching of the residential building and population distribution through a partition adjustment strategy.

The invention discloses an image genetics correlation analysis method based on comparative learning and a conditional generative network, and the method comprises the steps: firstly, carrying out the nonlinear mapping of multi-modal image data to a potential space, and extracting the features of the image data; then, implementing comparative learning among the multi-modal image features to enhance feature embedding; in order to adaptively learn the importance of feature embedding, an attention mechanism is introduced to effectively fuse multi-modal image data; in addition, single nucleotide polymorphism (SNP) data is preprocessed into numeric data through a sample population mapping matrix, and then the SNP data is further encoded using a variational autoencoder to extract potential genetic features; in order to construct a non-linear correlation between nerve image data and genetic data, SNP data and demographic data are utilized, a dual-identification condition generative adversarial network is proposed to generate a multi-modal nerve image and a modal attention vector, and early diagnosis of brain diseases can be realized.

The invention provides a lower respiratory tract hospital infection risk prediction method and system based on deep learning, and relates to the field of medical electronic medical record time sequence data analysis processing and deep learning. According to the method, two diagonal masking self-attention mechanism blocks are utilized to carry out missing value filling on electronic medical record medical time sequence data; processing the demographic data in the electronic medical record by using a full-connection layer neural network, and initializing the hidden state of a decoder; constructing an encoder-decoder model based on an attention mechanism, performing model training by using the filled time sequence data and the hidden state of the initialized decoder, and constructing a lower respiratory tract hospital infection risk prediction model; and inputting the processed data into the trained lower respiratory tract hospital infection risk prediction model, and outputting a lower respiratory tract hospital infection risk prediction result of the patient. According to the method, mass data of the electronic medical record can be effectively utilized, the static characteristics and the dynamic time sequence characteristics are fused, the health state of the patient is captured, and the prediction accuracy and effectiveness are improved.

The invention provides a number card distribution system based on message middleware, relates to the field of data management, and solves the technical problem of high data storage security risk of an existing number card distribution system. The system comprises a message center module which is used for processing asynchronous communication and data transmission in the system by adopting a distributed message middleware cluster; the data security module is used for managing distribution data of each region by adopting a data fragmentation storage mechanism; and the demand prediction module is used for predicting the number card demand quantity of the next period according to the sales data and the demographic data of each region, and guiding the formulation of a sales strategy of the next period. The method and the device are used in the whole-process management process of the number card distribution service.

A system and method for automated generation of simulated responses from one or more virtual emulated respondents includes collecting a reference survey artifact comprising unanswered questions, obtaining demographic data for the one or more virtual emulated respondents, constructing a demographic vector for each virtual emulated respondent representing the characteristics of the associated virtual emulated respondent, generating responses for each target virtual emulated respondent to each unanswered question by transmitting inputs including the associated demographic vector, each unanswered question, and an iterative contextual parameter to a response generation model, and updating a simulated response dataset with the generated responses.

The invention provides a risk assessment prediction method and system suitable for a county resource environment. Belongs to the technical field of resource environment risk assessment. The method comprises the steps that multi-source data in a county area are acquired, the multi-source data comprise meteorological data, water quality data, soil data, remote sensing images and demographic data, and the acquired multi-source data are preprocessed; and fusing the preprocessed multi-source data through a data fusion algorithm based on machine learning, constructing a county resource environment comprehensive database, and extracting key features from the comprehensive database through feature engineering. By integrating multi-source data from different fields and performing data fusion through a machine learning algorithm, multi-dimensional factors can be comprehensively considered, and information of different data sources is fully utilized, so that the accuracy and comprehensiveness of risk assessment are improved.

The invention relates to the technical field of carbon emission evaluation, and relates to a carbon emission evaluation method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining the space matching data of a target region boundary and parking spaces, and generating the number and distribution information of effective parking spaces; the time sequence dynamic remote sensing data, the traffic density fluctuation data and the demographic data are fused, a parking space activeness factor model is constructed, and the parking space use intensity is output; predicting the vehicle type distribution proportion of the target area by using a neural network, and calculating the annual average driving distance of the vehicle; calculating a dynamic vehicle carbon emission factor and a static vehicle carbon emission factor according to the time dynamic nature and the regional difference of the power grid energy; and calculating the total carbon emission of the target area by integrating the vehicle type distribution proportion, the annual average driving distance and the carbon emission factor based on a collaborative correction mechanism of the parking space resource tensity and the parking space use intensity. The objective of the invention is to realize community-level high-precision carbon emission accounting and improve the spatial resolution and adaptability of carbon emission accounting.

A method includes: receiving product data associated with a product; identifying at least one brand characteristic of a brand associated with the product, based on the product data; identifying at least one attribute of the product based on the product data; generating an attribute insight output based on at least the at least one attribute of the product; receiving demographic data associated with the product; generating a demographic insight output based on at least the demographic data associated with the product; receiving a suggested strategy output, wherein the suggested strategy output is generated based on, at least, the attribute insight output and the demographic insight output; generating a content brief output based on the suggested strategy output; and providing, at a display, an actionable output that includes at least the content brief output.

Methods, systems, and computer program products are provided for shared latent space-based debiasing. An example system includes at least one processor configured to: transform data from each of a target domain, which lacks protected features, and a separate source domain, which contains these features, into correlated latent representations; jointly train a cross-domain protected group estimator on the representations; and debias a downstream machine learning model an adversarial learning technique that leverages the group estimator.

pn. "A system for collecting, analyzing and processing of user behavior data from multiple sources in order to create a personalized user distribution scheme with contextual selection of content on the website." 1. Identifying and collecting information about the Internet user and his behavior in real time. 2. Processing of available data in different time intervals. 3. Providing information (real-time and historical) on the Internet user's traffic on the website. 4. Automatic contextual selection of materials on the website The prepared platform is an IT system designed to build 'content' websites focused on interaction with Internet users. The platform contains a number of logical modules aimed at increasing the activation of the Internet user. Modules can connect to each other using webservices or other defined APIs required in a specific situation. This enables easy connection of platform modules with external applications. The individual components have been additionally divided into virtual zones, only within which communication and data flow are possible. As part of building the prototype, we try to embed new projects on the PaaS platform, but it is necessary to ensure full compatibility with previous solutions, which is why an application development strategy was adopted in the microservices model. Flowchart of information Assistant (IA)) 1. Each internet user is automatically marked and assigned a unique identifier. 2. On each page or in our application there are IWA scripts that send information to the collector: 1. Describing the client environment, i.e. the type of application, operating system, browser information, IP addresses, adblock information, etc. 2. Optional (if completed) personal information such as demographic data such as age, gender, place of residence, e-mail hash, IDFA / GAID advertising identifiers. 3. Internet user's traffic on our websites, i.e. where he was, when and where did he come from and where he went, and what types of materials he viewed. 3. All the above data goes to our Big Data system, after appropriate transformation to the appropriate formats. 4. For all our products (internet services), a common content categorization taxonomy has been developed, which is used at subsequent stages to determine the interest groups of Internet users. 5. Based on the collected data, applications work in which we have developed a number of algorithms that are aimed at: 1. Preparation and calculation of an Internet user profile. 2. Preparation of tables describing thematic interests for each Internet user identified by us. 3. Reconstruction of Internet user IDs and implementation of the so-called cross-device identification. 6. On the basis of the collected data, another group of applications with recommendation and predictive algorithms works, the purpose of which is: 1. Preparation of a stream of information that is selected and arranged in such a way that the Internet user increases his engagement by making more views on our websites. 2. In the information stream, we inject additional individual supplementary information depending on the data resulting from the Internet user profile.

The invention discloses a heating season city gas system load prediction method and system, and relates to the technical field of gas system load prediction, and the method comprises the following steps: collecting the related original data of a heating season city gas system, the original data comprises historical data and real-time data, and carrying out the preprocessing of the collected original data, and obtaining a gas load analysis set and a demand variable of gas load analysis. According to the method, the gas consumption, the meteorological data, the demographic data and the gas price data are comprehensively analyzed, the gas load prediction model is constructed, the accuracy of gas load prediction is remarkably improved, and by analyzing historical extreme weather events, calculating the extreme weather influence index and evaluating possible risks, the gas load prediction accuracy is improved. And the risk level is quantified by combining the gas load evaluation index and the risk early warning coefficient, the early warning level is dynamically adjusted according to real-time data, prevention measures are taken in time, and the influence of extreme weather events on gas supply is reduced.

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

The invention provides an urban building group three-dimensional function partitioning and floor population dynamic distribution method and device, and relates to the technical field of urban space information processing. The method comprises the steps of constructing semantic feature vectors of multi-source POI data based on a BERT model and domain specific vocabularies to generate fusion data; building a floor position probability distribution model according to a three-layer progressive reasoning structure and a reasoning technology; carrying out three-dimensional function partitioning according to the function coupling strength and an improved spectral clustering algorithm; building-level demographic data are generated, and a differentiation attraction model is constructed; and solving the floor population distribution constraint optimization model according to the improved Haff model to obtain population dynamic distribution of each floor. According to the method, granularity limitation of traditional function division is broken through, floor-level refined three-dimensional function division and population dynamic distribution prediction are realized, and important decision support is provided for applications such as commercial site selection, emergency evacuation path planning, public service facility configuration optimization and the like in smart city construction.

Methods and apparatus are disclosed regarding an e-commerce system that places customers into a plurality of clusters and tailors services provided to a customer based on the cluster in which the customer is placed. In one embodiment, the e-commerce system defines the clusters based on purchase history data for customers having sufficient purchase history data. The e-commerce system then places customers without sufficient purchase history data into one of the defined clusters based on demographic data for the customer and demographic data for the customers in the cluster.

The invention discloses a neurodegenerative disease analysis method and device based on multi-modal data and a terminal, and the method comprises the steps: obtaining target user data corresponding to a user, and determining a data type according to the target user data, the data category comprises more than one of gait feature data, demographic data, cognitive data and bioelectricity detection data; and adopting a risk assessment method corresponding to the data category to determine a risk level corresponding to the user according to the target user data. Since the acquired target user data contains other data categories except the gait feature data, and the risk level corresponding to the user is assessed by adopting the corresponding risk assessment method according to the data categories, the problem that the existing method focuses on single analysis of the gait data can be effectively solved; and comprehensive analysis is not carried out in combination with demographic data, cognitive information and the like of different individuals, so that the comprehensiveness of neurodegenerative disease analysis is limited.

A system and method are disclosed for detecting chronic health conditions based on data collected by a wearable device such as an activity tracker or a smart watch. Deep learning algorithms are configured to process the monitored parameter data collected by the wearable device as well as additional embedding data obtained from health records corresponding to a user account registered to the wearable device. In some examples, the input vector can also include embedding data related to social determinants data and / or demographic data. The output of the deep learning algorithms provides predictions that represent probabilities that the user of the wearable device has an underlying health condition. If any underlying health condition is detected, then the user can be notified directly, via the wearable device or an associated application or technology, or indirectly, via a primary care provider associated with the user.

This invention relates to the field of ecological protection zone identification technology, and particularly to a method for improving the accuracy of the Human Footprint Index. The method comprises the following steps: S1, Population Grid Redistribution: A construction land mask is extracted from land cover data. Population statistics data from the construction land mask and population grid data are input into the iURBAN algorithm to redistribute the population grid data. Population statistics data are then used to verify the accuracy of the population grid data before and after redistribution. S2, Fragmentation Index Scoring: A movement speed grid is calculated. City tiers are classified based on land cover data. Based on the city tier classification and movement speed grid, different levels of urban patches representing urban edges are obtained. Landscape pattern indicators representing fragmentation are calculated, and fragmentation index scores are assigned based on the landscape pattern indicator calculation results. S3, Human Footprint Index Calculation. This invention redistributes population data from the population grid to optimize the population distribution and improve the accuracy of the population grid data.

A disclosed example includes accessing computer-generated impression records, the computer-generated impression records based on network communications received at a server of a database proprietor from client devices, the computer-generated impression records indicative of accesses to media at the client devices; accessing self-reported demographic data and behavioral data from a database of the database proprietor, the self-reported demographic data and the behavioral data corresponding to user accounts registered with the database proprietor and associated with the client devices; comparing the self-reported demographic data with a probability distribution of higher-accuracy demographic data; determining different adjustments for corresponding ones of self-reported demographics of the self-reported demographic data based on the comparison; adjusting the corresponding ones of the self-reported demographics based on corresponding ones of the different adjustments to generate adjusted user demographic data; and assigning the adjusted user demographic data to corresponding ones of the computer-generated impression records.

Methods and apparatus to determine probabilistic media viewing metrics are disclosed herein. An example apparatus includes memory including machine reachable instructions; and processor circuitry to execute the instructions to calculate a first probability for respective ones of a plurality of panelists as having viewed media based on viewing data, the viewing data including incomplete viewing data for one or more of the panelists relative to the media; identify respective ones of a plurality of panelists as included in a demographic subgroup based on demographic data for the panelists; assign a sampling weight to the respective ones of the plurality of panelists based on the demographic data; and calculate a second probability of the demographic subgroup having viewed the media based on the first probabilities and the sampling weights for the respective ones of the plurality of panelists in the demographic subgroup.

Demographic estimation system, comprising: a feature generator for generating features from return path data (RPD) reported by set-top boxes associated with return path data households; a neural network to process the features generated from the return path data and predict demographic classification probabilities for the return path data households, wherein the neural network is to be trained based on forum data reported by measurement devices that monitor media devices associated with the participant households; a demographic assignment engine for assigning one or more demographic categories to corresponding ones of the return path data households based on the predicted demographic classification probabilities; and a visitor assignment engine for assigning virtual visitors to at least a subset of the corresponding ones of the return path data households based on the one or more demographic categories assigned to the corresponding ones of the return path data households.

The invention provides a crowd health-oriented environment risk dynamic regulation and control method and a storage medium, relates to the field of public health, and systematically integrates macroscopic environment monitoring and demographic statistics data, multi-pollutant air quality health indexes and individual-level longitudinal queue data. A multi-pollutant air quality health index is used as priori knowledge through a constructed fusion module, an individual-level causal model is processed, a processed health risk function is coupled with a pollution control marginal cost function and refined health economic evaluation parameters, and a comprehensive optimization function is constructed. And performing probabilistic representation and propagation on uncertainty of all key parameters in the target function, finally solving to obtain a control threshold range with a confidence interval, re-substituting the optimized control threshold into the multi-pollutant air quality health index calculation framework, evaluating the risk level change of the multi-pollutant air quality health index calculation framework under a historical reference, and calculating the multi-pollutant air quality health index. And an empirical support from historical data is provided for the rationality and feasibility of an optimization result.

The invention relates to an infant asthma three-branch classification auxiliary judgment method and a medium, and relates to the technical field of data processing. The method comprises the following steps: collecting and preprocessing tidal respiration data and demographic data of infants, and performing heterogeneous feature extraction and grouping log-likelihood ratio acquisition on the preprocessed tidal respiration data and demographic data to obtain a log-likelihood ratio corresponding to each group; and performing hierarchical Bayesian fusion on each log-likelihood ratio to obtain a fusion result, and performing asthma risk auxiliary judgment of three-branch classification based on the fusion result. Compared with the prior art, the method can assist in judging the infant asthma more objectively and accurately based on the tidal respiration data.

The invention discloses a government-oriented environmental sanitation station planning distribution intelligent decision-making method and system, and relates to the technical field of environmental sanitation station planning decision-making. The method comprises the following steps: acquiring multi-class distribution decision data related to environmental sanitation station planning, wherein the distribution decision data comprises geographic information data, territorial resource planning data, demographic data, traffic flow data and economic data; obtaining an influence factor between each type of distribution decision data and the garbage generation amount; inputting the influence factors into a deep learning-based environmental sanitation station planning distribution prediction model to obtain the probability that each position in the to-be-planned area is set as an environmental sanitation station; and obtaining a sanitation station layout result of the to-be-planned area according to the numerical value of the probability that each position is set as the sanitation station. By means of the method and the related system, the scientific layout of the environmental sanitation station can be achieved.

Systems and methods for generating user-specific recommendations of products or services by collaborative filtering executed and / or performed by one or more trained or untrained predictive models configured to ingest product attribute(s), product purpose(s), user location data, and / or user demographics. The predictive model(s) are leveraged to detect and determine user-specific preferences for, and preferences against, particular attributes, features, ingredients, aesthetic styles, and so on.

The application discloses a coronary heart disease prediction method fusing cardiopulmonary exercise parameter time sequence information, and comprises the following steps: collecting multidimensional health data of a patient; preprocessing the multidimensional health data; extracting features from the preprocessed data; performing time sequence analysis on the extracted features by using a deep learning model; inputting the time sequence analysis result and static features into a classification model to perform coronary heart disease prediction, outputting a prediction result and generating a related health report. By acquiring demographic data, basic medical information and cardiopulmonary exercise test data of a target object, specifically including age, gender, body mass index, socio-economic information, past history, complications, clinical examination and inspection, exercise capacity, cardiovascular function and ventilation perfusion and the like, the coronary heart disease is predicted by fusing static parameters and time sequence information, so that dynamic changes in the health data of the patient can be better captured, and the prediction accuracy is improved.

The present invention relates to a method for predicting whether to perform an important intervention on the basis of machine learning, the method including the steps of: receiving an input of vital sign data of one or more patients, demographic data, arrival path data, and data on whether to perform an important intervention after arriving at an emergency room as training data for training a prediction model; training the prediction model by using the training data so that the prediction model outputs whether to perform an important intervention in response to receiving the input of vital sign data, demographic data, and arrival path data; and inputting vital sign data, demographic data, and patient arrival data of an emergency patient to the trained prediction model to output the degree to which performing an important intervention is necessary, the necessity thereof, and the number of necessary important interventions.

A method for managing computer resource use in computer-implemented risk assessment of a subject in respect of a context. Demographic subject data for the subject is passed to a first trained machine learning model trained on first context-specific historical risk outcomes correlated with historical demographic data corresponding to the demographic subject data. If a first threshold assessment from the first trained machine learning model is passed, the subject is approved. Responsive to failing the first threshold assessment, supplemental context-related subject data for the subject, in addition to the demographic subject data, is passed with the demographic subject data to a second trained machine learning model trained on second context-specific historical risk outcomes correlated with the historical demographic data and with historical context-related data corresponding to the supplemental context-related subject data. If a second threshold assessment from the second trained machine learning model is passed, the subject is approved.

The present invention concerns a method for calculating one or more isovists of a physical or extended reality environment, comprising the steps of: providing at least one electronic acquisition apparatus configured to obtain a plurality of georeferenced and physiological, and / or psychological, and / or environmental, and / or perceptual and / or demographic data, and to take a frame of the physical or extended reality environment, detect physiological variations and / or environmental variations; calculating a behaviour indicator comprising at least one positioning parameter for each user from the georeferenced data acquired by means of electronic acquisition apparatus; calculating a perception indicator comprising at least one or more physiological and / or environmental parameters for each user by means of electronic acquisition apparatus; providing a relationship identifying algorithm; identifying for each user one or more statistically significant physiological and / or environmental parameters by means of the relationship identifying algorithm by identifying a relationship between the positioning parameter and the aforesaid physiological and / or environmental parameters; associating for each user the positioning parameter with the identified, statistically significant parameters to define respective data units; defining a positioning threshold and a physiological and / or environmental threshold; grouping into respective experience sets data units having respective statistically significant physiological and / or environmental parameters differing from each other by a value lower than the respective threshold and respective positioning parameters differing from each other by a value lower than the positioning threshold, calculating at least one isovist for each experience set; representing each isovist calculated in a graphic interface of an electronic display apparatus.

Aspects of the subject disclosure may include, for example, a method in which a processing system obtains first cellular traffic data associated with an existing cell site of a communication network, first transportation data associated with a first geographic region including the existing cell site, and first household data regarding households located in the first geographic region; the first household data comprises cellular plan data, broadband plan data, demographic data and / or economic data. The processing system constructs a predictive model for cellular traffic applicable to a candidate cell site in a second geographic region, using the cellular traffic data and the first transportation data and / or the first household data. The processing system predicts second cellular traffic at the candidate cell site, using as inputs to the model second transportation data and second household data associated with the candidate cell site. Other embodiments are disclosed.