457 results about "Prediction score" patented technology

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for generating a plurality of training embeddings based on a pre-training dataset, wherein the plurality of training embeddings comprises one or more of descriptive embeddings, sequential ordering embeddings, age / time embeddings, locale embeddings, or encounter number embeddings; generating one or more initialized weights associated with respective one or more layers of a machine learning model based on the plurality of training embeddings; generating one or more fine-tuned weights for the machine learning model by updating at least a portion of the one or more initialized weights using a fine-tuning dataset associated with a target classification; and generating, using the machine learning model, one or more prediction scores for one or more prediction encounter data elements associated with the target classification, based on one or more input temporal sequence of encounters data records.

The invention is applicable to the technical field of biological information, and provides a drug target prediction method based on a graph neural network, which comprises the following steps of: respectively taking a drug and a target node as a core, and automatically extracting local sub-graphs of the drug and the target node to capture local context information of the nodes in a complex biological network; taking different types of edges as feature information and fusing the feature information into a feature learning process, dynamically adjusting contribution degrees of the different types of edges to node representation through an attention mechanism, introducing residual connection and a normalization processing strategy, and fusing node features; and performing deep mining on the fused node features by using a two-layer graph convolutional network, and calculating a potential interaction prediction score between the drug and the target. The invention further provides a drug target prediction model based on the graph neural network. According to the method, independent feature extraction and training are carried out on predicted drugs and target spots, and the accuracy and richness of node representation are effectively improved while the flexibility of the model is kept.

The invention relates to an article sequence recommendation method and system based on collaborative filtering and an LLM perspective, and the method comprises the steps: obtaining a user historical data set and an article data set, and carrying out the preprocessing, and obtaining an article title similarity matrix, a user behavior sequence, and historical collaborative filtering interaction information; historical collaborative filtering interaction information is enhanced through a large language model; constructing a sequence recommendation model, inputting the user behavior sequence into the sequence recommendation model for training, and correcting the deviation of the user behavior sequence through comparative learning according to the item title similarity matrix and the enhanced historical collaborative filtering interaction information to obtain a trained sequence recommendation model; and inputting the user behavior sequence of the user into the trained sequence recommendation model for prediction, calculating prediction scores, and generating a recommendation list according to score sorting, thereby completing article sequence recommendation. According to the method, the performance of the sequence recommendation system is greatly improved by solving the cold start problem.

The invention discloses a user behavior sequence recommendation method and system based on the combination of a large model and a graph neural network, and a storage medium, relates to the technical field of behavior sequence recommendation, and aims to solve the problem that the text feature extraction capability of a large language model and the dynamic modeling capability of sequence recommendation are difficult to deeply fuse in an existing method. And accurate and comprehensive recommendation is difficult to realize. A large-model-based article text representation learning module of an LLM-SR model extracts high-quality semantic embedding of an article text through a large language model, and article representation adaptive to a recommendation task is generated in combination with linear layer mapping and a multi-layer perceptron; capturing a local sequence relation and a cross-session co-occurrence relation of the articles by utilizing the session graph and the global graph through a sequence representation learning module based on a graph neural network, and generating comprehensive representation of a user behavior sequence; and article scores are calculated according to the dot product of the user sequence representation and the candidate article embedding through a prediction score calculation module, and a final recommendation result is generated.

The invention relates to an intelligent lighting energy-saving method and system based on multi-modal data fusion. The method comprises the following steps: periodically obtaining a decision trajectory data set; combining the experience pool of the previous period with the decision trajectory data set newly collected in the current period according to a set proportion, and screening according to priority weight; training to obtain candidate strategies; evaluating the candidate strategies in a test scene set; adding the strategy comprehensive score into a strategy library when the strategy comprehensive score meets an access condition; when the strategy capacity in the strategy library reaches a preset value, eliminating the strategy with the lowest comprehensive strategy score based on the Pareto frontier; constructing a fitness function of the strategy and establishing a feedback-weight mapping model used for obtaining a prediction score; and based on the feedback-weight mapping model, establishing a loss function gradient, and based on the loss function gradient, performing iterated weight calculation to obtain iterated weights after the iterated calculation. According to the invention, the long-term energy-saving efficiency and the user experience of the lighting system are improved.

The invention relates to the technical field of electric power system communication, in particular to an electric power 5G network service quality monitoring method and system and a storage medium, and the method comprises the steps: responding to a protocol data unit session establishment request signaling, and obtaining signaling plane data and data plane performance data through a signaling plane probe and a data plane probe; associating the signaling plane data with the data plane performance data through the service quality flow identifier and the timestamp to generate a multi-dimensional spatial-temporal characteristic matrix; based on the multi-dimensional spatio-temporal characteristic matrix, a network quality prediction score is obtained through dynamic spatio-temporal causality graph network prediction; and when the network quality prediction score exceeds a preset score threshold, establishing a network resource configuration optimization model to obtain an optimal network resource configuration strategy. According to the method, the signaling plane data and the data plane performance data are subjected to correlation analysis, and the network quality is predicted by using the dynamic space-time causal graph network, so that the network resource configuration is optimized, and the service quality of the power 5G network is ensured.

The invention relates to the technical field of artificial intelligence, in particular to a process reward model training method and system. The method comprises the following steps: calculating a confidence score of a question text in each sample; based on the correctness score, the confidence score and the tolerance distance hyper-parameter of each reasoning step of the problem text in the labeled sample, obtaining a target correctness score of each reasoning step of the problem text in the labeled sample; and training the process reward model based on binary cross entropy loss between the correctness prediction score of each reasoning step of the problem text in the labeled sample and the target correctness score, and taking the trained process reward model as a target process reward model. According to the method, the accuracy and reliability of process reward model training are improved, and the text generation precision of a large language model is enhanced.

The invention discloses a multi-modal robust recommendation method and system based on self-supervised learning, and the method comprises the steps: extracting the modal features of each modal of a user and an article from original data as initial embedded vectors, and constructing a user-article heterogeneous graph and an article-article isomerism graph under each modal; performing multi-modal feature coding on the initial embedded vector of each modal, and performing multi-modal feature fusion on the enhanced final embedded vector of each modal; and performing multi-modal feature alignment to extract a final user embedded vector # imgabs0 # and a final article embedded vector # imgabs1 #, calculating to obtain a final prediction score of the user on the articles, sorting the final prediction score, selecting specified first K articles as a recommendation list recommended to the user, and outputting the recommendation list. The invention aims to eliminate the influence of inherent noise in the multi-modal data and improve the recommendation effect of the whole multi-modal recommendation system.

The invention provides a risk control cross-domain risk prediction method and system in combination with transfer learning, and the method comprises the steps: carrying out the domain difference measurement of a source domain and target domain risk control scene, generating a domain difference measurement matrix, building a hierarchical knowledge transfer channel based on the domain difference measurement matrix, and carrying out the prediction of the risk control cross-domain risk. And selectively migrating the source domain risk prediction model parameters, generating a target domain initialization risk prediction model, and carrying out dynamic adaptive training on the target domain initialization risk prediction model by adopting a target domain risk sample to obtain a cross-domain risk prediction model. And inputting the to-be-evaluated sample of the target domain into the cross-domain risk prediction model to generate a preliminary risk prediction score, and finally performing cross-domain calibration on the preliminary score based on the domain difference metric matrix to generate a final risk prediction result, thereby improving the accuracy of risk control cross-domain risk prediction.

The invention discloses an N-sugar chain marker combination for predicting the curative effect state of IgA and IgG type MM and a prediction scoring system and application of the N-sugar chain marker combination. The N-sugar chain marker combination is composed of the following 11 N-sugar chains: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4 and NA4Fb. The method has the advantages that (1) the relevance between the N-carbohydrate chain and the curative effect in the blood of the IgA and IgG type MM patients in different curative effect states is found for the first time, and the N-carbohydrate chain can be used as an index for predicting the curative effect states of the IgA and IgG type MM patients for the first time; and (2) the N-carbohydrate chain curative effect prediction scoring system provided by the invention is used for predicting the curative effect states of IgA and IgG type MM patients, and a new technical means is provided for predicting the curative effects of the IgA and IgG type MM patients.

The embodiment of the invention provides a display method and device for a control on a human-computer interaction interface and a storage medium, and belongs to the technical field of industrial control. The method comprises the following steps: acquiring target user information and interface use data of a currently logged-in target user; the interface use data represents use data generated by clicking and using each preset control on the human-computer interaction interface; inputting the target user information and the corresponding interface use data into a preset operation prediction model, and determining a prediction score of classifying each preset control into each preset display category; according to each prediction score and the current menu level of each control, determining a corresponding target menu path of each control; and according to the target menu path of each control, rendering each control in a preset display interface. According to the embodiment of the invention, the control layout on the human-computer interaction interface can be automatically adapted to the login user, so that the operation efficiency of the control on the human-computer interaction interface is improved.

The invention discloses a psychological counseling intelligent recommendation method and system based on artificial intelligence, and relates to the field of psychological counseling cross technology, and the method comprises the steps: calculating a three-dimensional emotion vector according to an emotion axis space and a syntactic structure, constructing a psychological semantic tensor by using a tensor outer product, generating a user intention tensor by using a prototype weight, and carrying out a flattening operation. Obtaining a final intention vector; combining the structural potential energy deviation and the spectral distance to construct a matching factor tensor; constructing a factorization machine model, predicting a matching score, generating a prediction score matrix, screening by using a Top-K selection method, generating recommended content, and constructing a visual interface to display the recommended content. According to the method, the fine granularity and accuracy of emotional representation are improved through emotional axis space and syntactic dependency analysis, the matching precision of intention and resources is improved through comprehensive calculation of semantic gravitation intensity and spectral distance, and the expression ability of matching factor tensor is enhanced through fusion of structural potential energy deviation and spectral distance.

The invention provides a human behavior recognition method based on fusion of multi-modal data acquired by an unmanned aerial vehicle, and the method comprises the following steps: splicing a joint Tokens with a space CLS Tokens of the same frame after feature extraction is completed by using a space Transform, and obtaining a feature fusion module; performing structure coding on the joint point sequence based on a human anatomy structure, and constructing a human topological structure association module; the method comprises the following steps of: constructing a time sequence cross Transform module; the space Transform, the feature fusion module, the human body topological structure association module and the time sequence crossing Transform module are stacked to obtain a trunk feature extraction network; a 10-layer trunk feature extraction network is adopted to perform deep feature extraction on input early-stage fusion data and feature fusion data, and classification is performed by using a classification head; the features output by the backbone network are mapped to the dimensions with the same number as the behavior categories, a prediction score is output for each category, and a human body behavior recognition result is obtained according to the prediction scores. According to the method, the human body behavior in the multi-modal data acquired by the unmanned aerial vehicle can be effectively identified.

The invention provides a speculation sampling method and device based on draft model token screening. The method comprises the following steps: a draft model performs reasoning calculation based on an input sequence to generate a plurality of candidate tokens; calculating a prediction score of each candidate token, wherein the prediction scores are used for representing the probability that the candidate tokens are accepted by the target model; screening the plurality of candidate tokens through the prediction score; inputting the screened candidate tokens into the target model for reasoning calculation again; and sampling a calculation result to generate a target token for subsequent reasoning calculation. According to the speculation sampling method and device based on draft model token screening, the sampling performance can be optimized while the reasoning result quality is ensured, the speculation sampling efficiency during large model reasoning optimization is effectively improved, and the application range is wide.

Embodiments of the present disclosure provide a scoring model training method and apparatus, an image-text pair scoring method and apparatus, a device, and a medium. The method comprises: obtaining a sample image-text pair comprising a first image description of a first language, a sample image corresponding to the first image description, and a second image description of a second language, and label data of the sample image-text pair; using the first image description to obtain a first text representation by means of a first language encoder; using the second image description to obtain a second text representation by means of a second language encoder; using the sample image to obtain a sample image representation by means of an image encoder; using the first text representation, the second text representation, and the sample image representation to obtain a first multi-modal representation by means of a multi-modal fusion network; using the first multi-modal representation to obtain a prediction score by means of a scoring network; and using a difference between the prediction score and the label data to adjust a scoring model comprising the structure, so as to perform training to obtain a scoring model capable of performing quality evaluation on an image-text pair.

The invention relates to the technical field of computers, and discloses an account risk prediction method and system based on machine learning, and the method comprises the steps: obtaining transaction details, login behaviors and terminal environment information of a target account in different time periods, and constructing an account dynamic behavior feature data set; performing multi-layer nested feature extraction on the account dynamic behavior feature data set, and constructing an account risk behavior evolution model by adopting a time sequence convolutional network and attention mechanism fusion algorithm; judging whether the risk sensitivity of the account risk behavior evolution model in the current training period is stable or not based on the trend change of the response offset; aiming at the corrected behavior sequence, applying a multi-stage decision network and an anti-factual reasoning mechanism to generate a prediction score matrix of the potential risk account; and performing multi-dimensional fusion evaluation on the current state of the account according to a misjudgment boundary correction result in combination with a preset risk control knowledge base. The method has the advantage of reducing the misjudgment rate.

The invention belongs to the technical field of information, and relates to a time sequence knowledge graph reasoning method and system for distinguishing similar historical structures. The method comprises the steps that relation reasoning paths between entity pairs are extracted, the confidence degree of each relation reasoning path is evaluated, the confidence degrees of the relation reasoning paths under different timestamps are aggregated, and relation reasoning scores are obtained; and then calculating an entity sub-graph structure score, obtaining a prediction score of the candidate entity according to the relationship reasoning score and the entity sub-graph structure score, and complementing the missing entity in the prediction query by using the candidate entity with the highest prediction score to realize time sequence knowledge graph reasoning. According to the method, similar historical structures are distinguished from the combination of the relation and the entity, modeling is carried out on the potential logic of the relation in the time sequence and the time evolution of the sub-graph structure of the entity, and candidate objects with similar sub-graph structures or similar relation connection can be distinguished; and the performance of the external reasoning task of the time sequence knowledge graph is effectively improved.

The invention discloses a doctor-patient communication risk assessment and early warning system and method based on an internet hospital, and the system comprises a basic feature calculation module which is used for calculating the basic feature score of the early warning according to the collected online inquiry data of a doctor and a patient, and calculating the weight ratio; the dynamic feature analysis module is used for taking the dynamic feature factors as input of the constructed patient end prediction model and the constructed doctor end prediction model and respectively calculating to obtain patient dynamic prediction scores and doctor dynamic prediction scores; the risk fusion prediction module is used for performing feature fusion and final risk prediction by taking the patient and doctor dynamic prediction scores, the number of times of previous complaints of the patient, the number of times of previous complaints of the doctor and the basic feature scores as input of a constructed fusion prediction model, and performing calculation to obtain a doctor-patient communication risk score; and the early warning triggering module is used for triggering an intelligent early warning mechanism when the doctor-patient communication risk score exceeds a set threshold value. According to the invention, efficient and accurate doctor-patient communication risk assessment and intelligent early warning can be realized.

The invention discloses a two-stage few-sample automatic fact checking method, electronic equipment and a storage medium, and belongs to the technical field of information processing. In order to improve the accuracy and efficiency of information verification, the method comprises the following steps: selecting a public fact verification data set to construct a test set; constructing an evidence retrieval model, and retrieving the documents of the test set by adopting the evidence retrieval model to obtain candidate documents; constructing a sentence selection model, inputting each sentence in the candidate document and the statement into a prompt template to be combined into the input of the sentence selection model, and outputting the correlation score of the sentences as an evidence sentence for statement verification; constructing a pre-training language model and finely adjusting the pre-training language model; and inputting an evidence sentence for statement verification and a statement to be verified into a prompt template to be combined into input of a pre-training language model, inputting the input into the pre-training language model after fine tuning to obtain natural language output, then mapping the natural language output to a classification label, and calculating a prediction score according to the generation probability of an output sequence to obtain a final prediction result.

The invention discloses a news detection method and system based on a dual-model collaborative optimization framework, and a medium. The method comprises the following steps: screening news materials and news descriptions from a news material library to construct a training data set; respectively inputting the training data set into a student model and a teacher network model to obtain a first image feature and a second image feature, and aligning the first image feature to a space of the second image feature to obtain a third image feature, inputting the third image feature into a decoder of a teacher network model to perform target query optimization so as to obtain a fourth image feature, taking a prediction score obtained by the teacher network model as a soft label, supervising training of a student model until a preset condition is met, and determining a trained target detection model; and inputting the news material obtained in real time into the target detection model, and determining the news category and the sensitive element, so that the news detection accuracy and efficiency can be improved.

The invention discloses a method for establishing a rough-arrangement search model based on LTR, and the method comprises the following steps: S1, collecting candidate total data, and completing the preprocessing operation; s2, the sorting score of each piece of data is calculated and arranged in an ascending order, and the data are divided into equivalent data buckets; s3, randomly extracting data to form a training sample pair, generating a Pair-wise data set, and marking a sorting relationship; s4, executing rough-arrangement search model training and calculating a prediction score; s5, constructing a loss function by adopting a Lambda gradient signal, feeding back a sorting error and iteratively updating model parameters; s6, monitoring a training performance index, stopping training after a stopping condition is met, and deploying the model to an online environment; and S7, storing the trained model file, and defining an input format, a sorting structure and an output interface. According to the method, the sorting accuracy and fine sorting consistency of the rough sorting search model are improved, and the generalization ability of the model is remarkably enhanced.

The invention provides a multi-label classification method and device for an image. The method comprises the following steps: acquiring a to-be-processed image; extracting features of the to-be-processed image by using the trained first network to obtain first features; according to the first feature and a preset second feature, obtaining a prediction score of each label; and obtaining a classification label of the to-be-processed image according to the prediction score of each label and a preset score threshold. According to the method, features of an image are extracted through a trained first network, the features extracted by the first network are processed according to preset second features used for representing semantic features of all labels and correlation information of the semantic features, and therefore prediction scores of all the labels are obtained. The method not only considers the features of the image, but also considers the relevance between the labels, thereby achieving the deep fusion of the image features and the label topology, and enabling the multi-label classification result to be more accurate.

The present invention relates to a system and method for the enhanced detection and quantification of respiratory events, such as apneas and hypopneas, particularly useful in diagnosing and managing sleep-disordered breathing conditions. This system integrates sophisticated machine learning algorithms, specifically one-dimensional convolutional neural networks (1D CNNs), with advanced signal processing techniques to analyze physiological signals. It focuses on the detecting respiratory event with a localized portion of the input segment, a novel approach that increases specificity in event detection. The system segments physiological signals into overlapping segments, each analyzed by the machine learning model to generate a prediction score. A unique aspect of this invention is the application of a dual-threshold mechanism: a ‘Model threshold’ for initial event identification and a ‘Vote threshold’ for confirming events through an aggregate voting process of overlapping segment predictions. This innovative approach ensures high accuracy and reliability in event detection and counting.

The invention relates to the field of computer vision image classification, and provides an image classification task-oriented combined incremental learning method, which comprises the following steps of: extracting image features from an image feature extractor; selecting proper prompts from an object prompt pool and a combined prompt pool for the image features, and fusing the proper prompts; the object prompt is used for guiding attribute prompt selection and fusion; acquiring a feature sequence of the training image; respectively obtaining an attribute feature, an object feature and a combination feature of the training image through Transform Encoder; classifying the attributes, the objects and the combinations by using an attribute classifier, an object classifier and a combination classifier to obtain prediction scores of the attributes, the objects and the combinations; and finally, testing the test image to obtain a final combination prediction score, wherein the combination with the highest score is a prediction classification result. The method can greatly improve the classification precision of the combined incremental image, and enables the image classification to be more accurate.

The invention discloses a bid invitation review data processing method and device, a storage medium and computer equipment. The method comprises the following steps: in response to a bid invitation signal of a target bid invitation project, obtaining to-be-reviewed data of a bidder, and determining a plurality of review indexes of the target bid invitation project; based on each review index, classifying the to-be-reviewed data to obtain to-be-reviewed data in different categories; inputting the to-be-reviewed data under each category into a preset scoring model for scoring to obtain a prediction score under each review index; determining a review terminal corresponding to each review index, and sending the to-be-reviewed data corresponding to each review index to the corresponding review terminal, so as to determine a terminal score under each review index based on the corresponding to-be-reviewed data through each review terminal; and receiving the terminal score of each review index, and determining a review result of the bidding party based on the predicted score and the terminal score under each review index.

The invention discloses a sports market demand prediction method and device based on deep learning and a medium, and relates to the technical field of market demand prediction, and the method comprises the steps: collecting sports demand data, and carrying out the preprocessing; performing relation mining and graph structure learning on the preprocessed sports demand data through a graph attention space-time network to generate a macroscopic demand potential energy graph; performing potential area identification on the macroscopic demand potential energy diagram by adopting a pre-trained sports market simulation model, and outputting local demand prediction data; performing weighted fusion and error correction on the macroscopic demand potential energy map and the local demand prediction data, and outputting a sports demand prediction score; and making a sports market demand strategy according to the sports demand prediction score and the multi-granularity demand prediction report, and transmitting the sports market demand strategy to an enterprise manager through an enterprise decision support interface. According to the method, multi-level accurate prediction and decision support of sports market demands are realized through dual-mechanism cooperation of the graph attention space-time network and the space-time convolution.

The invention relates to the field of logistics, and discloses an express distribution obstacle risk management method, device and equipment and a storage medium, and the method is used for recognizing and evaluating potential distribution obstacles in advance, can significantly improve the distribution efficiency and safety, and reduces the operation cost. The method comprises the following steps: collecting an express order and delivery data corresponding to the express order, and preprocessing the collected data to obtain preprocessed data; key features related to distribution obstacles are extracted from the preprocessed data; inputting the extracted key features into a distribution risk prediction model to obtain a risk prediction score output by the distribution risk prediction model; according to the risk prediction score, carrying out risk grade division on the express order to obtain an express order set of a plurality of risk grades; and obtaining a coping strategy corresponding to the risk level according to the risk level of each express order set, and managing the express orders in the express order set according to the coping strategy.

The invention discloses a curriculum recommendation method for two-channel comparative learning and heterogeneous knowledge graph propagation, and relates to the technical field of machine learning and recommendation systems, and the method comprises the steps: 1, constructing a curriculum heterogeneous knowledge graph and an initial entity set; step 2, cooperative propagation of the curriculum heterogeneous knowledge graph and combined generation of a ripple set; step 3, designing a fine-grained convergence attention network; step 4, performing two-channel contrast learning; 5, performing multi-hop path reasoning and multi-view fusion; step 6, prediction score calculation and model optimization; according to the course recommendation method based on dual-channel comparative learning and heterogeneous knowledge graph propagation, the technical problems of data sparsity, insufficient coverage of long-tail courses, poor interpretability of recommendation results and the like in the prior art are solved.

The invention discloses a method for detecting the content of salidroside in a rhodiola rosea extracting solution based on artificial intelligence. The method comprises the following steps: S1, performing spectral scanning of different wavebands on an extracting solution sample in a plurality of near-infrared wavelength ranges; s2, performing multi-dimensional interference suppression processing and signal purification; s3, carrying out spectral feature compression calculation to obtain a salidroside feature intensity factor; s4, carrying out salidroside characteristic signal-to-noise ratio enhancement and purity index calculation; s5, predicting the content of salidroside and outputting a content prediction score; and S6, mapping the content prediction score into the final salidroside content. According to the method, spectral signal purification, nonlinear feature extraction, dynamic noise enhancement, artificial intelligence prediction modeling and a secondary recheck mechanism are utilized to realize adaptive analysis of extracting solutions from different sources, so that the detection accuracy and stability are remarkably improved, and rapid and traceable salidroside content detection can be realized under the condition that a large precise instrument is not needed.

The invention provides a recommendation model training method and device, a recommendation method and device and electronic equipment. The recommendation model training method comprises the steps that parameters in a recommendation model are initialized, the recommendation model structurally comprises a probability space output unit and an expert network, and the expert network is used for predicting at least one task; a sample used for training a recommendation model is obtained, target object features in the sample are input into a probability space output unit to obtain a task probability space, and the sample is correspondingly marked with a label corresponding to at least one task; inputting the target object features and the object features in the sample into an expert network to obtain a prediction score corresponding to the at least one task; and sampling at least one task based on the task probability space, and updating parameters in the recommendation model based on the predicted score corresponding to the sampled task and the tag corresponding to the sampled task.