56 results about "Relevance prediction" patented technology

Computer-implemented techniques for multimodal content relevance prediction using neural networks involves processing multimodal content comprising a digital image and text. Initially, dense embeddings are obtained: an image embedding from a pretrained convolutional neural network, and a text embedding from a pretrained transformer network. These embeddings encapsulate the features of the image and text respectively. Two pretrained dense neural sub-networks then reduce the dimensionality of these embeddings. A third dense neural sub-network determines a numerical score for the multimodal content using the reduced embeddings and an additional feature embedding. This score reflects various aspects of the multimodal content, leading to an action taken based on this numerical evaluation, providing a comprehensive and nuanced understanding and management of multimodal digital content.

The invention provides a mine disaster monitoring and early warning method. The method comprises the following steps: acquiring disaster monitoring data of a mine; preprocessing the collected disaster monitoring data, and matching a corresponding optimal relevance prediction analysis model according to a current task target; feature extraction is carried out on the preprocessed disaster monitoring data, and then the preprocessed disaster monitoring data are input into a corresponding optimal relevance prediction and analysis model to carry out disaster prediction and relevance analysis between disasters; acquiring a disaster prediction result, a disaster correlation analysis result and a disaster solution suggestion result output by the optimal correlation prediction analysis model, and performing visual output; according to the invention, various influence factors of mine disasters and relevance among the disasters are comprehensively considered, comprehensive monitoring, early warning and control of the mine disasters are realized, effective solution suggestions can be given for different disaster conditions, and the accuracy of disaster early warning, risk assessment and control management is improved; the method is suitable for the mine disaster monitoring and control field.

The invention discloses a time-varying channel estimation method and device based on correlation prediction in an RIS auxiliary system, and the method comprises the steps: assuming that the working mode of the RIS is an ON or OFF mode, and obtaining time domain cascade channel estimation at a historical moment at a base station side; constructing a training sample set by using time domain cascade channel estimation at historical moments; randomly initializing network parameters, and training the long and short-term memory neural network by using the training sample set to obtain a network model with an optimal weight and threshold; predicting the time correlation of the channel based on the trained network model; generating an AR random model by using the time correlation of the channel, and constructing a state space model of the Kalman filter based on the AR random model and the time domain receiving signal; time-varying channel estimation is carried out based on the state space model of the Kalman filter, and cascade channel estimation passing through each RIS unit is obtained. The method is suitable for high-precision acquisition of the time-varying channel information in the RIS-assisted mobile communication system.

Intelligent device for predicting macroeconomic indicators using artificial intelligence (AI)-assisted data analysis, comprising: a real-time data collection unit configured to collect economic data from a variety of data sources, including government reports, market data, social media trends, and financial transactions; an AI-driven processing unit integrated with the real-time data collection unit, wherein the AI-driven processing unit applies machine learning techniques to analyze the collected economic data and identify patterns and correlations between economic variables; a predictive analysis module communicatively coupled to the AI-driven processing unit and configured to generate macroeconomic forecasts based on the analysis of the economic data; a secure communication interface configured to transmit the macroeconomic forecasts to authorized users via a cloud-based platform; and a user interface that presents the macroeconomic forecasts and allows users to interact with the system to explore different economic scenarios and adjust the forecast parameters, with the system housed in a protective enclosure designed to ensure optimal performance, including heat dissipation and power efficiency.

Embodiments of the present disclosure provide systems and methods for emotion-based call summarization. One method may include receiving an emotion prediction vector for an utterance text segment from a transcript data object, the emotion prediction vector comprising a plurality of emotion prediction scores respectively corresponding to a plurality of emotion identifiers; generating a domain-specific relevancy prediction for the utterance text segment based on a category-relevant subset of the plurality of emotion prediction scores that correspond to one or more category-specific emotion identifiers of the plurality of emotion identifiers associated with a domain-specific summarization category; identifying the utterance text segment as a relevant utterance from the transcript data object based on a comparison between the domain-specific relevancy prediction and a relevancy threshold; and initiating a performance of a machine learning summarization operation based on the utterance text segment.

The invention discloses a subway short-time OD passenger flow prediction method based on a multi-knowledge graph neural network model. Firstly, based on multi-source data such as subway station position data, AFC data, POI data and mobile phone signaling data, a subway network structure, passenger flow characteristics and subway station surrounding land and population characteristics are quantified, key OD pairs are extracted, and the influence of low-value and random data on a model is prevented; secondly, constructing spatial connectivity, travel mode similarity and function similarity among a plurality of knowledge graph capture nodes; meanwhile, constructing a spatial feature modeling module, and capturing a potential spatial relationship between nodes by using a graph neural network; and finally, constructing a time feature modeling module, and capturing a passenger flow dynamic change process by using a time sequence modeling method. According to the prediction method, complex spatial-temporal correlation existing in subway passenger flow is effectively and quantitatively captured, and the prediction capability and the interpretability of the model are remarkably enhanced.

The application discloses a multi-agent collaborative perception feature enhancement method based on context aggregation, relates to the technical field of automatic driving and intelligent networked vehicles, and comprises the following steps: acquiring current frame features and at least one frame of historical features; adaptively aligning the historical features based on a motion prediction network and a deformable convolution offset; generating semantic consistency weights and identifying time sequence discontinuous regions based on a correlation prediction network; fusing the current and historical features according to the weights and carrying out state space selective scanning, pooling, denoising and aggregation; generating multi-scale features, combining scene complexity, quality evaluation and time sequence consistency constraints to determine scale weights and then fusing; and inputting an LSTM to perform time sequence modeling and output enhanced context features. Invalid alignment is avoided through motion compensation and semantic consistency constraints, noise and scale jitter are suppressed through position-level denoising and multi-scale adaptive smoothing, and the collaborative perception robustness and detection accuracy are improved.

The disclosure provides a relevance prediction model training method and device, and an abstract generation method and device. The relevance prediction model training method comprises: extracting a first semantic feature vector of a first sentence sample and a second semantic feature vector of a second sentence sample; generating a training sample, wherein the training sample comprises the first semantic feature vector and the second semantic feature vector, and a preset semantic relevance label of the first sentence sample and the second sentence sample; inputting the training sample into a machine learning model to obtain a semantic relevance prediction result of the first sentence sample and the second sentence sample; determining a loss function according to the semantic relevance label and the semantic relevance prediction result; and training the machine learning model by using the loss function to obtain a relevance prediction model.

The present application discloses a laparoscopic image segmentation method, system and computer storage medium. The method includes: acquiring a laparoscopic image; inputting the laparoscopic image into a segmentation model to obtain a corresponding segmentation map. The segmentation method of the segmentation model for the laparoscopic image includes: extracting hierarchical semantic features and spatial detail features of the laparoscopic image; fusing the hierarchical semantic features and the spatial detail features to obtain a correlation prediction matrix; using the correlation prediction matrix to optimize the fused features after the fusion of the hierarchical semantic features and the spatial detail features to obtain an optimized feature map; performing semantic segmentation on the optimized feature map to obtain a segmentation map. The present application uses a dual-path feature extraction technology to solve the problem of information loss in a single-path network, and uses a context information guidance technology to model category correlations, and uses this information to optimize the features extracted by the dual-path, improving the segmentation accuracy of laparoscopic images and being particularly suitable for the segmentation of laparoscopic images.

An implicit semantic association modeling method, a zero-shot image classification method and apparatus of the present invention belong to the technical field of image processing. The zero-shot image classification method includes the following steps: Step 1, obtain a test image and a source image, where the test image is an unlabeled target image and the source image is a labeled image; Step 2, use the source image for model training to obtain a semantic correlation prediction model g(*); Step 3, calculate the implicit semantic representation of the test image; Step 4, calculate the semantic association matrix H between the test image and each source image i,t ; Step 5, use the semantic correlation prediction model g(*) to predict the semantic consistency between the test image and each source image, and classify the test image into the category with the largest average semantic consistency. The present invention performs excellently on multiple public data sets and continuously improves the classification accuracy, and has good discriminative power and generalization ability.

A device receives comment information that is associated with users and includes comments provided by the users, about an entity, via social media sources, and receives transaction information that is associated with the users and includes financial transactions of the users with the entity. The device determines correlations between the comment information and the transaction information, where the correlations between the comment information and the transaction information provide weights to the comment information to generate weighted comment information. The device generates a prediction about a future stock price of the entity based on the weighted comment information, the transaction information, and the correlations between the comment information and the transaction information, and provides the prediction about the future stock price of the entity for display.

Automated content recommendation is used in conferencing. In one embodiment, a system receives a list of content recommendation actions. The system receives a number of utterances associated with the participants in real time. For each utterance, the system determines whether a prediction of relatedness is present between the utterance and one or more trigger phrases associated with a content recommendation action. Upon determining that a prediction of relatedness is present, the system performs the associated content recommendation action by transmitting, to one or more client devices, one or more pieces of content to be recommended.

The invention discloses a model training method, a knowledge retrieval method, electronic equipment and a storage medium. The model training method comprises the following steps: inputting a first user question of a user, first context information of the first user question and a first knowledge entry into a first model to obtain predicted correlation between the first user question and the first knowledge entry; wherein the first context information is obtained from historical dialogues with the user, and after the first model encodes the first user question, the first context information and the first knowledge item, a coding vector of the first user question and a coding vector of the first context information are fused to obtain a first fusion vector; performing correlation prediction based on the first fusion vector and the coding vector of the first knowledge entry to obtain predicted correlation; parameters of the first model are adjusted based on the predicted correlation and a reference correlation between the first user question and the first knowledge entry.

The embodiment of the invention provides a traffic flow prediction method and device, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring traffic flow data of a plurality of position nodes to generate an initial flow matrix, carrying out data optimization on the initial flow matrix to obtain an input flow matrix, inputting the input flow matrix into a correlation prediction sub-model for correlation analysis to obtain a first output matrix, and outputting the first output matrix to a second output sub-model; performing data processing on the input traffic matrix by using the gated convolutional sub-networks to obtain corresponding first gated data, obtaining second gated data according to the two pieces of first gated data, inputting the second gated data and the input traffic matrix into the graph convolutional network together for data processing to obtain a first output matrix, and outputting the first output matrix to the graph convolutional network; and inputting the first output matrix into an output prediction layer for data processing to obtain a target traffic matrix corresponding to the position node. Prediction is carried out based on the bidirectional spatial correlation and the temporal correlation obtained by the traffic flow prediction model, and the accuracy of traffic flow prediction is improved.

An approach for product localization is described that includes detecting one or more movement activities of a user in an indoor environment, and determining a respective position for each of the one or more movement activities using a sensor associated with the user. The approach further includes identifying one or more stoppage locations from the detected one or more movement activities, and receiving a list of one or more purchased items purchased by the user in the indoor environment. Finally, the approach further includes correlating at least one of the more purchased items with at least one of the one or more stoppage locations, and predicting the location of the one or more purchased items based on the correlating.

This invention belongs to the field of image segmentation technology and discloses a method, system, medium, device, and terminal for extracting identification documents. The system utilizes an embedded device to acquire multispectral images of the identification document; it models the document image edge segments based on their straight-line geometric properties and infers from the contextual information of the global image; it extracts global image features using a ResNet network and then encodes them using a Deformable DETR encoder; in the first-stage decoding process, it predicts the document edge segments using an attention mechanism and learnable line segment and position queries; in the second-stage decoding process, it compares the correlation between line segment features and image features to predict the relative order between line segments, and obtains the complete edges, vertices, and bounding boxes of the document image through perspective transformation. This invention uses a two-part matching method to predict line segments, avoiding pre- and post-processing, simplifying the detection channel, and achieving true end-to-end processing.

The application provides a point cloud encoding method based on a multi-level spherical octree and a graph-driven attention entropy model, comprising: encoding point cloud data using an octree entropy model based on multi-level spherical coordinates; constructing an adjacency matrix of a parent graph and a distance graph; using a graph convolution network module to embed context information with the aid of the adjacency matrix; constructing a grouping graph attention module and a cross attention module to learn the relevance of parent node context and sibling node context; predicting the probability of each octree node placeholder symbol; compressing the placeholder symbol sequence into a binary floating point number sequence and converting it into a bit stream; converting the bit stream into an octree placeholder symbol sequence, reconstructing the octree and restoring the point cloud. The application combines multi-level spherical coordinate octree structure, graph convolution, grouping graph attention module and cross attention module, reduces quantization error, improves compression efficiency, balances calculation complexity and compression effect, and significantly enhances the adaptability to high-resolution point cloud data.

The present application provides a kind of shale oil horizontal well volume fracturing ultimate recoverable reserves prediction method, first based on the reservoir geological parameters of the horizontal well to be measured and adjacent horizontal well in the same block, establish productivity prediction model, calculate effective fracture network sweep efficiency using reservoir numerical simulation method, and establish effective fracture network sweep efficiency and maximum recoverable reserves correlation prediction chart;Secondly, the correlation coefficient between the geology and volume fracturing reconstruction parameters of the horizontal well to be measured and the fracture network sweep volume is calculated using grey relational analysis method, and the key control parameters affecting fracture network sweep volume are determined;Finally, the fracture network sweep volume prediction model coupled with key control parameters is established, the fracture network sweep volume of the horizontal well to be measured is obtained, the effective fracture network sweep efficiency is obtained, and the maximum recoverable reserves of the horizontal well to be measured is obtained using the correlation prediction chart. The method can quickly predict the maximum recoverable reserves of any horizontal well by establishing the correlation chart of effective fracture network sweep efficiency and maximum recoverable reserves.

The present disclosure relates to field of wireless communication network that discloses method and system for configuring initial congestion window value in User Equipment (UE (201)). UE (201) determines for each of one or more applications running in UE (201), user experience parameters and UE (201) network parameters. Further, UE (201) predicts using trained Artificial Intelligence (AI) model (203), an optimal initial congestion window value for each of one or more applications based on correlation of user experience parameters and UE (201) network parameters corresponding to each of one or more applications. Finally, UE (201) configures optimal initial congestion window value for each of one or more applications based on prediction. The present disclosure helps in reducing Flow Completion Time (FCT) of each application by predicting optimal initial congestion window value dynamically.

Dynamic conversation alerts are provided within a communication session. In one embodiment, the system presents, to a client device associated with a user of a communication platform, a user interface (“UI”) including a prompt for the user to submit one or more alert phrases, each alert phrase being associated with a category; receives, from the client device, a list of submitted alert phrases; and receives a transcript of a communication session between participants. For each utterance in the transcript, the system determines whether one or more predictions of relatedness are present between the utterance and one or more alert phrases from the list of submitted alert phrases. The system then transmits, to the client device, a list of related categories, each related category including one or more timestamps of utterances for which a prediction of relatedness is present for an alert phrase associated with that category.

The invention discloses a method for predicting the minimum miscible pressure of CO2 and crude oil by using machine learning, and the method comprises the steps: carrying out the high-precision modeling based on a linear SVM algorithm, building a correlation prediction formula, and constructing a correlation prediction model; and training and optimizing the correlation prediction model by using the collected data, and establishing a stacked integrated model in combination with a random forest regression model RF and the correlation prediction model to realize accurate prediction of the CO2-crude oil minimum miscible pressure MMP. According to the method, the time and the cost for determining the CO2-crude oil MMP value can be reduced to the maximum extent. Therefore, a reliable CO2-crude oil MMP value can be provided through prediction, and the miscible phase type of CO2 oil displacement is determined, so that the design of a CO2 oil displacement scheme is improved, the optimal design of an injection-production scheme is guided, and the crude oil recovery rate is increased.

The invention discloses a semantic correlation prediction model, method and device, a storage medium and computer equipment, and relates to the technical field of Internet. The models comprise a student model and an online prompt model, and the student model comprises a parameter sharing twin-tower network used for performing feature extraction on a first keyword and a second keyword to be predicted to obtain a first keyword feature and a second keyword feature; the thinking chain tower network is used for carrying out feature extraction on thinking chain prompt information generated by the online prompt model to obtain thinking chain features, and parameters of the thinking chain tower network and parameters of the double-tower network are not shared; the expert hybrid network is used for carrying out feature fusion on the first keyword feature, the second keyword feature and the thinking chain feature to obtain a keyword fusion feature; and the deep neural network is used for performing semantic correlation prediction based on the keyword fusion features to obtain a semantic correlation prediction result of the first keyword and the second keyword. The above model can improve the accuracy of correlation prediction.

The application belongs to the technical field of audio processing, and a plurality of pre-trained audio text expert models are introduced to construct a multi-expert audio text semantic representation. Meanwhile, the consistency relationship between audio and text at the overall semantic level and the semantic inconsistency between audio and text that is perceptually significant to humans are modeled through a semantic alignment branch and a semantic mismatch branch respectively, and a multi-branch correlation score fusion mechanism is used to output an audio text correlation prediction result that is highly consistent with human subjective evaluation. This method can simultaneously consider semantic consistency and perceptual differences, effectively achieving more accurate and more human subjective evaluation standard-compliant audio text correlation evaluation. Meanwhile, based on the above method, the application constructs an audio retrieval system that evaluates and sorts the correlation between a text query and audio samples in an audio library to realize an audio retrieval function oriented to natural language description.

In some implementations, the techniques described herein relate to a method including: classifying motion data and network device usage data in a networked environment; integrating the classified motion data and the network device usage data to create a combined dataset; processing the combined dataset to identify correlations between the motion data and the network device usage data; and using the identified correlations to predict an occupancy state of the environment.

Methods and systems provide for dynamic conversation alerts within a communication session. In one embodiment, the system presents, to a client device associated with a user of a communication platform, a user interface (“UI”) including a prompt for the user to submit one or more alert phrases, each alert phrase being associated with a category; receives, from the client device, a list of submitted alert phrases; and receives a transcript of a communication session between participants. For each utterance in the transcript, the system determines whether one or more predictions of relatedness are present between the utterance and one or more alert phrases from the list of submitted alert phrases. The system then transmits, to the client device, a list of related categories, each related category including one or more timestamps of utterances for which a prediction of relatedness is present for an alert phrase associated with that category.

The present application relates to a method, apparatus, computer device, and storage medium for determining the access power of sensors. The method includes: obtaining sensor data within a preset time period, inputting the sensor data within the preset time period into a trained correlation prediction neural network, and outputting a prediction matrix, where each element in the prediction matrix includes the occurrence probability of a corresponding emergency event type and the correlation coefficient between the corresponding emergency event type and the corresponding sensor type; determining a target emergency event and a target sensor according to the occurrence probability of the corresponding emergency event type and each correlation coefficient; and determining the scheduling decision and access power of the target sensor through an optimal decision formula. Using this method can improve the accuracy of sensor access and reduce the energy consumption of sensor access.

A computing system for generating structural information of a biomolecule, comprising: a processor; and one or more non-transitory computer-readable media having stored thereon instructions that, when executed by the one or more processors, cause the computing system to receive training inputs, process the training inputs with a machine-learned biomolecule prediction model, evaluate a loss function, and modify parameters of the machine-learned model. A computing system for generating structural information of a target biomolecule, comprising: a processor; and one or more non-transitory computer-readable media having stored thereon a machine-learned biomolecule prediction model and instructions that, when executed by the one or more processors, cause the computing system to receive a target input and predict structural information of the target biomolecule. 1. A computing system for predicting affinity of a target biomolecule, comprising: a processor; and one or more non-transitory computer readable media comprising: a machine-learned affinity prediction artificial neural network comprising: (i) one or more biomolecular prediction layers trained to predict biomolecular structural information from a target input; (ii) one or more docking layers trained to generate docking complexes from two or more input three-dimensional biomolecules; and (iii) one or more affinity prediction layers trained to predict affinity from the input docking complexes, wherein the one or more biomolecular prediction layers, the one or more docking layers, and the one or more affinity prediction layers are connected; and instructions, when executed by the one or more processors, that cause the computing system to receive a target input and process the target input using the affinity prediction artificial neural network to generate a docking complex corresponding to the target input and a corresponding structural affinity value.

The application realizes a stream music recommendation method for removing attention deviation based on reward correction through the method in the network security field. The core of the method contains three modules: a reward correction model, an attention prediction module and a correlation prediction module. The reward correction module obtains the unbiased reward after correction by combining the predicted user attention and correlation through the importance sampling method; the attention prediction module models the probability of user attention to each song; the correlation prediction module is used to predict the preference of the user to each candidate song, and the parameter is updated based on the corrected reward. The application eliminates the attention deviation in the user feedback to obtain unbiased reward and improve the prediction accuracy of the model.

This application discloses a method, apparatus, medium, device, and program product for processing pre-training data, relating to the field of artificial intelligence technology. The method includes: acquiring an initial dataset for a target knowledge domain, the initial dataset comprising multiple pre-training data retrieved for the target knowledge domain; performing data quality prediction on the pre-training data in the initial dataset based on a first evaluation model to obtain a quality evaluation result; performing domain relevance prediction on the pre-training data in the initial dataset based on a second evaluation model to obtain a domain relevance prediction result, the relevance prediction result indicating the relevance between the pre-training data and the target knowledge domain, the first and second evaluation models being constructed based on a large-scale language model; filtering the initial dataset based on the quality evaluation result and the relevance prediction result to obtain an intermediate dataset; and performing fine-tuning and filtering on the intermediate dataset to obtain a target pre-training set. This application can efficiently acquire a large amount of high-quality pre-training data.

This disclosure relates to a training method for a video recognition model, a video recognition method, and related equipment. The method includes: acquiring a video sample set, wherein the video samples in the video sample set include display data, audio data, and correlation annotation data between the display data and the audio data; extracting display text data from the display data; performing speech recognition on the audio data to obtain playback text data; inputting the display text data and the playback text data into a first network structure of the video recognition model to obtain a text relation vector; inputting the text relation vector into a second network structure of the video recognition model to obtain correlation prediction data; and training the network parameters corresponding to the second network structure based on the target loss determined by the correlation prediction data and the correlation annotation data to obtain a video recognition model that meets preset conditions. The obtained trained video recognition model can quickly and accurately recognize videos.