43 results about "Recall rate" patented technology

Embodiments disclosed herein relate to a method, a device, and a computer program product for training an image classification model. The method includes: determining a first classification result obtained by the image classification model on pixel points of a sample image in a first training iteration, wherein the first classification result indicates that each of the pixel points belongs to one of a plurality of object classes; determining a second classification result obtained by the image classification model on the pixel points of the sample image in a second training iteration; determining, based on the first classification result and the second classification result, a first set of recall rates and a second set of recall rates for the plurality of object classes; and adjusting, based on the first set of recall rates and the second set of recall rates, the image classification model to obtain a trained image classification model.

The invention provides an evaluation optimization method and device for recall results of a knowledge question-answering system, equipment and a medium, and the method carries out finer-grained position matching on labeled document fragments and recall document fragments based on a dynamically adjusted threshold value and a constructed document tree, thereby carrying out precise evaluation on the knowledge question-answering system. The anti-interference capability of the system is evaluated by introducing a negative sample problem, so that the robustness of the knowledge question-answering system to noise data is improved; and by introducing the similarity between the original document fragment vector and the annotated document fragment vector, the problem of low recall rate caused by incompleteness of the annotated document fragment is avoided.

The invention discloses a policy text analysis method and device, computer equipment and a storage medium. The method comprises the steps of obtaining policy information to be analyzed; analyzing the policy information to be analyzed in a data enhanced recall mode to obtain retrieval recall data; performing task splitting on the policy information to be analyzed to obtain a plurality of sub-tasks; determining an analysis agent matched with the plurality of sub-tasks, and analyzing the retrieval recall data in parallel through the analysis agent to obtain sub-task candidate results of the plurality of sub-tasks; and integrating the plurality of sub-task candidate results to obtain an analysis result of the policy information to be analyzed. According to the method and the device, through various data enhancement recall modes, the recall rate of the actual associated text blocks is effectively improved on the data set after data enhancement.

Methods, apparatus, systems, and articles of manufacture for the generation and application of models for immunotherapy prediction and treatment are disclosed. A balance of accuracy and recall rate may be applied to at least one of a toxicity-related model or a therapeutic effect-related model to configure an immunotherapy treatment of a patient and / or a group of patients. Model outputs may be evaluated differently depending on determining patient selection criteria to trigger different actions.

The embodiment of the invention discloses a problem shunting method, device and equipment. In the embodiment of the invention, user question information is obtained; inputting the user question information into a preset high-recall-rate word library, and determining a comparison result; responding to the comparison result that the user question information comprises the same vocabulary in the high-recall-rate word bank, and determining the user question information as risk question information; inputting the risk problem information into a preset dichotomy model, and outputting a classification result; and in response to the fact that the classification result is high risk, determining that the risk problem information is high-risk problem information, and intercepting the high-risk problem information. According to the method, question shunting can be accurately and efficiently carried out, and the user experience is improved.

Systems and methods for improving the accuracy and recall rate of prototype networks for sound event detection. A method of training a prototype network for sound event detection includes: receiving samples of an audio signal, the samples including positive samples corresponding to sound events and negative samples not corresponding to sound events; determining corresponding positive prototypes of the plurality of sound event categories based on the positive samples; determining, based on the negative samples, a respective negative prototype for a respective negative sample group, each negative prototype corresponding to a combination of a plurality of negative samples; and generating an output signal indicating whether the first sample belongs to one of the plurality of sound event categories based on a comparison between the first sample and the respective positive prototype and each negative prototype.

The invention provides a method and system for improving RAG recall rate and accuracy based on block global relevance, and the method comprises the steps: detecting a newly-added document through timing scheduling, carrying out block processing, and fusing an explicit structure relation and an implicit semantic relation to construct a weighted adjacency matrix; block global correlation scores are calculated through an iterative algorithm; the query similarity and the global correlation score are dynamically fused, and Top-K blocks are returned to generate answers; the system comprises a knowledge block graph construction server, a global correlation calculation server, a fusion retrieval server and a data storage server. According to the method, the limitation that traditional RAG only depends on local similarity is broken through, the core knowledge recall rate is remarkably increased, redundant noise is restrained, and the accuracy and logicality of generated answers are improved by quantifying the global importance of the blocks in the knowledge network.

The application discloses a strategy determination method and device and related equipment, and relates to the technical field of computers. The method comprises the following steps: determining a first recall rate and a first injury rate corresponding to a first strategy based on a classification result of the first strategy; the first strategy comprises at least one sub-strategy; different sub-strategies in the at least one sub-strategy correspond to different preset strategy values, and the preset strategy values are used for determining a classification label; the recall rate is the proportion of correct classification labels determined by a strategy, and the injury rate is the proportion of incorrect classification labels determined by a strategy; at least one iteration is performed on at least part of the sub-strategies in the first strategy, and the iteration is used for adjusting preset strategy values corresponding to the at least part of the sub-strategies; and a second strategy after iteration is obtained under the condition that a preset condition is met. The method improves the classification accuracy of the second strategy after iteration optimization.

The application discloses a kind of lightweight explainable aviation equipment abnormal state perception method and system, belong to aircraft health management and deep learning technical field.The application constructs 1+1 branch's LiteInception light weight feature extraction network, designs and aligns two-stage cascaded diagnosis process with aviation maintenance process: first stage adopts LiteInception and hybrid architecture of Transformer to execute fault detection binary classification, and priority is guaranteed recall rate;Second stage adopts pure LiteInception architecture to execute fault identification multi-classification to abnormal sample, and priority is guaranteed accuracy and explainability.The application significantly compresses model size, adapts edge device real-time inference needs, can output the diagnosis evidence chain that can be traced to specific sensor, specific time period, considers diagnosis efficiency, precision and compliance, and provides complete solution for general aircraft fault diagnosis.

The application discloses a Chinese human phenotype ontology recognition method and related equipment, which can be applied to the technical field of text information processing. The application can effectively improve the recall rate by expanding the human phenotype ontology concept synonym set through a large language model, generating an expanded dictionary with the initial dictionary, significantly enhancing the discrimination ability of the recognition model for semantically ambiguous concepts through two-stage comparative learning, balancing the precision and recall rate by constructing an initial dictionary based on the human phenotype ontology official database and the Chinese human phenotype ontology official data, merging and disambiguating the current input text after accurate matching and semantic similarity matching to obtain a fused candidate result list, and finally obtaining the structured recognition result corresponding to each current input text by post-processing each candidate fragment in the fused candidate result list, which can provide effective data support for disease diagnosis.

The embodiment of the invention provides an auxiliary proof-giving method, an auxiliary proof-giving system, electronic equipment, a storage medium and a computer program product. The auxiliary proof-proof method comprises the following steps: acquiring right protection request content corresponding to a to-be-proof right protection event; performing case analysis on the right protection request content to obtain a right protection category positioning result matched with the right protection event; retrieving a preset checking template based on a right protection category positioning result to obtain a proof item list for the right protection event; and executing a verification task corresponding to the proof item list based on a mode of iteratively retrieving a preset knowledge base, and obtaining a proof suggestion report corresponding to the right protection event for guiding the user to provide a proof. According to the method, an iterative autonomous retrieval mechanism is driven by a task, an analysis process of manual gradual evidence collection is simulated through multiple rounds of query, and compared with a manual dominant evidence collection process, the evidence collection efficiency is improved, and the recall rate and the accuracy of key evidence are effectively improved.

The present application relates to a credit report generation method and device based on a multi-index context bus, applied to the field of artificial intelligence technology, comprising: generating micro-context through "entity-spatiotemporal-semantic" three-dimensional filtering, physically isolating noise data, effectively solving the "lost in the middle" effect of large model long text input, significantly improving the recall rate and reasoning accuracy of key risk information; using a "configurable expert skill body" to replace full-parameter fine-tuning, decoupling business logic and model, and realizing instant effect by updating the configuration only for credit policy adjustment, avoiding catastrophic forgetting and reducing model maintenance cost; using pointerized context transmission and life cycle management to greatly reduce memory occupation and IO overhead, effectively supporting high-concurrency credit reasoning scenarios.

The invention discloses a target detection anchor frame alignment degree optimization and recall rate improvement method based on RPA, and the method comprises the following steps: S1, obtaining an application program interface screenshot through RPA software, obtaining an element frame in the interface screenshot through element capture, carrying out the annotation of frame data of the element frame, and carrying out the annotation of the frame data of the element frame; training a target detection model by using the frame data and the interface screenshot; s2, performing target detection on an upper frame of the interface screenshot by using the trained target detection model to obtain a detection frame and a corresponding category and confidence; comparing the confidence of the detection frames with a set confidence threshold, and retaining the detection frames greater than the confidence threshold; the anchor point elements of the webpage are dynamically recognized through the webpage screenshot, the element coordinates and the element types, and the method has the advantages of being wide in application range and reliable in recognition.

This application discloses a data request method, apparatus, system, server, and storage medium. The method includes: sending a received data request to multiple data request nodes, so that each data request node can query the requested data in its respective data domain. If the request time does not exceed a target time, the recall rate corresponding to the current data query detection is obtained; based on the recall rate, it is determined whether to discard the target node; if the target node is not discarded, it is determined whether to trigger the target node to re-query the data; if it is determined to re-query the data, the target node is triggered to re-query the data and enter the next data query detection. Thus, the above-mentioned method enables the overall requested data to be completed within the target time, and can further improve the stability and integrity of the requested data.

This disclosure provides a category-level recall control method, device, electronic device, and storage medium, involving natural language processing and deep learning technologies in the field of artificial intelligence. It can be applied to multi-category classification scenarios in high-risk businesses such as content security review, financial risk control, and public opinion monitoring. The specific implementation scheme is as follows: Input samples are classified based on a pre-set multi-category classification model to obtain calibration scores for each category on the input samples; based on pre-configured target recall rates for each category, and combined with a pre-built mapping table of recall rates and thresholds, a standard threshold corresponding to the target recall rate of each category is determined; the category with the highest calibration score among the multiple categories is selected as the candidate predicted category for the input sample; based on the candidate predicted category, a standard threshold for the candidate predicted category is determined from the standard thresholds corresponding to the target recall rates of each category; based on the calibration score of the candidate predicted category and the corresponding standard threshold, it is determined whether to recall the input sample.

The application provides an abnormal waybill detection method and device. The abnormal waybill detection method comprises the following steps: obtaining a plurality of to-be-predicted waybill information; determining a plurality of target matching conditions met by each to-be-predicted waybill information based on the plurality of to-be-predicted waybill information and a plurality of preset matching conditions; determining an abnormal probability value of each to-be-predicted waybill information belonging to an abnormal waybill based on each to-be-predicted waybill information and the plurality of target matching conditions met by each to-be-predicted waybill information; and performing abnormal detection on the plurality of abnormal probability values to obtain an abnormal value in the plurality of abnormal probability values. Compared with the prior art, the application improves the prediction accuracy by only relying on waybill information. In addition, after obtaining the abnormal probability value of each to-be-predicted waybill information, the application performs abnormal detection on the plurality of abnormal probability values, thereby identifying abnormal waybills, improving the recall rate of prediction, and thus improving the accuracy and recall rate of abnormal waybill detection.

Some embodiments include systems and methods for recall estimation. An exemplary method comprises determining reservoir sampling and size of labeling from each strata; determining recall and variance for each strata; determining sum of sample size for a time period; and determining reservoir sampling for each strata and label the sample items. Other embodiments are described.

The present application discloses a method and device for determining a personnel file cover, which relates to the field of artificial intelligence technology and is used to provide an accurate personnel file cover to implement personnel archiving and improve the precision and recall rate of the personnel archiving system. The method comprises: obtaining a target image set of a first person, wherein the target image set comprises a plurality of target images, and the target images are images representing the characteristics of the person; clustering the plurality of target images based on the type of the personnel characteristics to obtain a plurality of class centers; wherein one type of personnel characteristics corresponds to at least one class center; and determining the file cover of the first person according to the plurality of class centers.

A labeling data quality control method under a multi-person cooperation scene comprises the steps that samples are selected from a to-be-labeled data set, then labeling personnel label the selected samples to obtain sample labeling results, the data set is labeled with reference to the samples, the accuracy rate, the recall rate and the F1 score of the labeling result of each labeling personnel are calculated, and the accuracy rate, the recall rate and the F1 score of the labeling result of each labeling personnel are calculated; according to the accuracy rate, the recall rate and the F1 score, the labeling personnel class is divided into qualification labeling personnel and general labeling personnel, and task allocation is dynamically performed on the qualification labeling personnel and the general labeling personnel; by the adoption of the method, the problem that attention of annotation personnel is reduced in the continuous annotation process is solved, so that the annotation quality is improved, and the error rate and the rework cost are reduced.

The invention provides a man-machine interaction method and system, a server, a storage medium and a program product. The method relates to the field of man-machine interaction, and supports a user to construct a corresponding customized word bank through a client, and the customized word bank comprises at least one customized word corresponding to a standard word. In the question and answer process, in response to the received query information sent by the client, replacing customized words in the query information with corresponding standard words based on the customized word bank corresponding to the client to obtain rewritten query; according to the method, retrieval is carried out in the knowledge base according to the rewriting query, knowledge matched with the rewriting query is obtained, the knowledge needed for reply generation can be recalled accurately according to habit use preferences of different users, and the recall rate of the knowledge is increased; furthermore, the reply information is generated according to the knowledge matched with the rewriting query, so that the quality of the generated reply information can be improved, and the question and answer quality is improved.

The invention discloses a semantic enhanced vulnerability information retrieval method and related equipment. The method comprises the following steps: acquiring a vulnerability retrieval request of a user; the vulnerability retrieval request of the user is input into the trained Sensor Transform model to be subjected to vectorization processing, and a vulnerability retrieval request vector of the user is obtained; and performing semantic similarity calculation on the vulnerability retrieval request vector of the user and a vulnerability vector set pre-stored in the vulnerability retrieval database by utilizing an ElasticSearch engine to obtain a vulnerability retrieval result. According to the method, semantic related results can be accurately understood and retrieved, and the retrieval accuracy and recall rate are remarkably improved.

This application relates to the field of data processing technology, and provides a method and related apparatus for selecting recall strategies, which selects suitable recall strategies for specific users of an application to improve the recovery of users with low activity. The method mainly includes: determining the number X of recall strategies in the recall strategy set; dividing the sample user group into X sub-sample user groups, where X is a positive integer greater than 0; for each of the X sub-sample user groups, pushing the same recall strategy from the recall strategy set to sample users within the same sub-sample user group; calculating the recall rate for each sub-sample user group based on the number of users responding to the recall strategies in the recall strategy set within each sub-sample user group; and selecting target recall strategies that meet a preset standard for a target recall rate, where the target recall rate is one or more of the recall rates, and the target recall strategy is one or more of the recall strategies.

The invention relates to the technical field of question answering in financial and medical scenes, and discloses a question answering method and device for application entity extraction, equipment and a medium, and the method comprises the steps: confirming all similar questions associated under each standard question in a first corpus, carrying out the replacement processing of entities in all similar questions through employing specific anaphora symbols, obtaining an updated second corpus; performing replacement processing on entities in the user questions by adopting specific anaphora symbols, performing question recall processing on the user questions after entity replacement in a second corpus to obtain at least one second candidate question, and performing multi-dimensional feature similarity matching processing on the user questions and all the second candidate questions to obtain at least one second candidate question; and selecting a corresponding target question and returning an associated answer. By means of the entity replacement technology, entity information in user questions can be analyzed and compared with more data in a corpus, so that the retrieval range of the user questions is expanded to match related answers, and the recall rate is effectively increased.

The embodiment of the invention provides a text partition length determination method and device, equipment and a medium. The method comprises the steps that text data and at least one sliding step length are obtained; performing sliding window sampling on the text data based on the at least one sliding step length, and determining at least one first candidate interval; determining a first overall evaluation score of each first candidate interval in the at least one first candidate interval, wherein the first overall evaluation score is obtained based on weighted comprehensive evaluation of recall rates and accuracy rates of a plurality of partition lengths in the first candidate interval; determining a target interval in the at least one first candidate interval according to the first overall evaluation score; and the golden section method is applied to the target interval to determine the length of the target partition, so that the recall rate and the accuracy rate are effectively improved, and the application of the large model in actual production is ensured.

The invention relates to the technical field of network security, and provides a line protection device network attack detection method and system based on deep learning, which combines mean square error loss function optimization with a recall rate index, ensures the high-precision reconstruction capability of normal data through a loss function in a model training stage, and improves the network attack detection accuracy. And the weight corresponding to the highest recall rate is used as a final model parameter, so that the reconstruction precision and the attack sample detection sensitivity are considered, the risk of missing report is remarkably reduced, and the generalization robustness is improved.

The invention relates to the field of vector databases, and provides a vector database system based on query distribution perception aiming at the problems of high delay and low recall rate caused by different distributed user queries. Comprising an index construction module, a vector retrieval module, an interval measurement module, a log recording module, a distribution perception module, a distribution detection module, a distribution mapping module, a storage module, an interface agent module and a graphical management interface. According to the method, a set of efficient solution is provided for storage and retrieval of vector data, meanwhile, the distribution condition of user query is monitored in real time, vector distribution is effectively measured, a vector index structure is optimized through user historical query, and stability and reliability of the query quality of a vector database are guaranteed.

The invention discloses a 3D object detection method and device with a high distant object recall rate, a mobile tool and a storage medium. The method comprises the following steps: acquiring point cloud data; inputting voxel features or cylinder features corresponding to the point cloud data into a pre-trained 3D object detection model to obtain 3D object candidate frame information output by the 3D object detection model; and obtaining point features corresponding to the 3D object candidate frame information, and inputting the point features to a pre-trained candidate frame classifier model, so as to determine an identification result of the point cloud data according to an output result of the candidate frame classifier model. According to the invention, the voxel feature or the cylinder feature corresponding to the point cloud data is used as the input part of the 3D object detection model to obtain the 3D object candidate frame information, and the point feature of the point cloud data corresponding to the 3D object candidate frame information is used as the input part of the candidate frame classifier model to carry out object classification and recognition. The object recall rate of 3D object detection can be effectively improved.

The application provides a positive and negative sample selection method for extreme scale target detection, comprising the following steps: step 1: arranging the target real value bounding box area in descending order; step 2: calculating the Distance value of each real value bounding box and all Anchors; step 3: calculating the Distance threshold value of the negative training sample; step 4: setting the value of the hyperparameter k; step 5: assigning the negative training sample of all targets; step 6: assigning the positive training sample of all targets; step 7: directly discarding the Anchor which is not a positive training sample and not a negative training sample; and step 8: outputting the respective positive and negative training samples of all targets for subsequent target detection training. The application can assign more suitable training samples to the target, thereby improving the detection effect of the extreme scale target and further improving the recall rate.

The application relates to the field of vector databases, and aims to solve the problems of high delay and low recall rate caused by different distribution user queries, and provides a vector database system based on query distribution perception, which comprises an index construction module, a vector retrieval module, an interval measurement module, a log recording module, a distribution perception module, a distribution detection module, a distribution mapping module, a storage module, an interface agent module and a graphical management interface; the application provides an efficient solution for storage and retrieval of vector data, simultaneously, the distribution of user queries is monitored in real time, the vector distribution is effectively measured, the vector index structure is optimized through historical user queries, and the stability and reliability of vector database query quality are guaranteed.

Embodiments of the present invention disclose a data processing method, apparatus, device, and storage medium. The method includes: obtaining a plurality of evaluation metrics of historical user samples; wherein the historical user samples include positive samples and negative samples, and the historical user samples include a plurality of preset users; determining initial evaluation weights corresponding to the plurality of evaluation metrics respectively by using a first preset algorithm and a second preset algorithm; determining the recall rate of negative samples in the historical user samples based on the initial evaluation weights; iteratively optimizing the initial evaluation weights by using a preset optimization algorithm based on the recall rate to obtain target evaluation weights; and processing the evaluation metrics of target users based on the target evaluation weights to obtain evaluation results. The data processing method provided by the embodiments of the present invention optimizes the weights of evaluation metrics according to the recall rate of negative samples, so that the optimized evaluation weights can accurately evaluate the users to be evaluated.