476 results about "Sample classification" patented technology

The invention discloses a semi-supervised identification method and system for a small target of a container, and belongs to the technical field of artificial intelligence and computer vision, and the method comprises the steps: carrying out the target detection of a container image through a pre-trained target detection model, intercepting a sub-image, and inputting the sub-image into an initial classification model, and obtaining a classification confidence coefficient; the uncertainty of the model on a sample classification result is quantified through a Monte Carlo Dropout method; a feature space distance filtering and dynamic threshold adjusting mechanism is combined, and samples with high confidence, low uncertainty and consistent feature space are screened out to serve as pseudo label data; pseudo label data and initial synthesis data are mixed, and the generalization ability of the model is gradually improved through semi-supervised iterative training. According to the method, the dependence on manual annotation can be remarkably reduced, meanwhile, the distribution difference between synthetic data and real scene data is gradually reduced, and finally, high-precision recognition and strong generalization ability of a classification model in a real scene are achieved.

Methods and apparatus to determine machine learning (ML) configurations for classifying malware are disclosed. An example server comprises interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to determine a computing parameter associated with a computing device, the computing device communicatively coupled to the server, select a ML model to deploy on the computing device based on the computing parameter, determine a configuration of the ML model based on the computing parameter, deploy the ML model to the computing device, and cause the deployed ML model to classify a sample as clean or malicious, the sample received at the computing device.

The invention discloses an OCR (Optical Character Recognition)-based document automatic recognition intelligent management system, and relates to the technical field of intelligent document OCR processing. A preprocessing module adopts adaptive filtering and a GAN (Generic Area Network) to repair low-quality documents and separate seals from characters; the recognition engine realizes end-to-end recognition of inclined characters and mixed characters through a multi-scale feature pyramid network, and the semantic module constructs a dynamic knowledge graph and supports event evolution reasoning and cross-year correlation analysis; the classification module realizes small sample classification based on a prototype network, the security module realizes fine-grained authority control and anomaly detection through a block chain and federated learning, the text edge definition is improved after system processing, and real-time reasoning is supported. The intelligent level of document processing is improved through cooperation of multiple modules, the preprocessing and recognition module solves the complex scene problem, and deep analysis and correlation analysis are achieved through semantic understanding and a knowledge graph; the security module guarantees data security; and the efficiency and decision scientificity are comprehensively improved.

The invention relates to a Han-Cross industry text classification method and system based on prompt learning and adaptive loss weighting, and belongs to the technical field of natural language processing. The method comprises the following steps: designing and constructing a universal prompt template; the method comprises the following steps: recombining a Han-Cross cross-border industry text classification data set, namely converting an original single sample into paired samples; in a few-sample and multi-language scene, related vocabularies are adopted as external knowledge resources, and vocabularies most related to the mapping labels are retrieved from the related vocabularies; expanding the vocabulary mapper by introducing synonyms and associated vocabularies; adopting a dynamic mixed loss function and applying the dynamic mixed loss function to a pre-training language model to optimize a few-sample classification task; and classifying Chinese and Vietnamese cross-border industry texts by using the optimized pre-training language model. The method shows a remarkable effect in Chinese and Vietnamese industry text classification tasks, and is particularly suitable for a few-sample learning scene with data scarcity and language imbalance.

The invention relates to the technical field of patent information analysis, in particular to a patent image few-sample classification method based on multi-modal representation fusion. The method comprises the following steps: constructing a hybrid pre-training model by designing a language branch, a visual branch and a visual auxiliary branch; obtaining input corresponding patent image few samples, and carrying out image feature parallel extraction and text description feature generation to obtain patent image text prompt description features; a cross-modal fusion layer is designed by adopting an attention mechanism, and multi-modal representation fusion and feature fusion loss calculation are carried out, so that comparative learning loss and visual auxiliary branch loss between a language branch and a visual branch are obtained; and performing model fusion optimization on the mixed pre-training model to output fusion features, and constructing a patent image few-sample classification framework to execute corresponding patent image few-sample classification work. The feature representation corresponding to the patent image sample can be fully fused and learned to improve the classification effect of the model.

A battery life early prediction method based on pre-classification and LSTM-CNN comprises the following steps: extracting features by using capacity difference data in a discharge process, determining a classification interval of a sample through an unsupervised learning clustering method, dividing the sample into a long life group and a short life group according to the actual life distribution condition of the battery, according to the method, a brand-new superposition model of LSTM and CNN networks suitable for battery sample classification and life prediction is constructed and trained, new samples are classified through an LSTM-CNN classification model, different life prediction models are matched, and then the life of the battery is predicted through the LSTM-CNN prediction model, so that early prediction of the life of the battery by using a small amount of data is realized, and the prediction efficiency of the life of the battery is improved. The model structure is simple, and the prediction accuracy and efficiency are remarkably improved.

A processor may gather raw data comprising a plurality of characteristic data samples of a target user group. The processor may categorize the characteristic data samples into a plurality of user-related classes and triggers. The processor may build an input property graph for each characteristic data sample. The processor may augment the input property graph by a concept of hierarchies. The processor may determine a modification vector from the augmented input property graph. The processor may train an encoder / decoder combination machine-learning system. An embedding vector and a modification vector are used as input for the decoder to build a trained machine-learning generative model.

The invention discloses a few-sample classification method research based on feature cache optimization, and the method comprises the steps: (1), employing an improved ResNet-based SwAV model as a backbone, and enabling the SwAV model to be used for generating auxiliary features; (2) taking the image data and the text description as original input, and generating text input with rich downstream language semantics by utilizing GPT-3 to serve as text prompt of a CLIP model; (3) through a feature selection method based on feature similarity and difference, the problems of feature redundancy and inaccurate selection in the feature selection process are solved, feature dimensions with high selection value are identified, and normalization and enhancement zooming processing are performed on the features; and (4) utilizing visual contrast knowledge of SwAV, introducing a learnable cache model, and adaptively mixing prediction results from CLIP and SwAV. According to the method, under the CLIP framework without extra training, the accuracy of few-sample image classification is effectively improved through feature cache optimization and a self-adaptive hybrid strategy.

The invention relates to the technical field of tourism services, in particular to a grease detection device and method for edible oil production and processing, and a system comprises a spectral data acquisition module, a component ratio calculation module, a spectral feature analysis module, a chemical component deviation evaluation module and a smell component fluctuation detection module. According to the method, accurate identification of edible oil component characteristics is improved by using the laser Raman spectrometer, fine analysis of complex components is realized by combining gas and liquid chromatograms and mass spectrometers, release of volatile substances is monitored by the electronic nose sensor, and environmental adaptability and dynamic response capability of data are enhanced. Through automatic abnormal data elimination and normalization processing, the data consistency and standardization level are improved, the accuracy of sample classification and edible oil safety assessment is further improved through multi-parameter ratio calculation and peak position offset analysis, the data processing flow is optimized, automation and intelligence of the detection system are enhanced, and the detection efficiency is improved. And the efficiency and the precision of edible oil detection are obviously improved.

The invention provides a collaborative classification method and system fusing advantages of large and small models, and the method comprises the steps: inputting to-be-classified data into a trained zero-sample classification small model, and outputting candidate label domain screening information and an initial classification result; inputting the to-be-classified data, the candidate label domain screening information and the initial classification result into the trained large model, and outputting a final classification result; the training process of the small model comprises the following steps: inputting training data into the zero sample classification small model to obtain a preliminary prediction result; screening and obtaining pseudo label data based on an active learning strategy; checking and re-marking the pseudo-label data by using the large model to obtain a modified pseudo-label data set; and carrying out iterative training on the zero sample classification small model by utilizing the modified pseudo label data set. The method has the advantages that a small model has classification performance close to that of a large model while keeping lightweight calculation characteristics; the calculation burden of a large model is reduced, and the accuracy of classification decision is improved.

The invention discloses a soil physical and chemical parameter spectrum inversion method based on an SVM and a BP neural network. The method comprises the steps that near infrared spectrum data of soil samples with different water contents are collected and preprocessed; training and verifying a pre-constructed dry and wet soil sample classification model based on an SVM (Support Vector Machine) by utilizing the near infrared spectrum data of the preprocessed soil samples with different water contents, and dividing the soil samples into dry soil and wet soil by utilizing the trained dry and wet soil sample classification model; and respectively training and verifying a pre-constructed soil physical and chemical parameter prediction model based on a BP neural network by using the near infrared spectrum data after the pretreatment of the dry soil and wet soil samples and the measured values of the physical and chemical parameters of the soil samples to obtain soil physical and chemical parameter prediction models corresponding to the dry soil and wet soil samples respectively. The model not only can improve the accuracy and efficiency of soil physical and chemical index analysis, but also can provide a scientific basis for agricultural production and environmental protection.

A method for technical issue resolution. The method includes: receiving, from a user, a text query concerning a technical issue; obtaining query-related context relevant to the text query; and processing, through a large language model (LLM), the text query and the query-related context to produce a multimodal query response used by the user to address the technical issue. More specifically, embodiments described herein utilize text topic and zero shot classification models to translate multimodal technical documentation (e.g., including text and images) into topic relevant metadata; and process queries, pertaining to technical issues, using a multimodal LLM provided with query-related text and image context derived from said topic relevant metadata.

The invention discloses an image classification method and device, equipment, a medium and a product, and relates to the field of image classification, and the method comprises the steps: obtaining to-be-classified image data; according to the to-be-classified image data, performing classification by using a classification model to obtain a classification category; the classification model is a trained pulse neural network; the spiking neural network comprises a plurality of neural layers; each neural layer comprises a plurality of neurons; each neuron comprises an emission threshold value and an input resistance; the training process of the classification model specifically comprises the following steps: taking sample image data as the input of a spiking neural network, taking a sample classification category as the output of the spiking neural network, and determining a total loss function of the spiking neural network according to cross entropy loss and adaptive sparse loss; and optimizing the synaptic weight, the emission threshold and the input resistance of the spiking neural network by using a back propagation algorithm to obtain a classification model. The method can improve the classification precision.

The invention provides a network attack processing method and system based on a zero-sum game, relates to the field of data processing, and can solve the problem of low network attack processing accuracy. The method comprises the steps that a target sample in samples is determined according to the optimal weight of all classifiers in a classification system and predicted values of all the classifiers for the samples, the classification system at least comprises a random forest classifier, an extreme random tree classifier and a gradient boosting classifier, and the samples comprise network attack information; then, carrying out quantity expansion processing on the target class samples through a generative adversarial network model to obtain expanded samples; and then, carrying out network attack detection according to the expanded sample and the sample, and determining whether a network intrusion attack behavior exists or not. And an expansion sample with authenticity and diversity is generated through the generative adversarial network model, so that hidden network intrusion attack behaviors can be found. Therefore, the processing accuracy of the network attack is improved.

The invention discloses a visual analysis and screening method for fish maw appearance features, and relates to the technical field of computer vision and intelligent sorting, and the method comprises the steps: carrying out the polarization reflection feature analysis of a fish maw main region image, constructing a surface direction-reflection relation tensor, and carrying out the clustering to generate an anisotropic reflection texture feature pattern; gradient pyramid fusion is carried out on the anisotropic reflection texture feature map and the RGB image, contour information is extracted, and a fish maw boundary enhanced image is generated; inputting the fish maw boundary enhanced image into a contour detection network, performing automatic modeling, outputting a high-precision boundary thermodynamic diagram, and generating a high-precision boundary contour diagram; and carrying out feature fusion on the anisotropic reflection texture feature map and the high-precision boundary contour map, constructing a fish maw appearance description vector, carrying out appearance grade classification through a multi-layer neural network model in combination with a historical evaluation sample, and outputting a fish maw sample classification result. The accuracy and efficiency of fish maw quality detection are remarkably improved.

The embodiment of the invention provides a backdoor attack method and device, processing equipment, a program product and a medium, and is applied to the technical field of artificial intelligence. The method comprises the steps that target attention distribution corresponding to a target vision converter model is constructed based on a sample set of a target category, and the target vision converter model is trained to be capable of classifying all samples in the sample set into the target category; based on the target attention distribution and the to-be-processed source image, generating a trigger corresponding to the to-be-processed source image; based on a dual-objective loss function, parameters of the trigger are iteratively optimized, a target trigger is obtained, and the dual-objective loss function is constructed based on Wasserstein distance and classification loss; and generating a poisoning sample corresponding to the to-be-processed source image according to the target trigger and the to-be-processed source image. By the adoption of the method, the problem that an existing backdoor attack method is difficult to adapt to a self-attention mechanism, and consequently efficient hidden attack on a visual converter model is difficult to achieve is solved.

The invention discloses a multi-granularity financial text noise open classification method based on a large model, and belongs to the technical field of text noise classification. Performing clustering processing on the semantic features to obtain a plurality of balls with different labels, and calculating attributes of the plurality of balls with different labels; according to the positions of the samples in the pellets and the attributes of the pellets, the samples in the pellets are classified into clean samples, internal noise is distributed, and external noise is distributed. A plurality of pellets with different labels are obtained through clustering processing, the attribute of each pellet is calculated, at the moment, each category is finally represented by a plurality of pellets with the same label but different attributes, the multi-granularity prototype and the distribution range of the category can be effectively reflected, and the representation learning effect is improved. Meanwhile, classification processing is carried out according to the positions of the samples in the pellets and the attributes of the pellets, and the method can be suitable for data environments of various distribution types.

The invention relates to a road crack detection method and system based on a zero-sample large language model, and relates to the technical field of computer vision and artificial intelligence. The method comprises the steps that a road surface image is collected through an unmanned aerial vehicle or a vehicle-mounted camera, after denoising and contrast adjustment preprocessing, visual features are extracted through a multi-modal model and converted into text description, and an image-text embedding vector is generated; semantic reasoning is performed on the embedded vector based on a large language model, zero sample classification is realized through similarity matching of predefined text tags, and crack types and severity are identified; and generating a restoration suggestion in combination with the road environment and the traffic load data, and outputting a detection result and a user feedback optimization model through a visual interface. The method can realize high-precision crack detection without labeling data, has the advantages of strong generalization, multi-modal fusion, intelligent decision support and the like, and can be widely applied to health monitoring and maintenance of infrastructures such as roads, bridges and the like.

The invention discloses a method for analyzing a complex system by using a surface enhanced Raman spectrum three-dimensional code and application of the method. The method specifically comprises the following steps: obtaining SERS sensing channels with different affinity characteristics by using an unmarked or functionally-marked SERS substrate; collecting an SERS spectrum data set by utilizing the constructed SERS sensing channel, and integrating the SERS spectrum data set into a'three-dimensional code 'image through data conversion and integration of three dimensions of Raman vibration band distribution, the SERS sensing channel and SERS intensity, so as to realize imaging of the molecular landscape; the feature information in the SERS three-dimensional code is screened and extracted through a convolutional neural network algorithm, and a mapping relation between the SERS three-dimensional code and a complex system label is established, so that a complex system classification / regression model is established, rapid qualitative or quantitative analysis of a complex system is realized, and the accuracy of the SERS three-dimensional code is improved. More applications can be expanded in the fields of molecular recognition, biological sample classification and identification, disease diagnosis, cell monitoring and the like.

The invention relates to the technical field of sample classification, in particular to an automatic classification system for reproductive test samples, which comprises the following steps: a real-time dynamic characteristic monitoring module for monitoring key characteristics of the reproductive test samples in real time by using a detection device and capturing key characteristic micro-change information of the samples; the dynamic adaptive classification control module is used for evaluating the micro-change information in real time based on the micro-change information transmitted by the real-time dynamic characteristic monitoring module and selecting an optimal classification path according to an evaluation result; and the multi-mode classification module constructs a multi-mode classification model based on a machine learning algorithm, and the multi-mode classification model classifies the specimens in combination with the optimal classification path. According to the method, the classification model can automatically adjust the model parameters when the characteristic data slightly changes, so that the classification effect is further improved, and particularly, the method has remarkable advantages when being used for processing high-dynamic reproductive samples with complex characteristics.

The invention discloses a semi-automatic labeling method and system for rail transit engineering construction video images, and the method comprises the steps: removing redundant frames from a video stream, extracting key frames, and forming a block index set; and driving the multi-modal large model to pre-annotate the image by using the constructed cue word, and carrying out binarization processing on an annotation result. Based on active learning, migrating unmarked samples to a marked set for multiple times and training a key sample classifier CD until the scale of the marked set reaches the standard; meanwhile, a confidence classifier CC is trained by comparing manual and pre-labeling results. And for residual samples in the unmarked set, after the residual samples reach the standard through a CC precision test, marking tasks are divided according to a threshold value theta: high-confidence samples are pre-marked, and low-confidence samples are manually marked. And finally, updating the set of the manually labeled samples, and determining whether to retrain the model or not according to category distribution. On the premise of ensuring the labeling quality, the blindness of manual labeling is effectively reduced, and the efficiency of the labeling process is improved.

Sample classification using natural language processing (NLP) models is disclosed herein. An example apparatus comprises interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to access a set of instructions, the set of instructions representing executable instructions, determine an Application Programming Interface (API) call sequence based on the set of instructions, transmit the API call sequence to a NLP model, the NLP model to generate a set of tokens, the set of tokens representing the API call sequence, the set of tokens readable in the NLP model, and classify the API call sequence as clean or malicious based on the tokens, and classify the set of instructions as clean or malicious based on the classification of the API call sequence and at least one other feature of the set of instructions.

The invention discloses a small sample abnormal signal identification method and system, a storage medium and a program product. The method comprises the following steps: acquiring a to-be-tested signal; identifying a to-be-detected signal by using the abnormal signal identification model; wherein the abnormal signal identification model comprises an isolated forest model and a small sample classification model. According to the method, the to-be-detected signal in the actual industrial field can be accurately judged, and the recognition reliability is effectively improved.

The invention provides a multivariate data-based printing machine top project approval design method, which relates to the technical field of printing equipment, and comprises the following steps of: acquiring printing machine operation data comprising printing quality parameters, equipment working condition parameters and environment parameters; performing feature analysis and parameter extraction through an adaptive fuzzy clustering algorithm, dynamically adjusting a sample classification membership degree, identifying a key parameter combination influencing the printing quality, and constructing a correlation matrix; constructing a dynamic Bayesian probability network model based on a parameter mapping relation, constructing a core network structure through an information entropy value and information gain, and establishing probability association between a printing quality parameter and an equipment working condition parameter; and determining the optimal parameter configuration of the function modules of the printing machine by utilizing a bidirectional constraint dynamic programming algorithm and combining the coupling weight and the state transition equation among the function modules, and generating a top design scheme.

Some embodiments of the current disclosure disclose methods and systems for training for training a natural language processing intent classification model to perform few-shot classification tasks. In some embodiments, a pair of an utterance and a first semantic label labeling the utterance may be generated and a neural network that is configured to perform natural language inference tasks may be utilized to determine the existence of an entailment relationship between the utterance and the semantic label. The semantic label may be predicted as the intent class of the utterance based on the entailment relationship and the pair may be used to train the natural language processing intent classification model to perform few-shot classification tasks.

The invention discloses a positive and negative unmarked image classification method and system based on depth metric learning, and relates to the technical field of sample classification, and the method comprises the steps: obtaining a marked positive sample image and an unmarked image, inputting the marked positive sample image and the unmarked image into a depth metric learning model, adjusting the marked positive sample image in a depth feature space, and obtaining the unmarked image; minimizing the distance between the marked positive sample image and the center of the positive sample image, aligning the unmarked image and the corresponding enhanced view in a representation space through self-consistent metric learning, and outputting related feature representation; using related feature representation to select sample images which are in multiple proportion to the positive sample images as reliable negative samples; and taking the related feature representation as input, using a positive sample image and a reliable negative sample as supervision signals to train a binary classifier, and classifying positive and negative unmarked images to be detected. According to the method, dependence on heuristic negative sample selection is avoided, and high-quality feature representation with discrimination on positive and negative samples can be learned.

The invention relates to the technical field of ore raw material analysis, and discloses an intermediate fusion strategy ore sample classification method based on random model combined data, and the method comprises the following steps: S1, collecting LIBS spectrum and Raman spectrum data of a manganese ore sample; s2, preprocessing the spectral data; s3, performing dimensionality reduction on the preprocessed LIBS and Raman data by adopting principal component analysis; s4, splicing the feature matrixes after dimension reduction to construct an intermediate fusion data set; s5, training a random forest classification model based on the intermediate fusion data set; and S6, carrying out classification and identification on the manganese ore samples by utilizing the trained model. According to the method, metal element characteristic spectral lines captured by the LIBS spectrum and molecular vibration characteristics identified by the Raman spectrum are organically integrated through an intermediate fusion strategy, two-dimensional identification is achieved, redundant information is effectively eliminated while main characteristics of the spectrum are reserved, and the accuracy and reliability of manganese ore sample classification are remarkably improved.

For ticket classification in a building automation system, a large language model (LLM) is used to classify. In one approach, a prompt is generated for zero-shot classification, and a prompt is generated for few-shot classification. In another approach, a hybrid annotation provides corrections (review) by an expert to correct LLM classification for sample tickets to be used as examples in the few-shot classification. The LLM may operate on a diverse and complex range of tickets in an efficient and scalable manner.

The invention provides an auxiliary rock core geological logging method based on drilling parameters, and relates to the technical field of geological investigation, the method is applied to a logging control terminal, and the method mainly comprises the following steps: obtaining regional typical rock core samples and five types of drilling parameter signals, classifying and extracting static characteristics of the rock core samples, and carrying out parallel noise reduction analysis on the parameter signals; obtaining a matching relationship between the rock core and the parameter signal, and constructing a lithologic parameter bidirectional association reference library; on the basis of the matching relation of the lithologic parameter bidirectional association reference library, the model is embedded into an incremental learning module to update the weight, and a subsection lithologic pre-judgment result is output; and calling a preset catalog template to load a pre-judgment result, generating a catalog report after man-machine interaction recheck and correction, collecting correction data, reversely transmitting the correction data back to the reference library to update a matching relationship, and triggering incremental learning of the model to complete a closed loop. The technical problems that in traditional rock core logging, regional lithology adaptation is poor, parameter interference is large, a model is not dynamically optimized, the deep operation risk is high, and efficiency is low are effectively solved.

The invention relates to a hyperspectral image classification method based on a bidirectional interactive fusion space-spectrum multi-order gating aggregation network, and belongs to the field of remote sensing image processing. The method comprises the steps of determining a data set; data preprocessing: performing sample block extraction on the hyperspectral data set, and dividing the hyperspectral data set into a training set, a verification set and a test set; network construction: constructing a space-spectrum multi-order gating aggregation network based on bidirectional interactive fusion, wherein the space-spectrum multi-order gating aggregation network is used for hyperspectral image classification; the hyperspectral samples in the training set are input into the constructed network in batches for training, and after each training batch is completed, the classification performance is evaluated by using the verification set samples; and sample classification: inputting the hyperspectral samples in the test set into the trained classification network to obtain a final classification result. According to the method, efficient feature extraction can be realized, high-accuracy classification is performed on the hyperspectral image, and the method can be widely applied to remote sensing application fields such as hyperspectral image surface feature category detection and recognition.