12 results about "Feature selector" patented technology

The invention relates to a sperm cell analysis and diagnosis system based on a multi-modal large language model, which comprises a multi-modal data co-processing unit, a cross-modal semantic alignment module, a dynamic diagnosis decision engine and a self-adaptive evolution system, the four-dimensional data processing module is used for synchronously processing microscopic images, motion trail videos, biochemical detection data and four-dimensional input data of medical record texts and comprises a feature selector based on a gating attention mechanism. According to the sperm cell analysis and diagnosis system based on the multi-modal large language model, quantitative analysis of sperm movement chaos features is realized for the first time, a nonlinear dynamic evaluation standard is established, a cross-modal knowledge distillation and meta-learning migration framework is developed, a data annotation dependence bottleneck is broken through, and an interpretable clinical decision support system is constructed; dynamic updating and probabilistic suggestion of diagnosis rules are achieved, semantic analysis of single-cell multi-omics data is achieved, and molecular mechanism research results are converted into clinically available knowledge.

The invention provides an online course MOOC learning prediction method based on heterogeneous feature fusion, and belongs to the field of computer-aided intelligent education. The method comprises the following steps: acquiring an MOOC data set, and preprocessing the data set to obtain a test set; constructing an IHFNet network comprising a multi-agent adaptive static feature selector module, a hierarchical time sequence feature extractor module and a heterogeneous feature fusion module; a multi-agent adaptive static feature selector module screens key static features; the hierarchical time sequence feature extractor module extracts behavior time sequence features with high discrimination ability; the heterogeneous feature fusion module carries out adaptive fusion on the key static features and the behavior time sequence features and carries out classification prediction; an IHFNet network is trained; and collecting MOOC data of a to-be-predicted learner, and inputting the MOOC data to the trained IHFNet network for learning risk prediction. According to the invention, modeling is carried out by fusing the static features of the learner and the behavior time sequence features, the feature representation ability of the learner is enhanced, and the learning risk prediction effect is improved.

A system includes a hardware processor and a system memory storing software code and one or more machine learning (ML) models. The hardware processor is configured to execute the software code to train a first ML model of the one or more ML models as a denoising feature selector, generate, using the trained first ML model a plurality of candidate feature sets, and identify a best volumetric feature set of the plurality of candidate feature sets using a predetermined selection criterion. The hardware processor is further configured to execute the software code to train, using the identified best volumetric feature set, one of the first ML model or a second ML model of the one or more ML models as a denoiser, receive an image including noise due to rendering, and denoise, using the trained denoiser, the noise due to rendering to produce a denoised image.

This invention relates to the field of defect detection technology, specifically to a PCB defect detection method for industrial incremental scenarios. It includes a feature fusion method based on a lightweight segmentation strategy that combines random pruning with separate processing of strong and weak features. This strategy simplifies redundant computation by using a feature selector, and differentiates and recombines defect features of varying saliency during the feature fusion stage. This significantly improves inference speed while maintaining the precision of segmentation, solving the problems of high computational cost and difficulty in capturing minute defects in existing methods. The method also includes an incremental learning approach employing a background classifier adaptation mechanism and local semantic distillation. Class-specific regularization and spatially weighted logical alignment distillation work synergistically. By dynamically calibrating the background prediction logic and constructing a pixel-level semantic relevance matrix, it achieves deep alignment between new and old knowledge and background distribution. This effectively solves the catastrophic forgetting problem caused by the evolution of PCB background texture in existing incremental learning methods, significantly enhancing detection stability and the adaptability of the enhanced model.

The application provides a semantic communication system, comprising: a semantic encoder configured to extract a semantic feature vector of an input source; a source-channel joint encoder configured to encode the semantic feature vector to obtain a to-be-transmitted semantic feature vector; an elastic feature selector configured to sort the to-be-transmitted semantic feature vector according to importance to obtain a semantic importance-sorted feature vector, and transmit a corresponding number of semantic features with the highest importance after sorting to a source-channel joint decoder; and a semantic decoder configured to receive a decoding result and perform semantic decoding on the decoding result to obtain a classification result. The semantic communication system shown in the embodiment of the application uses one semantic encoder for multiple semantic tasks, uses multiple semantic decoders set to complete the multi-task semantic communication requirement, and sequentially sorts the to-be-transmitted features according to importance, so that important semantic information can be preferentially transmitted, and efficient communication transmission and recovery of semantic features are realized.

The invention discloses a soil organic matter mapping feature screening method (DSFS) based on a DS evidence theory. The method is used for improving the precision and stability of soil organic matter (SOM) space prediction. According to the method, results of six feature selectors (Pearson, VIF, RFE, Boruta, RF and XGBoost) are integrated, a recognition framework is constructed, a quality function of each feature is calculated, multi-source evidences are fused by using an improved Dempster combination rule, and finally an optimized feature subset is output. Experiments show that the DSFS is obviously superior to a traditional method in six machine learning models (RF, SVR and the like), the prediction precision of the DSFS occupies the first position with the highest R2 (0.655) and the lowest RMSE (4.187 g / kg), and the DSFS shows high robustness in high-dimensional data. The method has the core advantages that multi-model decisions are integrated through the evidence theory, high-contribution remote sensing variables such as Sentinel-2 are accurately recognized, and an efficient and reliable feature screening scheme is provided for digital soil mapping.

This application relates to a motion capture method, device, equipment, and medium for ice hockey passing and control training. The method includes: acquiring raw motion data from an inertial measurement unit (IMU), synchronizing and cleaning it to obtain a synchronization data matrix; constructing an initial spatiotemporal graph using IMUs as nodes and spatial relationships as edges; generating weight information using a LASSO feature selector, and weighting it to obtain an updated spatiotemporal graph; inputting the updated spatiotemporal graph and weights into a graph neural network for synchronous inference of motion segmentation, category, and quality assessment information; decoding the motion boundaries and category labels using the Viterbi algorithm; inputting the data into a scoring regression model to calculate a technical quality score, and outputting the motion analysis results. This method can accurately capture ice hockey passing and control motions, achieving integrated motion segmentation, classification, and quality assessment, thus improving training analysis efficiency.

The invention relates to the field of finance, and discloses a multi-dimensional investment decision support system and a dynamic income prediction method, and the system comprises a multi-source heterogeneous data integration layer, a dynamic feature engineering module, a hybrid machine learning model group and a multi-dimensional decision engine which are connected in sequence. The multi-source heterogeneous data integration layer comprises a financial quantification database, an unstructured data processing engine and an industrial chain knowledge graph which are arranged in parallel, and the dynamic feature engineering module integrates a sliding time window dynamic segmentation algorithm and a feature selector based on an Attention mechanism. Structured financial data and unstructured data (such as public opinions and industrial chain supply and demand relationships) are synchronously collected through a multi-source heterogeneous data integration layer, a dynamic industrial chain knowledge graph is constructed, and the defect that a traditional system only depends on financial data is overcome through multi-dimensional data integration, so that investors can more comprehensively capture market signals, and the market quality is improved. Decision deviation caused by one-sided information is reduced, and the objectivity and accuracy of judgment are improved.

The invention relates to a water plant equipment working condition prediction method based on multi-factor coupling, and belongs to the technical field of intelligent prediction and deep learning. The method comprises the following steps: acquiring data of working conditions of water plant equipment, preprocessing the data into triple data, converting the triple data into vector representation, sending the vector representation into a feature extraction module, extracting relation features by using LSTM and PNA, extracting context features by using ALN, inputting the extracted features into an improved Transform after passing through a feature selector, and fusing by using an attention fusion mechanism, and inputting the fused event representation into a cross attention calculation and prediction module, calculating a final similarity score of the historical event chain and each candidate event, and selecting the candidate event with the high similarity score as a final prediction result. According to the method, the dynamic coupling relation between different working condition parameters of the equipment can be decoupled to predict the working condition script event, chain propagation of prediction errors is effectively reduced, meanwhile, the complexity of an algorithm model can be reduced, and the robustness of the model can be improved.

This invention discloses a method for selecting biometric features of brain topological networks, comprising the following steps: 1. Acquiring brain region biosignals and inter-brain region biosignals, using brain region biosignals as node features and inter-brain region biosignals as edge features; 2. Inputting node features and edge features into a feature selector to obtain initial weight values ​​corresponding to node features and edge features; 3. Updating the weight value of node feature i; 4. Sort all updated weight values ​​of node features and initial weight values ​​of edge features from largest to smallest, deleting the feature with the smallest weight value, and retaining the remaining node features and edge features; 5. Continuing step 2, iterating repeatedly until a total of K node features and edge features remain; 6. Using these node features and edge features as typical features of the brain network topology to assist in the discovery and diagnosis of mental illnesses. This invention is used to find typical feature data for subsequent highly reliable diagnostic classification.

This invention discloses a rapid method for detecting and analyzing the composition of geological experimental samples, belonging to the field of geological analysis and testing technology. The method includes: acquiring multimodal data such as laser-induced breakdown spectroscopy, X-ray fluorescence spectroscopy, and Raman spectroscopy; performing baseline correction, noise filtering, and feature extraction through multi-agent collaborative processing; constructing a deep reinforcement learning feature selector to dynamically screen cross-modal shared sparse feature sets and heterogeneous complementary feature sets; using an improved ant colony algorithm and a Bayesian neural network for adaptive weighted fusion; constructing a physical information constraint network embedded with geochemical constraints to generate preliminary predicted values; and finally correcting abnormal predicted values ​​using a graph neural network topology corrector to output the final compositional analysis results. This invention integrates multi-agent algorithms with prior physical knowledge, significantly improving the accuracy, robustness, and interpretability of geological sample compositional analysis, and solving the problems of low analytical accuracy, poor generalization ability, and lack of physical consistency in existing technologies.

A system includes a hardware processor and a system memory storing software code and one or more machine learning (ML) models. The hardware processor is configured to execute the software code to train a first ML model of the one or more ML models as a denoising feature selector, generate, using the trained first ML model a plurality of candidate feature sets, and identify a best volumetric feature set of the plurality of candidate feature sets using a predetermined selection criterion. The hardware processor is further configured to execute the software code to train, using the identified best volumetric feature set, one of the first ML model or a second ML model of the one or more ML models as a denoiser, receive an image including noise due to rendering, and denoise, using the trained denoiser, the noise due to rendering to produce a denoised image.