225 results about "Model interpretation" patented technology

The invention provides a prognosis risk prediction model construction method based on coexistence of acute myocardial infarction and atrial fibrillation and application. The method comprises the following steps: S1, acquiring historical patient clinical information of a target patient and death time after coexistence of diseases; s2, preprocessing clinical information of historical patients; s3, using an LASSO method and a ten-fold cross validation method to identify related clinical features related to death time after coexistence of diseases; s4, dividing a training set and a verification set; s5, performing training optimization on the four machine models by using the training set, evaluating the prediction performance of the models by using the verification set, and determining a Glmnet model with the optimal prediction performance; and S6, performing model interpretation on the Glmnet model by using an SHAP value, and determining an optimal prediction factor of which the model prediction contribution ranks the first three. The prognosis risk of coexistence of acute myocardial infarction and atrial fibrillation can be accurately predicted, and reference is provided for clinical diagnosis and treatment of patients with coexistence of acute myocardial infarction and atrial fibrillation.

The invention discloses a human pancreas islet function digital evaluation method and system based on an AI algorithm, and relates to the technical field of artificial intelligence medical evaluation, and the method comprises the steps: collecting and preprocessing multi-modal physiological data; extracting multi-source features to generate a low-dimensional input vector; performing dynamic weighted fusion based on a space-time attention mechanism; constructing a neural differential equation model in which a beta cell two-phase secretion mechanism is introduced, and outputting pancreas islet function evaluation indexes; and a dynamic evaluation report is generated through a visualization and early warning module. According to the method, the modeling accuracy of the pancreas islet function under the interference of complex behaviors can be improved, the individualized quantitative evaluation of function decline trend prediction and sensitivity scoring is realized, the clinical staging reference value and the model interpretation ability are enhanced, and the technical problems of low fusion precision, poor dynamic nature and lack of physiological constraints in the existing method are solved.

The invention discloses an automatic data analysis system and method based on artificial intelligence, and relates to the technical field of data management, and the method comprises the following steps: S1, data collection and preparation; s2, data annotation and classification; s3, feature selection and extraction; s4, data standardization and normalization are carried out; s5, model selection; s6, training and optimizing the model; s7, performing model evaluation; s8, model interpretation and visualization are carried out; s9, generating an automatic report; and the system is constructed based on the steps. According to the automatic data analysis system and method based on artificial intelligence, by setting a classification and labeling model, a training optimization mechanism and a dynamic model evaluation method, the efficiency and precision of automatic data analysis are remarkably improved, and the quality of data preprocessing is improved; meanwhile, through self-adaptive hyper-parameter adjustment and optimization, the efficiency of the training process and the model performance are optimized; the dynamic evaluation mechanism ensures that the model is more accurate in performance under different data characteristics.

A system embeds source content segments of the source content to generate input vectors of the source content segments and embeds generated content segments of the artificial-intelligence-generated content to generate output vectors of the generated content segments. The system performs a similarity measurement on the input vectors and the output vectors to generate a similarity score for each pair of input vectors and output vectors. The system defines a similarity correspondence between individual content segments of the source content to individual generated content segments of the artificial-intelligence-generated content, based on performing the similarity measurement and outputs the explanation to a user interface device. The explanation indicates generated result correspondences between the individual content segments of the source content and the individual generated content segments of the artificial-intelligence-generated content.

The invention provides a bio-oil yield prediction method based on interpretable machine learning, and relates to the field of biomass pyrolysis technology and machine learning, and the method comprises the following steps: 1, obtaining a biomass pyrolysis original data set, and carrying out the preprocessing; 2, enhancing the preprocessed data by applying a condition table generative adversarial network CTGAN; 3, dividing the enhanced data set into a training set and a test set, performing training by adopting an extreme gradient enhanced XGB model, optimizing hyper-parameters of a BO model through Bayesian, and constructing a CTGAN-XGB-BO prediction model; and 4, performing model interpretation on the trained and optimized bio-oil yield prediction model. Compared with the prior art, the machine learning algorithm based on the CTGAN-XGB-BO model provided by the invention has the advantages that the prediction precision of the bio-oil yield is improved, the time and the cost of experimental research are reduced, and researchers can better understand and optimize the production process and improve the process control level due to explanation and analysis of the model.

The invention relates to a clustering-based industry subdivision electricity sale quantity prediction method and device and a storage medium, which are applied to the technical field of electricity market transactions, and comprise the steps of obtaining representation vectors in a historical preset period of time of an electricity user, so that the electricity consumption behavior of the user can be represented more accurately, the accuracy of a prediction model is improved, and the user experience is improved. Meanwhile, vectorization representation is carried out on the attributes of the users, and the attributes are spliced with power consumption behavior representation vectors, so that more comprehensive user features can be provided through the integration, and the generalization ability of the model is enhanced; according to the method, high-dimensional original time sequence data is converted into low-dimensional feature vectors, so that the calculation burden of a clustering algorithm is reduced, the clustering efficiency is also improved, users are divided into different class clusters, modeling prediction is carried out for the electrical load of each group of users, clearer model interpretation can be provided, and the user experience is improved. And the electricity utilization characteristics of different user groups can be understood conveniently.

Methods, systems, and apparatus, including computer-readable media, for determining and revealing interpretations of artificial intelligence models. In some implementations, a system receives a prompt from a user. The system obtains code or instructions generated by a artificial intelligence or machine learning (AI / ML) model, where the code or instructions specify criteria to retrieve data from a data source to respond to the prompt. The system generates a set of results from the data source based on the generated code or instructions, and obtains a response to the prompt that an AI / ML model generates using at least a portion of the set of results. The system also generates an interpretation statement that indicates how the prompt was interpreted by the one or more AI / ML models. The system provides output that includes (i) the response to the prompt and (i) the generated interpretation statement.

A cloud-based, machine-learned cybersecurity command line interpretation service simplifies complex command lines using plain language. Command lines are input to the cybersecurity command line interpretation service for an interpretation by a machine learning model. If, however, a command line is known and been previously interpreted, then the cybersecurity command line interpretation service may conserve hardware and software resources by retrieving a historical command line interpretation. If the command line is unknown or not historically logged, then the cybersecurity command line interpretation service may generate a current command line interpretation using the machine learning model. The cybersecurity command line interpretation service may then generate a cybersecurity prediction associated with the command line based on the historical or current command line interpretation. The cybersecurity command line interpretation service thus provides a much faster interpretation and cybersecurity prediction for assessing command lines as malicious or benign.

The invention provides an ultra-high performance concrete multi-performance prediction method based on machine learning. The ultra-high performance concrete multi-performance prediction method comprises the following steps: Step 1, establishing a data set; step 2, data preprocessing is carried out; step 3, establishing an optimal prediction model: based on the feature subset, adopting a plurality of different machine learning algorithms for training, and selecting the machine learning algorithm with the best training effect as the optimal prediction model; step 4, selecting an optimal feature subset; step 5, explaining the influence of the features on model prediction: calculating the contribution degree of each feature to a prediction result based on the optimal prediction model and the optimal feature subset, and helping to understand the decision process of the model; and Step 6, performance prediction of the ultra-high performance concrete: inputting parameters of the to-be-predicted ultra-high performance concrete into the optimal prediction model to obtain a predicted value of the performance. The technical problems that an existing UHPC performance prediction method is incomplete in data set, insufficient in consideration of data processing and feature engineering and poor in model interpretation can be solved.

The invention discloses a depression detection method based on cross-modal knowledge distillation, belongs to the field of depression identification, and solves the defects of an existing depression detection method in the aspects of single-modal data and time sequence model interpretation. The method comprises the following steps: constructing a multi-modal teacher model, wherein the teacher model integrates electroencephalogram and pupil area signals; constructing a single-modal student model which only uses pupil area signals and learns multi-modal features from the teacher model through a knowledge distillation process; training the student model by using a knowledge distillation process, and aligning the middle layer features and output probability distribution of the student model with the teacher model through feature layer distillation and probability distribution layer distillation; and in a test stage, inputting a pupil area signal to be detected into the distilled student model, and outputting a depression classification result. According to the method, the performance of the single-mode model is improved, the dependence on multi-mode data is reduced, and the model interpretability is enhanced.

The invention belongs to the technical field of artificial intelligence and medical diagnosis, and particularly relates to an interpretable Transform medical diagnosis method based on prototype learning, and the method comprises the following steps: data preprocessing; extracting prototype features; constructing a model; optimizing an attention mechanism; key value pair storage: storing the key value pair as a parameterized prototype; designing a loss function; an Adam algorithm is selected to train the model, and the learning rate and batch size hyper-parameters are adjusted according to the actual situation; and evaluating and optimizing the model. According to the method, a prototype learning mechanism is introduced, the attention mechanism of the Transform model is optimized, and the innovative method overcomes the defect of insufficient model interpretation in the prior art. By combining the prototype features and the attention weight, the model can more accurately position key information, and the accuracy of medical diagnosis is improved.

The invention belongs to the technical field of traffic pollution prediction, and particularly relates to a LightGBM-based multi-source data fusion pollutant concentration prediction method, which comprises the following steps of: 1, integrating and processing multi-source data, and constructing a plurality of feature sets; and 2, carrying out feature analysis on meteorological, pollutant and traffic index variables through correlation analysis and time sequence analysis, and providing support for modeling and model interpretation. And step 3, dividing a training test set according to a time sequence, and training and optimizing the LightGBM pollution prediction model through parameter tuning by taking minimization of RMSE as a target. And 4, verifying the performance of the model from prediction precision, spatial distribution and wind direction influence. And 5, analyzing and displaying the key driving factor and quantifying the contribution of the key driving factor. According to the invention, through feature engineering processing and model optimization of heterogeneous data such as weather and traffic, high-precision prediction and influence factor analysis of lane-level pollutant concentration are realized.

The invention provides a power load prediction method based on a space-time diagram convolutional network in extreme weather. Comprising the following steps: collecting historical load data and regional meteorological element data of a plurality of load nodes in a power system, and screening key meteorological characteristics which have obvious influence on loads through a mode of combining model interpretation and regression analysis to construct a meteorological characteristic vector; multivariable empirical mode decomposition and singular value decomposition are adopted to carry out multi-scale reconstruction on load data, and smooth and effective load feature tensors are extracted. On the basis, a graph network structure is constructed in combination with a node physical connection relationship, and load and meteorological characteristics are fused in a time dimension to form node time sequence characteristics. And predicting the load by using the space-time diagram convolutional network model. According to the method, the space-time dependency relationship of the load data can be effectively modeled, the prediction accuracy of the load change under the extreme weather condition is enhanced, and the method has good robustness and generalization ability.

The invention discloses an engineering vehicle safety simulation and prediction system based on digital twinning, and the system comprises a data collection layer which collects multi-source heterogeneous data in real time; in the knowledge graph layer, a streaming inference engine is constructed based on an Apache Jena graph database, and an entity-relationship-attribute triple dynamic graph structure is adopted; according to the AI model layer, a physical rule serves as a loss function constraint term to be embedded into a neural network through a physical information neural network, a digital organ model concept is combined to split a vehicle into key organs for heterogeneous modeling, a simplified physical model is adopted in the core physical process, and an LSTM-AI model is adopted in external behaviors; the explanatory analysis layer is used for integrating an SHAP / LIME explanatory tool to output a visual evidence chain during fault prediction, and deploying an online incremental learning framework to allow the model to learn from new data and dynamically adjust normal range definition; and the visualization and application layer is used for performing three-dimensional visualization rendering based on WebGL or Three.js, and ensuring data transmission security through block chain evidence storage and end-to-end encryption.

The invention discloses a photovoltaic power generation power prediction method based on deep learning and multi-source data fusion, and the method comprises the steps: carrying out the preprocessing of the real-time power generation data, environment parameter data and weather forecast data of a photovoltaic power station, reducing the data noise through Gaussian filtering and bilateral filtering, and then carrying out the prediction of the real-time power generation data, environment parameter data and weather forecast data; according to the method, feature extraction is carried out on data based on a model of an LSTM and an Attention mechanism, the LSTM is used for capturing a long and short term dependency relationship in a time sequence, the Attention mechanism focuses on key environment features through a weighting mode, the feature extraction precision and the model interpretation are improved, finally, multi-source data are fused into a final power prediction result by using a full-connection layer, and the power prediction accuracy is improved. And further smoothing processing is carried out through median filtering, so that the stability and the anti-interference performance of a prediction result are improved. According to the method, the real-time performance and accuracy of photovoltaic power prediction can be improved under the complex environment condition, and the adaptability and robustness of the system are remarkably enhanced.

The invention belongs to the technical field of medical data processing and health assessment, and discloses a multi-source medical data-based risk prediction method, which comprises the steps of multi-source medical data acquisition, data preprocessing, feature selection, model training, risk prediction and model explanation. According to the method, by adopting a systematic multi-stage feature selection strategy, a specific key feature combination highly related to a specific medical event or disease can be screened out from multi-source heterogeneous medical data including clinical data, laboratory data, heart MRI (Magnetic Resonance Imaging) and the like; the screened feature subsets can be used for constructing an interpretable machine learning model, and through combination with SHAP and other model interpretation technologies, clinicians are helped to understand prediction logic, the credibility of results is enhanced, and more valuable reference information is provided for individualized clinical decision and intervention.

The invention discloses a lung squamous cell carcinoma survival probability prediction system fusing multiple models, and relates to the technical field of medical data analysis. Comprising a data acquisition module used for acquiring a lung squamous carcinoma clinical data set and an inspection index of a patient; the data preprocessing and dynamic feature table construction module is used for preprocessing the lung squamous cell carcinoma clinical data set in existing data processing and dynamically updating a physiological feature information table; the model training and predicting module is used for carrying out training and online prediction on various survival analysis models; and the SHAP interpretation and weight calculation module is used for calling a corresponding SHAP algorithm to obtain an average absolute SHAP value of each input feature. According to the method, model interpretation conflict measurement, invalid variable elimination and pseudo high risk verification are provided, and automatic arbitration or artificial recheck is realized through weighted scoring, so that the reasonability and safety of prediction and intervention suggestions are ensured, the decision risk is reduced, and the clinical trust is enhanced.

The invention relates to an interpretable model robust training method based on topological regular terms, and belongs to the field of artificial intelligence safety. The method comprises the following steps: firstly, performing semantic preserving disturbance on an original sample to obtain a disturbance sample; secondly, inputting the original sample and the disturbance sample into a target model, and respectively calculating gradient values to generate corresponding interpretation images; secondly, calculating gradient differences before and after sample disturbance based on a cosine distance and an Euclidean distance, and extracting topological features of interpretation images before and after sample disturbance by using a persistent coherence method to quantify topological differences; and finally, taking the gradient difference and the topology difference as regular terms, forming total loss by the regular terms and the cross entropy loss, and dynamically adjusting the weight of the regular terms according to the proportion of each difference in the total loss. Aiming at the problems that the anti-interference performance of an existing method is influenced by only utilizing a gradient difference feature training model and a fixed regular term weight is difficult to adapt to generalization of a multi-type disturbance reduction model, the invention proposes that the robustness of model explanation is effectively improved by utilizing topological features of an explaining image.

A computing device accesses a plurality of datasets, each dataset comprising an input of a first plurality of inputs to a computer program, a first output of a plurality of outputs of the computer program, and a value identifying an importance of the input to the first output and produced by an explanation service. The computing device trains a machine-learned model to predict values based on the plurality of dataset. The computing device executes the computer program with a second plurality of inputs to produce a second output. The computing device inserts the second plurality of inputs and the second output into the machine-learned model, and the machine-learned model produces values for the second plurality of inputs based on the second plurality of inputs and the second output.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention discloses a long-span bridge typhoon effect probability prediction method based on physical-data cooperative driving, and the method comprises the steps: obtaining typhoon and typhoon effect monitoring data recorded by a structure health monitoring system, and calculating typhoon characteristic parameters and bridge vibration response parameters; then, taking typhoon characteristic parameters as model input and bridge vibration response parameters as model output, constructing a sample set, and dividing the sample set into a training set and a test set; a deep integration strategy of physical-data cooperative driving is further adopted to adjust a neural network architecture, and a typhoon effect probability prediction model providing response mean value and variance dynamic estimation is constructed; then, based on the divided data set and the established probability prediction model, carrying out model training and response prediction; and finally, carrying out model interpretation analysis by adopting an improved Shapril additive interpretation method. According to the method, the typhoon effect prediction accuracy and uncertainty quantification performance of the long-span bridge can be effectively improved, and the robustness and interpretability of the model are synchronously enhanced.

The invention provides a semantic contribution recognition method and system using self-described prompt and integrated gradient. The method comprises the following steps: acquiring input data and compiling a specific question prompt; inputting the question prompt to a preset large language model for multiple times, guiding the large language model to automatically identify and report words playing a key role in a decision making process, and generating a keyword proposal set; extracting keywords based on the keyword proposal set, and generating a first semantic contribution degree list; performing integrated gradient calculation through a preset formula based on the input data to generate a second semantic contribution degree list; and carrying out integration analysis on the first semantic contribution degree list and the second semantic contribution degree list, and determining vocabularies having great influence on large language model decision. According to the method, the problem of limitation of a single method for large language model behavior interpretation in the prior art is solved, the comprehensiveness and accuracy of model interpretation are improved, and the requirement for interpretability in various natural language processing applications is met.

The invention discloses an aquatic organism integrity index modeling method based on a random forest, and relates to the field of water ecology. The method comprises the following steps: collecting candidate indexes; determining a reference point selection standard and selecting a reference point; calculating and screening candidate biological index values; obtaining a PCA candidate index; a P-IBI parameter is calculated; and carrying out random forest modeling, carrying out repeated re-modeling according to an output result, and reserving an index combination with the maximum model interpretation rate to obtain a plurality of indexes which generate comprehensive influence on a single index or a biological integrity index. According to the method, the complex influence of other elements on the aquatic organism integrity index is comprehensively considered, the index which has greater influence on the aquatic organism integrity index can be better disclosed, and a scientific tool is provided for identification of aquatic organism protection.

The invention provides an evaluation method for gastric cancer peritoneal metastasis state recognition and PCI score estimation, and the method comprises the steps: receiving a preoperative abdominal enhancement CT image, and carrying out the automatic positioning preprocessing of the peritoneum through resampling, normalization, image enhancement and a region attention mechanism; image omics features are extracted from the region of interest and fused with the depth features of the multi-scale convolutional neural network, and a binary classification model based on a residual network / Transform is constructed to output transition state probability and confidence; pCI scores of all the areas are quantitatively evaluated synchronously through the peritoneal thirteen subareas, and finally a transfer prediction result, a PCI spatial distribution map, a visual heat map and a model interpretation report are integrated to form structured diagnosis output. According to the method, transition state identification and PCI score quantification can be realized, and the problems of single function, weak generalization, low interpretability and the like of a traditional model are solved.

The invention discloses a non-performing asset management method based on an adaptive multi-modal causal inference network. The method comprises the steps of data acquisition and integration, data preprocessing, data labeling and classification, causal relationship adaptive identification module proposing, model training and optimization, and prediction and management. The causal relationship self-adaptive identification module is used for constructing an efficient and dynamically updated causal graph through a Bayesian network learning method and a method of integrating prior knowledge and data driving; wherein the causal relationship adaptive identification module comprises hierarchical Bayesian prior and knowledge graph fusion, a dynamic adjustment mechanism, hierarchical variational distribution and adaptive regular interaction. According to the method, the accuracy of the bad asset prediction can be remarkably improved, and the defects of a traditional statistical model and a machine learning algorithm in complex relation capture are overcome. The model interpretability is enhanced, and a dynamic adjustment mechanism ensures that the model can respond to market changes in real time.

The invention provides an interpretable machine learning-based giant coronary artery tumor prediction method in Kawasaki disease, and belongs to the technical field of biological information processing. Comprising the following steps: S1, collecting hospitalized KD child patient cases by adopting a retrospective queue research method, and excluding cases which do not meet requirements; s2, coronary artery lesion evaluation and classification; s3, performing variable preprocessing; s4, constructing a machine learning model; s5, performing variable screening; s6, model interpretation; s7, determining an optimal intervention threshold value; and S8, developing an online prediction tool. According to the method, an SVM kernel lab model with the optimal performance is screened out through multi-model comparison, and a group of key prediction molecules are determined in combination with an SHAP method and recursive feature elimination; based on a multi-center data verification model, the model applicability is improved; and early recognition and layered management of high-risk child patients can be realized. A webpage tool is developed, and a patient with high risk is suggested to intervene in advance.

The invention provides a multi-modal data integration and machine learning model for predicting the curative effect and prognosis of an ovarian cancer patient on a PARP inhibitor. The model comprises a data collection and processing module, a feature screening module, a model construction and prediction module and a model interpretation and application module. Data collection relates to clinical, pathological, biochemical indexes and the like, and feature screening is performed through correlation analysis, Cox regression and other methods. The model construction adopts random forest, logistic regression and other algorithms, and meta-learning is carried out through XGBoost or LightGBM. The performance of the model is evaluated through indexes such as AUC and accuracy, and the model is explained by using an SHAP value. According to the invention, the curative effect and prognosis of the PARP inhibitor by the ovarian cancer patient can be accurately predicted, and a scientific basis is provided for clinical treatment.

The invention provides a blood pressure measurement system and method based on oscillatory wave and PPG signal collaborative learning, and relates to the technical field of wearable medical health monitoring. According to the method, the advantages of the CNN in local feature extraction, the Transform in time sequence modeling and the PPG signal in assisting blood pressure prediction are brought into full play, and the accuracy of blood pressure estimation can be remarkably improved. Complex features are automatically extracted through deep learning, dependence on manual feature engineering is reduced, traditional machine learning is adopted, model interpretation is enhanced, and the precision and robustness of blood pressure estimation are improved by combining the advantages of the two. The method is wide in applicability, can be applied to various scenes such as family health monitoring, clinical monitoring and wearable equipment, adopts multi-task learning, can predict systolic pressure and diastolic pressure at the same time, and shares a feature extraction network. Moreover, the algorithm is a lightweight algorithm, can be deployed on wearable equipment to realize low-power-consumption and high-efficiency real-time blood pressure estimation, and provides powerful support for health monitoring.

The present invention discloses a method for collaborative simulation of multiple water quality indicators based on a physical information deep neural network, comprising the following steps: S1, determining the water quality indicators to be collaboratively simulated, and constructing a multi-source database for multi-water quality indicator system simulation; S2, selecting a deep learning model suitable for multiple output water quality indicators, and completing the construction of a physical information deep neural network for multi-indicator water quality collaborative simulation; S3, using a k-fold cross-validation method to train the water quality collaborative simulation model, and completing parameter learning of the water quality collaborative simulation model; S4, selecting evaluation indicators to evaluate the water quality collaborative simulation model, and improving the simulation effect by adjusting the network structure and optimizing hyperparameters until the water quality collaborative simulation model meets the simulation accuracy requirements; S5, based on the optimized water quality collaborative simulation model, using a deep learning model interpretation method to analyze the key driving factors of the collaborative changes of multiple water quality indicators, and complete the multi-indicator water quality collaborative simulation.

The invention discloses an enterprise energy efficiency evaluation and promotion model construction method based on electric power big data, and belongs to the field of data analysis. Comprising the steps of data collection; preprocessing the data; extracting characteristic indexes; constructing a model; evaluating and optimizing the model; and model interpretation application. Based on multi-dimensional electric power big data, an energy efficiency evaluation and improvement model is constructed, energy efficiency analysis and evaluation of key energy consumption enterprises are realized, the value of the electric power big data is fully mined, the enterprises are helped to understand own energy use efficiency, the energy efficiency improvement potential of the enterprises is mined, the enterprises are supported to formulate energy use strategies and energy saving measures, and the energy cost is reduced. And energy conservation, emission reduction and dual-carbon targets can be achieved. The method can be widely applied to the fields of power supply and consumption operation management and key energy consumption enterprise energy efficiency analysis and evaluation.