293 results about "Decision tree" patented technology

An approach is provided in which a method, system, and program product display, on a user interface, at least one of a set of node split parameters in response to receiving a first user selection that selects a node in a decision tree. The selected node branches to a set of child nodes in the decision tree based on the set of node split parameters. The method, system, and program product adjust at least one of the set of node split parameters of the selected node in response to receiving a second user selection. The method, system, and program product modify the decision tree based on the adjusted set of node split parameters. The modified decision tree includes a modified set of child nodes that branch from the selected node based on the adjusted set of node split parameters.

A data processing system implements performing a root cause analysis that includes identifying a first anomalous signal data predictive of a root cause of a first anomaly in signal data received from a computing system, analyzing the sub-signals of the first anomalous signal data to generate labeled training data, training a gradient boosted tree model using the labeled training data, generating a decision tree based approximating a predictive performance of the gradient boosted tree model, determining insights data predictive of the root cause of the first anomaly based on the gradient boosted tree model and the decision tree, aggregating the insights and analyzing the aggregated insights data to determine a predicted root cause for the first anomaly, determining a confidence level associated with the predicted root cause, and categorizing the predicted root cause into one of a plurality of categories based on the confidence level.

Method and system for processing encrypted data comprising evaluating a client's encrypted data vector by at least one decision tree provided by a server. This method comprises, for evaluating a client's encrypted data vector by at least one decision tree (22) provided by a server (6), in a comparison phase: by a first execution environment, implementing a homomorphic encryption scheme, calculation (52, 54) of an evaluation vector, comprising encrypted components representing a difference between a component of the encrypted data vector and a decision threshold value associated with a corresponding node of said at least one decision tree, and providing the evaluation vector to a second execution environment which is a secure execution environment, executed by said server but controlled by said client.and by the second secure execution environment: - decryption (56) of said evaluation vector by applying a private key of said client, determination (58) of the sign of each component of the decrypted evaluation vector and provision of an encrypted sign vector to the first execution environment. Figure for the abstract: Figure 4,

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a decision tree to generate a recommended action in response to detecting a change in a data map representing a computing environment. The disclosed system determines relationships among data objects representing digital data, digital assets, and data processing activities and generates a data map according to the relationships. The disclosed system monitors the data map for changes and, responsive to determining a change in the data map, traverses a decision tree by executing one or more calls to one or more application programming interfaces according to the change in the data map and one or more data policies relevant to the changes. The disclosed systems utilize the decision tree and application programming interfaces to generate a recommended action for modifying digital assets, digital data, and / or data processing activities.

Disclosed are various embodiments for analyzing machine learning models. A selection is obtained of a first tuple comprising a first feature vector and a first result generated by a machine learning model and a second tuple comprising a second feature vector and a second result generated by the machine learning model. Then, a plurality of emulated feature vectors are generated. Next, a plurality of emulated results are generated. Subsequently, a plurality of emulated decision instances are generated. Next, a decision tree is built based at least in part on the first tuple, the second tuple, and the plurality of emulated decision instances. Finally, an importance of each feature on the decision tree is computed.

The application provides a parallel training method and device of a Transformer model, and relates to the technical field of computers; the parallel training method of the Transformer model comprises the following steps: determining an initial Transformer model parallel training strategy search space based on M preset parallel training strategies; constructing at least one decision tree based on each preset parallel training strategy; the decision tree is used for determining an initial Transformer model parallel training strategy set from the parallel training strategy search space; determining a target parallel training strategy combination based on the training strategy set; and training the initial Transformer model by using the target parallel training strategy combination to obtain a target Transformer model. The initial Transformer model is trained by using the target parallel training strategy combination with the highest throughput, and the training efficiency of the model is improved.

A method for processing a digital content with a decision tree, including: transmitting a ciphertext to a server; obtaining, from the server, a processed ciphertext function of an encryption of a difference between a third vector equal to a multiplication of the plaintext and a first matrix, and a first vector; decrypting the processed ciphertext; setting elements of the processed plaintext selectively to 1 or 0; encrypting the processed plaintext; transmitting the resulting second ciphertext to the server; obtaining, from the server, a second processed ciphertext function of an encryption of a difference between a fourth vector equal to a multiplication of the processed plaintext and a second matrix, and a second vector; decrypting the second processed ciphertext; setting elements of the resulting second plaintext selectively to 0 or 1; and determining an outcome of the decision tree based on the second plaintext.

Various embodiments of the present disclosure provide hallucination mitigation techniques for text-to-code conversions that improves the functionality of a computer in various aspects. The techniques comprise receiving a text-based file that defines a set of standards for a prediction domain; generating, using a machine learning model, a decision tree based on (i) the text-based file and (ii) a decisioning prompt for the text-based file; generating, using the machine learning model, computer programmable code for the set of standards based on the decision tree and a code conversion prompt for the decision tree; and providing the computer programmable code to implement an automated task for the prediction domain.

This invention relates to the field of multimodal transport route planning technology and discloses a route planning method for multimodal freight transport. The method includes constructing an initial decision tree based on dynamic historical trajectories, where branches are sequenced path nodes, nodes are tool call chains, and leaf nodes are resource consumption profiles. By matching real-time constraints and cost objectives, iterative pruning and branch injection operations are performed on the decision tree to generate an optimized decision tree. After obtaining a set of candidate solutions through deduction, multi-dimensional robustness stress tests are conducted on them to simulate path stability and resource fluctuations under preset disturbances, selecting a subset of feasible solutions. Finally, the optimal solution is dynamically selected based on the real-time status and converted into an operation command. This method can adapt to changes in dynamic constraints and proactively ensure the robustness of solution execution.

This invention discloses a method for predicting the heading date of rice, comprising the following steps: S1. Acquiring multispectral images using a drone; S2. Manually recording heading date data; S3. Screening and processing the multispectral images to obtain a complete full-frame multispectral orthophoto image; S4. Segmenting the image, selecting different breeding plots, and calculating the average spectral reflectance of all pixels as spectral reflectance data; S5. Obtaining vegetation indices through band calculations; S6. Constructing a prediction model using a decision tree regression algorithm, and selecting the model with the highest Pearson correlation coefficient as the final prediction model. The drone-based multispectral prediction model for assessing the heading date of rice constructed in this invention takes approximately 20 minutes to collect data from 400 breeding plots. The model's predictive correlation is 0.89, reaching a highly significant level, demonstrating significant application value in improving the efficiency of high-quality rice breeding.

This application relates to the field of civil aviation optimization technology, and in particular to a dynamic fuel consumption optimization system for civil aircraft based on real-time route data. The system includes modules for data storage, input, multi-source information fusion and feature extraction, intelligent evaluation, adaptive decision support, and output response. The system extracts multi-dimensional features by fusing real-time route and QAR data, constructs a correlation dynamic evaluation model using gradient boosting decision trees, simultaneously calculates flight performance index, fuel efficiency index, and correlation score, and generates a structured report based on a decision rule base, including cost index adjustment suggestions, altitude change schemes, and expected fuel savings predictions. This application enables dynamic and quantitative evaluation of the correlation between route environment and fuel consumption, overcomes the lag of traditional planning, and achieves precise and personalized flight strategy optimization, resulting in significant benefits in reducing costs, improving on-time performance, and reducing emissions.

The application discloses a data hierarchical compression storage method and an energy storage management system, device and storage medium, and comprises the following steps: acquiring operation data of a cascaded energy storage system; classifying the operation data into first data, second data and third data according to the reading frequency and the importance of the data; compressing the second data by using a second lossless compression step to form second storage data, wherein the compression rate of the first lossless compression step is lower than that of the second lossless compression step; screening the third data by using a decision tree screening step to form third storage data, wherein the decision tree screening step is provided with a screening condition, part of the content in the third data is deleted based on the screening condition, and the remaining content in the third data forms the third storage data; and storing the first storage data, the second storage data and the third storage data in a local storage, so that the classified storage improves the flexibility of storage reading, reduces the storage load of the storage and improves the reliability of data storage.

The application provides a line loss analysis method and system based on carbon flow tracking, and belongs to the technical field of power data anomaly analysis. CE = CE in ‑ CE o , CE The total loss carbon amount of the node i ; CE in The total input carbon amount of the node i ; CE o The total output carbon amount of the node i ; an XGBoost model for carbon amount anomaly screening is constructed based on a gradient decision tree XGBoost, and a target function of the model; each node in a network topology is analyzed based on the XGBoost model, and a carbon amount anomaly node is predicted based on the power and carbon emission density of each node; each user corresponding to the carbon amount anomaly node is subjected to power analysis, the power analysis includes high-voltage user abnormal power analysis and low-voltage user abnormal power analysis, and the type of abnormal situation existing in the user is obtained.

The present invention relates to a method for performing two-phase detection on abnormal congestion on an urban road on the basis of the polar coordinate transformation of trajectory data. The method includes two phases, i.e., the identification of an abnormal trajectory segment and the detection of incident-induced congestion. In the phase involving the identification of an abnormal trajectory segment, two key features (i.e., an average speed and a time when a road section is entered) are defined so as to capture trajectory segment features within spatio-temporal windows, and the features are affected by both incident-induced congestion and signal timing at downstream intersections; and on the basis of the two key features, the abnormal trajectory segment is identified by means of clustering. In the phase involving the detection of incident-induced congestion, anomaly rates of the spatio-temporal windows are defined; and on the basis of the anomaly rates, a decision tree is used to identify spatio-temporal windows in which incident-induced congestion occurs. Compared with the prior art, the present invention is applicable to urban road networks, and by means of the present invention, the detection of incident-induced congestion at a signal-cycle level in time and a vehicle-flow-level in space is realized.

One aspect of the invention relates to a method (100) for detecting faulty equipment from among a plurality of items of equipment of a device using a dynamic global decision tree. The detection method (100) comprises: - at a current node of the global decision tree, displaying (110) the question relating to a failure of the device respectively associated with the current node; - in response to the question being displayed (110), receiving (120) an item of information relating to a characteristic of at least one item of equipment from among the plurality of items of equipment; - determining (130) at least one item of equipment to be checked from among the plurality of items of equipment of the device according to the received item of information; - selecting (140) at least one decision tree from among the one or more decision trees associated respectively with the at least one item of equipment to be checked; and - updating (150) the global decision tree by replacing a lower tree structure of the current node with at least one portion of the at least one selected decision tree.

This application provides a software switch caching acceleration method and related apparatus based on FPGA, belonging to the field of network packet processing technology. The method includes: compiling a caching rule set into a decision tree forest using a decision tree construction algorithm, the decision tree forest comprising multiple decision trees of equal depth; mapping the decision tree forest to multiple parallel tree pipelines, the tree pipelines running on the FPGA, each tree pipeline corresponding to a decision tree, and each pipeline stage corresponding to a node layer of the decision tree; broadcasting a preset data packet to all tree pipelines for parallel processing; for each tree pipeline, guiding the preset data packet to traverse each pipeline stage until the preset data packet reaches the target leaf node of the decision tree; performing parallel rule matching on the preset data packet at the target leaf node of each decision tree to obtain the target caching rule matching the preset data packet, thereby accelerating flow table caching classification.

The application discloses an intelligent learning question and answer analysis method and application system based on government service, relates to the technical field of artificial intelligence and government service, and comprises the following steps: performing semantic analysis on question and answer data submitted by a user, extracting intention features and entity features to form semantic representation, identifying mutual exclusion relations of cross-department rules in a government rule library, and generating a conflict set. On this basis, the time limit for validity and the time limit for completing the matter of the user are combined to calculate the time limit priority, the conflict resolution path is prioritized, and a multi-path decision tree is constructed. Meanwhile, high-frequency resolution paths are learned from historical question and answer data and a knowledge base is established, so that experience reuse is realized. When a new question and answer request is received, the resolution path in the knowledge base is matched preferentially, and when the matching fails, the optimal reply is output through the decision tree, so that the intelligent level of government question and answer and the cross-department business processing efficiency are improved.

This invention discloses a carbon emission prediction system and method for papermaking processes based on BO-GBDT. The system collects process parameters and energy consumption data during papermaking production, preprocesses the input data, constructs a feature set containing key influencing factors, and obtains a carbon emission dataset for model training. A Bayesian optimization algorithm is introduced to intelligently search for the optimal hyperparameters of four gradient boosting models, constructing a hyperparameter optimization space and setting an optimization objective function. Model performance is evaluated through cross-validation, and the optimal model is updated and selected. BO-GBDT is selected as the final prediction model. The trained model is then used to accurately predict carbon emissions from the papermaking process. By automatically optimizing the hyperparameters of the gradient boosting decision tree model using Bayesian optimization and combining it with multi-source data feature engineering, high-precision and high-efficiency prediction of carbon emissions from the papermaking process is achieved, providing an effective tool for carbon management in the production process.

This invention relates to the field of medical intelligent technology, specifically to an artificial intelligence-based patient treatment path planning system, comprising: state inversion, etiology refinement, department matching, path planning, and conflict resolution modules. The state inversion module acquires the patient's initial state vector and, through multiple rounds of reverse traversal of the clinical decision tree, infers all prior pathological states and forms a set; the etiology refinement module removes duplicates from the set and merges them to generate a set of potential etiology nodes; the department matching module matches this set with a departmental capability knowledge graph to calculate the transfer cost; the path planning module invokes a reinforcement learning engine to generate a recommended path, using transfer cost as feedback and minimizing treatment time; and the conflict resolution module rearranges time overlap checks and generates a planning table. This system can comprehensively uncover etiologies, accurately match departments, optimize the treatment process, avoid overlapping examinations, and improve treatment efficiency.

This invention relates to the field of intelligent visual inspection technology, specifically to a visual recognition system for the wear state of cutting tools on machined parts. The system includes a liquid film interference suppression module, a multi-scale tool segmentation module, a wear feature generation module, a machining parameter acquisition module, an adaptive decision-making module, and a visualization output module. Specifically: the liquid film interference suppression module generates a liquid film interference-suppressed image; the multi-scale tool segmentation module outputs a tool body mask; the wear feature generation module calculates the cutting edge defect rate and surface mottledness, and generates a tool wear index; the machining parameter acquisition module acquires machine tool machining parameters; and the adaptive decision-making module outputs the wear level through a working condition matching decision tree. This invention, by fusing image feature extraction and working condition parameter determination, achieves accurate identification and visualization output of tool wear state, improving the efficiency of intelligent monitoring and decision-making in complex machining scenarios.

This invention discloses an adaptive tree neural network structure and processing method for construction project management. The structure includes: an engineering data input module for collecting structured and time-series data of construction projects; a tree structure encoding module for constructing a multi-level decision tree structure based on project hierarchy and encoding node features; a recurrent neural network module for performing time-series modeling of node feature sequences to predict project status; an adaptive feedback module for feeding back the prediction results to the tree structure encoding module to update node features or weights, forming an adaptive closed loop; and an engineering management output module for generating construction management decision information based on the prediction results. This invention solves the problems of missing engineering structure representation, discontinuous time-series prediction, and lack of dynamic adjustment capability in existing technologies by integrating hierarchical engineering structure modeling and time-series prediction and introducing an adaptive feedback mechanism, thereby improving the intelligence level of construction progress prediction, resource scheduling, and risk warning.

This application provides a product risk detection method, apparatus, medium, and program product, which can be applied to the fields of fintech, artificial intelligence, intelligent operation and maintenance, or other fields. The product risk detection method includes: acquiring non-production data associated with the product to be detected, generated during the non-production phase; extracting features from different dimensions of the non-production data to obtain features of multiple dimensions; determining a target decision tree from multiple decision trees that matches the features of each dimension among the multiple dimensions, wherein each decision tree consists of a root node and leaf nodes, the root node being determined based on historical non-production data of historically deployed products, and the leaf nodes being determined based on historical production data of historically deployed products; and determining the risk detection result for the product to be detected based on the decision results obtained from making decisions on the features of each dimension.

This invention provides a method and system for train operation risk assessment based on dynamic Bayesian networks. The method includes: acquiring multi-source heterogeneous data involved in train operation; modeling the multi-source heterogeneous data using a dynamic Bayesian network to construct a causal relationship graph containing time slices, generating an initial risk assessment model; updating the node variables in the initial risk assessment model in real time to obtain a target risk assessment model; calculating the probability and severity of each risk factor using a conditional probability inference algorithm based on the target risk assessment model; generating risk response strategies using a decision tree algorithm based on the probability and severity of each risk factor; and introducing a distributed monitoring mechanism to continuously track the train operation status and record feedback data for optimizing the risk assessment model during the execution of the risk response strategies. This invention significantly improves the accuracy, timeliness, and adaptability of train risk assessment.

The present application relates to the technical field of tumor prognosis prediction, and particularly relates to a tumor prognosis evaluation method and system based on clinical data analysis. Newly added clinical data is collected regularly to train incremental decision trees and add them to a random forest ensemble model; for each historical decision tree, based on the statistical stability of the feature distribution of the newly added data, the usability is evaluated, combined with the prediction bias to calculate a prognosis rule change index; the right-censored samples with definite outcomes in the newly added data are used to calculate an asymmetric misleading effect index; the weight of the historical decision tree is decayed and calibrated according to the two indexes, to reduce the interference of rule obsolescence and data defects; the prognosis of the patient is predicted based on the calibrated model. The present application effectively overcomes the interference of clinical practice evolution and data right-censored on the accuracy of prognosis evaluation, ensures that the ensemble model is always close to the latest clinical reality, and significantly improves the accuracy of tumor prognosis evaluation.

This invention relates to the field of medical data-driven dose prediction technology, and discloses a method, system, device, and storage medium for individualized dose prediction of hypothyroidism based on medical data. The method includes: constructing a standardized feature vector based on the child's weight, age in days, corrected age in months, current L-T4 dose, TSH value, FT4 value, previous TSH value, TSH rate of change, feeding method, month of consultation, etiology of hypothyroidism, comorbidity status, previous dose adjustment magnitude, and age at which TSH first reached target levels; extracting TSH dynamic trajectory features from the child's TSH time-series data from previous follow-ups; obtaining a basic recommended dose using a gradient boosting decision tree model constructed with counterfactual filtering training data; and correcting the basic recommended dose to obtain an individualized recommended dose. This method improves the prediction accuracy of the gradient boosting decision tree model and allows the individualized recommended dose to simultaneously take into account multiple clinical confounding factors.

The application provides a liver cancer risk stratification and treatment recommendation information generation method and device, the method comprises the following steps: obtaining liver cancer related structured clinical data of a target object; based on a pre-set electronic medical record narrative template, converting the structured clinical data into an electronic medical record style narrative text containing clinical semantics, embedding key clinical variable markers corresponding to the structured clinical data into the electronic medical record style narrative text, and constructing a model input sequence; inputting the model input sequence into a pre-trained large language model for inference operation, outputting a structured text stream, and the large language model is obtained through decision tree constraint based on a liver cancer diagnosis and treatment guideline and multi-objective reinforcement learning strategy training; and extracting comprehensive decision assistance information from the structured text stream by using a pre-set analysis rule, realizing high-credibility clinical assistance decision, and significantly improving the accuracy and logical consistency of the treatment recommendation information.