1276 results about "Learning based" patented technology

The invention discloses a personalized learning path recommendation system based on artificial intelligence, and relates to the technical field of path recommendation, firstly, the system collects multi-dimensional feature data of a learner, and constructs a personalized feature vector; secondly, in combination with knowledge graph modeling and graph neural network technologies, deeply mining explicit and implicit knowledge point association; then, predicting an optimal learning path by using a sequence recommendation model, and ensuring reasonable sorting of knowledge points; in the learning process, the system combines real-time interaction data, dynamically adjusts a learning path, and continuously optimizes a recommendation strategy through an adaptive optimization algorithm; and finally, based on the learning result and the behavior data, evaluating the effectiveness of the learning path, and updating the knowledge point weight and recommendation strategy through a feedback mechanism, thereby realizing intelligent and self-adaptive personalized learning recommendation, the accuracy and adaptability of learning path recommendation can be effectively improved, learners are helped to master knowledge more efficiently, and the learning recommendation efficiency is improved. And the learning experience and effect are improved.

The invention provides a case quality intelligent evaluation method and system based on a large language model, and the method comprises the steps: building an evaluation standard library covering various cases based on a law normative file and judgment practice; designing a complete evaluation reasoning chain based on the evaluation standard library; constructing a multi-agent system based on the evaluation reasoning chain; based on the multi-agent system, realizing distributed task allocation for unplanned online tasks by applying an imprecise alternating direction multiplier method algorithm; based on the distributed task allocation result, combining planning and reinforcement learning technologies to solve a relational multi-agent case association problem; based on the multi-agent case association processing result, applying a forced zero method sparse graph technology to realize effective learning based on a case graph; and constructing a case quality assessment knowledge graph based on the effective learning result of the case graph. By adopting the technical scheme, the accuracy, comprehensiveness and efficiency of case quality evaluation are improved.

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a time series data processing method, device and equipment and a medium. Performing a data enhancement operation on the original time series data to generate enhanced time series data; training a feature encoder based on the enhanced time sequence data in a comparative learning mode to obtain a pre-trained feature encoder; connecting the pre-training feature encoder with a sparse attention mechanism module to construct a time sequence modeling network; target time series data is processed using the time series modeling network to generate a processing result. According to the method, the enhanced view is constructed on the unlabeled data and the contrast learning training feature encoder is introduced, so that the time sequence representation with generalization ability is obtained, effective modeling of the long dependency relationship is realized in combination with a sparse attention mechanism, and the accuracy of time sequence modeling is improved under the condition of not depending on a large amount of labeled data.

A system answers natural language questions using retrieval-augmented generation. The system stores a set of domain specific documents in a vector database. The system receives a natural language question. The system retrieves a subset of documents relevant to the natural language question from the vector database. The system determines prior knowledge information required in addition to the subset of documents retrieved from the vector database for answering the natural language question. The system generates a prompt for a machine learning based language model including instructions to the machine learning based language model to refrain from using prior knowledge obtained by the machine learning based language model during training of the machine learning based language model. The receives a response generated by executing the machine learning based language model based on the prompt. The system performs an action based on the response.

An apparatus may include a receiver configured to receive a reference signal using a channel, and receive restriction information relating to the channel, a processing circuit configured to determine channel information based on the reference signal, and generate, using a machine learning model, a representation based on the channel information and the restriction information, a transmitter configured to transmit the representation.

Technology embodied in a method that includes receiving, as an input to a machine-learning model, data indicative of user-interaction of a particular user with a generative artificial intelligence (AI) system. The machine learning model is trained to identify one or more topics associated with inputs provided to the machine-learning model. The method also includes identifying a first context associated with the data indicative of the user-interaction with the generative AI system, and parsing a file system to determine that one or more folders within the file system correspond to the first context. The file system includes multiple folders each corresponding to a separate topic as identified from historical interactions of the particular user with the generative AI system. The data indicative of the interaction is augmented and provided to the generative AI system for generation of a response to the interaction.

The subject matter described herein includes methods, systems, and computer readable media for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping. According to one method for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping includes obtaining user related information, wherein the user related information includes metrics derived from a user interacting with one or more applications executing on at least one user device; generating, using the user related information and a machine learning based model, a user assessment report including a prediction value indicating a likelihood that the user has a neurodevelopmental or psychiatric (neurodevelopmental / psychiatric) disorder and a prediction confidence value computed using relative contributions of the metrics to the prediction value generated using the machine learning based model; and providing the user assessment report to a display or a data store.

The invention discloses a teaching information processing system and method based on electrical automation control, and relates to the technical field of intelligent teaching, and the method comprises the steps: collecting multi-modal teaching information, and carrying out the preprocessing; extracting and fusing features by using the preprocessed multi-modal teaching information to generate learning state features; constructing and dynamically adjusting a knowledge graph by using the learning state features to obtain a dynamic knowledge graph; generating a personalized learning path based on the dynamic knowledge graph, recommending learning resources, and recording the utilization rate of the learning resources; generating a comprehensive learning state of the student based on the learning state feature and the learning resource utilization rate; and generating teaching feedback according to the comprehensive learning state. According to the invention, by constructing the dynamic knowledge graph and combining the learning state characteristics and the learning resource utilization rate of the students, the knowledge point mastering condition of the students is dynamically evaluated, the personalized learning path is generated, and the adaptive learning resources are pushed.

An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

The invention discloses a learning achievement prediction method based on learning behavior data mining and a computer device. The method comprises the steps of collecting multi-dimensional learning behavior data of students, performing preprocessing and feature extraction, constructing a structured learning behavior feature set, then constructing a target machine learning model suitable for different prediction tasks, inputting structured learning behavior features of the students to be predicted into the model, and obtaining a continuous cumulative risk prediction curve; converting the cumulative risk prediction curve into discrete classification labels, and generating a classification prediction result with reserved time sequence characteristics; and expanding the structured learning behavior feature set by adopting a pseudo-label semi-supervised learning algorithm, iteratively training a target machine learning model, inputting the structured learning behavior features of a to-be-predicted student into the trained model to obtain a comprehensive prediction result, and generating a visual learning achievement prediction scheme according to a classification prediction result. The method can provide a comprehensive and accurate prediction result.

An education platform has systems and associated methods for using generative artificial intelligence to generate and present individualized education curriculums based on tracked user engagement with the presented content. The education platform receives a request to provide individualized instruction to a user on a particular subject, and presents first content for a first part of the particular subject to the user. The education platform tracks user engagement with each of the first content using a set of sensors, and determines the user's learning preferences in response to tracking positive engagement that the user has with a subset of the first content of a first type, format, or presentation. The education platform customizes second content for a second part of the particular subject to have more content of the first type, format, or presentation than the first content based on the learning preferences.

A system for AI-supported cash flow management and forecasting, consisting of: a data acquisition unit configured to retrieve transaction data from multiple financial data sources, the data acquisition unit incorporating encrypted API connections and secure data transfer protocols to ensure data security; a data preprocessing unit that is operationally linked to the data acquisition unit and includes a data cleansing module for removing anomalies, duplicates and missing values, as well as a normalization engine for standardizing data formats, thereby ensuring a uniform data structure for processing; a predictive analytics unit configured to receive processed data from the data preprocessing unit, wherein the predictive analytics unit comprises an ensemble of machine learning models, including recurrent neural networks and long short-term memory networks, each model being optimized for different prediction horizons; an adaptive learning component embedded in the predictive analytics unit and configured to regularly retrain the machine learning models based on updated historical data and feedback on financial performance to ensure the accuracy of predictions through iterative improvement; a decision support unit that is operationally coupled with the predictive analytics unit and configured to provide real-time recommendations for cash allocation and liquidity adjustments based on forecasted cash flow patterns; a liquidity warning module configured to continuously monitor cash balances, compare them with pre-configured liquidity thresholds, and generate alerts when balances approach critical limits, thus enabling proactive management of cash flow risks; a visualization module configured to present insights into cash flow, trend forecasts, and liquidity recommendations in an interactive graphical format to facilitate user evaluation and strategic decision-making; and a network interface integrated into the system, configured to connect to external data sources for dynamic updates.

An apparatus comprises at least one processing device configured to generate a first data structure comprising a numerical representation of content of a given system log associated with an information technology asset, to utilize the first data structure to determine a system log cluster to which the given system log belongs, to select non-anomalous system logs which are part of the system log cluster, and to perform contextual contrastive tuning of a machine learning model utilizing the selected non-anomalous system logs. The at least one processing device is further configured to generate a second data structure utilizing the tuned machine learning model which takes as input the first data structure, the second data structure characterizing (i) detected anomalies and (ii) causes of the detected anomalies. The at least one processing device is further configured to perform remediation actions, selected based on the second data structure, for the information technology asset.

The invention provides an unsupervised continuous emotion electroencephalogram analysis method and device based on deep reinforcement learning, and the method comprises the steps: in a prototype learning stage, obtaining sample features of time sequence signals corresponding to sample objects, carrying out the clustering processing of the sample features based on a K-Means algorithm, obtaining the global distribution information of emotion information in the time sequence signals, and carrying out the clustering processing of the global distribution information; in the reinforcement learning stage, a distribution-prototype reward function is determined according to the global distribution information, an Actor-Critic framework is adopted, a near-end strategy optimization algorithm PPO is used, an Actor executes a current action for a given time sequence signal based on a current strategy, Critic determines a current score of the current action according to a distribution-prototype reward fed back by the environment, and the current score of the current action is determined according to the distribution-prototype reward fed back by the environment. And the Actor performs strategy learning based on the current score until a preset iteration termination condition is reached, and a model used for determining key signals in the target time sequence signals according to the target time sequence signals is obtained. Therefore, the accuracy and reliability of the finally obtained model are improved.

The invention discloses a reinforcement learning method and system, and relates to the field of reinforcement learning, and the method comprises the steps: obtaining suboptimal demonstration data in a target application scene; performing imitation learning through the intelligent agent according to the suboptimal demonstration data to obtain an initial strategy; determining a plurality of behavior sequences based on the initial strategy; performing iterative training on the reward model according to the plurality of behavior sequences to obtain a trained reward model; based on the trained reward model, introducing a regular term into a contrast reinforcement learning algorithm of KL divergence to obtain a contrast reinforcement learning loss function with the regular term; and according to the plurality of behavior sequences, based on the trained reward model and the contrast reinforcement learning loss function with the regular term, performing reinforcement learning to obtain a final reinforcement learning strategy in the target application scene. According to the method, the reward model and the loss function of contrast reinforcement learning are improved, and after the agent simulates and learns the suboptimal demonstration data, a better strategy can be quickly learned.

Systems and methods for the use of various artificial intelligence (AI) / machine learning (ML) models with respect to various mobility aspects are described herein. The generation and use of L3 beam-level measurement predictions, L3 cell-level measurement predictions, L1 measurement predictions, network-based timing advance (TA) value predictions, and UE-based TA value predictions using corresponding ML models are discussed. Various examples of the inputs that may be used with respect to these ML models are discussed. The use of various ones of these predictions within mobility contexts including Layer-3 based handover, Layer 1 / Layer 2 triggered mobility (LTM), and conditional handover (CHO) are discussed.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention discloses a machine learning-based weld defect intelligent identification system, relates to the technical field of weld defect intelligent identification, solves the technical problems of multi-modal data fusion precision and robustness optimization and defect shielding or overlapping feature deficiency, and provides a machine learning-based weld defect intelligent identification method based on PSNR dynamic parameter adjustment and gradient weight optimization. The limitation of existing fixed parameter denoising is solved, the edge feature retention rate of cracks, air holes and other defects is improved, the omission ratio is reduced, improved DeepLabv3 + segmentation semantic masks are introduced and mapped to point cloud voxels, geometric + semantic double-attribute enhanced point clouds are formed, the defect area positioning accuracy is improved, and through a cross-modal attention module, the defect area positioning accuracy is improved. Weights are dynamically distributed according to illumination intensity and workpiece materials, feature waste caused by fixed weights is avoided, depth mutation and a shielding area with semantic defects are positioned by utilizing depth information of enhanced point cloud, real overlapping and projection overlapping can be effectively distinguished by combining an improved Poisson fusion algorithm, and the overlapping defect recognition accuracy is improved.

The invention discloses a multi-point linearization model prediction trajectory tracking control method based on a Gaussian process, and the method comprises the steps: building an ASV kinetic model for the trajectory tracking problem of an autonomous unmanned surface vehicle ASV; a GP prediction model is obtained by adopting unmodeled dynamics of a trajectory tracking system of Gaussian process GP approximation ASV; on the basis of dictionary scale limitation and a maximum likelihood method, updating criteria of dictionary data and hyper-parameters are established, and a GP prediction model is optimized; combining the optimized GP prediction model with the ASV kinetic model, using a learning-based model prediction control MPC method to construct a GP-MPC framework, and designing a terminal state set; on the basis of a multi-point linearization method, a local linear model predictive control LMPC method is combined, an algorithm for solving a GP-MPC framework optimization problem is designed, and real-time control over ASV trajectory tracking is achieved. According to the method, the calculation time is remarkably shortened while the ASV trajectory tracking precision is ensured.

The invention belongs to the technical field of sea transportation port congestion prediction, and relates to a port congestion prediction deep learning method based on ship AIS data. The core of the method is that two neural networks of Transform and LSTM are combined, a global sea transportation liner transportation network is integrated, and the constructed model can capture the temporal and spatial change characteristics of the port congestion state from a space angle and a time angle. According to the method, the prediction precision of the port congestion state can be effectively improved by combining the dependency relationship of space and time. The method provided by the invention is obviously superior to an existing learning-based method, can learn and efficiently capture the complex and potential space-time correlation of the port congestion state, and is good in effect and high in practicability. And a certain enlightenment is provided for a port operator to monitor the congestion state of the port in real time so as to improve the operation efficiency of the port.

A system generates text artifacts using a machine learned language model. The text artifacts may be provided to a search engine for providing to users along with search results. The system iteratively improves the set of text artifacts by performing the following steps. The system updates the prompt used to generate the text artifacts based on the performance of the text artifacts to obtain a new prompt. The system executes the machine learned language model using the new prompt to generate a new set of text artifacts. The system evaluates the new set of text artifacts to determine performance of each of the new set of text artifacts. These steps are repeatedly performed to improve the set of text artifacts.

The invention discloses a medical data structured extraction method based on machine learning, and the method comprises the following steps: carrying out the standardization processing of multi-source heterogeneous data in different medical scenes, constructing a time and condition two-dimensional filtering rule, and extracting preliminary data; and a modular index structure is formed according to medical process and technical attribute division. And generating analysis limiting conditions by fusing the medical knowledge graph and the knowledge base, guiding an analysis engine to perform semantic routing and reasoning, and outputting a structured result. Finally, disease identification and quality judgment are achieved, and structured information meeting or not meeting the standard is output. The method aims at efficiently extracting the structured information from various types of medical documents.

Some embodiments of a method may include: decoding a motion feature by accessing a motion bitstream; predicting a predicted feature based on the motion feature and one or more reference point cloud frames; decoding a first feature representing an occupancy status of a child level voxel; predicting a second feature based on the first feature and the predicted feature; and decoding a tree voxel occupancy status of the child level voxel via the second feature.

The invention discloses a touch and talk pen interactive content generation method based on adaptive learning. The method comprises the following steps: S1, acquiring a learning behavior data set of a user; s2, based on the learning behavior data set, constructing a dynamic knowledge graph of the user through an adaptive learning algorithm; s3, analyzing the learning behavior data set by using a query committee algorithm; s4, triggering a content generation module according to a bifurcation calculation result, wherein the content generation module generates learning content adaptive to the learning demand of the user based on the dynamic knowledge graph and the learning behavior data set of the user; s5, performing multi-mode interactive presentation of the learning content through the touch and talk pen; s6, performing feedback based on a learning behavior data set of the user for the learning content; s7, outputting a learning feedback report, wherein the learning feedback report is analyzed and generated based on the learning behavior data set of the user; and S8, repeating the steps S1 to S7. The interactive experience of the user is remarkably improved, the touch and talk pen is converted into an intelligent learning assistant from a passive content presentation tool, and efficient learning support and feedback are provided for the user.

The invention provides a multi-modal data alignment method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting a multi-modal training session into a to-be-trained feature extraction model, and obtaining a modal feature of each modal data in the multi-modal training session; performing comparative learning according to the modal features of each modal data in the multi-modal training session to obtain a first loss; according to the modal feature of each modal data in the multi-modal training session, constructing a fusion feature, and inputting the fusion feature into a preset question and answer model to obtain a question answer corresponding to the multi-modal training session; semantic features of the answers to the questions are extracted, the semantic features are compared with the fusion features, and second losses are obtained; and according to the first loss and the second loss, adjusting training parameters of a feature extraction model to obtain a trained feature extraction model, and extracting a plurality of mutually aligned modal features of the multi-modal real-time session through the feature extraction model. According to the invention, the feature alignment effect of the multi-modal data can be improved.

The invention relates to an Internet of Vehicles edge computing multi-target unloading method and system fusing dynamic environment modeling and improved SARSA, and belongs to the field of intelligent traffic and edge computing fusion. The method and the system comprise MEC environment perception and multi-dimensional state construction, dynamic reward feedback oriented to multi-dimensional performance indexes, intelligent decision model construction and learning based on improved SARSA, and antagonism training oriented to real disturbance. A high-fidelity environment model is constructed through a space-time attention mechanism, an SARSA algorithm is improved to realize hierarchical qualification trace attenuation and collaborative Q table updating, a multi-target hierarchical reward engine is combined to implement differential optimization on an emergency task and a conventional task, and an adversarial training mechanism is introduced to improve robustness. The core problems of high mobility, task diversity, resource limitation and the like in the Internet of Vehicles are effectively solved, the comprehensive performance is optimal in multiple dimensions of delay, energy consumption, resource utilization rate and the like, and the actual landing of the edge computing technology of the Internet of Vehicles is promoted.

Disclosed herein are deep-learning based systems, methods, and instrumentalities for medical decision-making. A system as described herein may implement an artificial neural network (ANN) that may include multiple encoder neural networks and a decoder neural network. The multiple encoder neural networks may be configured to receive multiple types of patient data (e.g., text and image based patient data) and generate respective encoded representations of the patient data. The decoder neural network (e.g., a transformer decoder) may be configured to receive the encoded representations and generate a medical decision, a medical summary, or a medical questionnaire based on the encoded representations. In examples, the decoder neural network may be configured to implement a large language model (LLM) that may be pre-trained for performing the aforementioned tasks.

The invention provides a federated learning differential privacy method based on Rayleigh divergence and adaptive noise adjustment, and aims to balance data privacy protection and model training performance and improve model accuracy and convergence speed of federated learning on the premise of protecting user data privacy. And the contradiction between privacy protection and model performance in the existing federated learning is solved. The method comprises the following steps: step 1, constructing a privacy loss quantification model based on Rayleigh divergence; 2, deducing a tight upper bound of a Gaussian noise standard deviation; 3, initializing noise parameters of the federated learning system; 4, the client side executes local model training and noise adding; 5, updating the aggregation model of the central server and evaluating the performance; step 6, implementing a self-adaptive noise adjustment decision based on model performance; and step 7, iterating federal learning training until convergence or completion.

A system receives a natural language request for configuring a computing infrastructure using a cloud platform. The system executes a machine learning based language model to generate infrastructure-as-code (IaC) to configure a cloud platform to obtain the desired computing infrastructure. The system may display the IaC generated by the machine learning based language model via a user interface as an example for use by the user. The system may send instructions to the cloud platform to provision computing infrastructure in accordance with the IaC obtained from the machine learning based language model. The system may repeatedly determine whether the desired computing infrastructure is deployed on the cloud platform and if the computing infrastructure currently provisioned on the cloud platform fails to match the desired computing infrastructure according to the natural language request, the system reconfigures the computing infrastructure deployed on the cloud platform.