9 results about "Rule-based system" patented technology

Disclosed in the present invention are a non-technical source hierarchical rule base system and risk assessment method for critical information infrastructure. The present solution comprises: first, constructing a rule base of first-level index attributes and second-level rule attributes, so as to form a hierarchical rule base model; second, further classifying and analyzing non-technical source data, and using a confidence rule base model to assess non-technical source rule indexes and safety of a critical information infrastructure system; and finally, with regard to the safety risk situations of critical information infrastructure institutions, fusing and analyzing technical source information and non-technical source information, so as to provide a more accurate risk assessment result. On the basis of a critical information infrastructure safety assurance system, the present solution classifies rules into different levels, so as to improve the detection efficiency and accurate assessment of rule bases.

A 2-way and 3-way compare-merge tool that allows consultants and customers to reconcile the changes that they have made with a generated copy of the resources. Systems and methods that provide an effective, easy-to-use comparison tool includes: a schema-aware compare; sophisticated handling of collection properties, especially nested collections; effective matching algorithms to recognize that two items are the same business object in spite of outward differences; an ability to accept user hints and input and persist user decisions; a structured report that reflects the structure of the business object; color formatting, tagging, and filtering to find the important changes among the large volume of differences (among the thousands of changes per resource). Furthermore, adding a common ancestor as a third data point in the 3-way compare identifies the source of changes.

Methods and systems are provided for automatically diagnosing a patient based on a reduced lead electrocardiogram (ECG), using one or more deep neural networks. In one embodiment, a method for automatically diagnosing a patient using a reduced lead ECG comprises, acquiring reduced lead ECG data, wherein the reduced lead ECG data comprises less than twelve lead signals, determining a type of each of the less than twelve lead signals, selecting a deep neural network based on the type of each of the less than twelve lead signals, and mapping the less than twelve lead signals to a diagnosis using the deep neural network. In this way, reduced lead ECG data may be mapped to a diagnosis using an intelligently selected deep neural network, wherein the deep neural network was trained on reduced lead ECG data comprising a same set of ECG lead types as the acquired reduced lead ECG data.

Techniques for restricting functionality of a user device are disclosed. A service restricts the functionality in response to a detection of a wireless signal emanating from a remote beacon device. The service detects the wireless signal emanating from the remote beacon device. In response to the detection of the wireless signal, the service restricts the functionality of the user device.

An adaptive workflow and creative automation system is disclosed that dynamically generates personalized outputs by integrating contextual variables based on a user’s personality traits, philosophical perspectives, and cognitive biases. The system comprises at least one computing device in operable communication with an application program, a user interface module for receiving user inputs, and a server configured to process those inputs and generate workflow automation tasks and creative content. An adaptive algorithm analyzes user data and adjusts system behavior in real time to produce contextually relevant outputs, while one or more data stores preserve user information and historical interactions for continuous optimization. Unlike traditional rule-based systems, the disclosed invention personalizes automation through behavioral modeling, enabling real-time adaptation of tasks and outputs aligned with the user’s evolving needs, beliefs, and decision-making tendencies. The system improves efficiency, creativity, and user alignment across both operational and creative workflows.

Provided are a method and a device for classifying specimens included in images, the method comprising the steps of: generating a plurality of specimen images from images of specimens; acquiring the type and the reliability score of a target specimen corresponding to each specimen image by using a classification model; determining, on the basis that the reliability score is included in a predetermined range, the type of the target specimen corresponding to the specimen image by using a reviewer model; and determining, on the basis of the type of the target specimen and information related to the specimen image, a medical classification by using a machine learning or rule-based system.

The invention relates to a system and method for evaluating the reliability of network management decisions generated by artificial intelligence, machine learning, rule-based systems or other automated decision mechanisms in cellular communication networks. The system consists of the Decision Generation Module (10), Decision and Data Analysis Module (20), Decision Reliability Analysis Module (30), Decision Implementation Module (40), Network Element (50) and Network Performance Monitoring Module (60). The automated network decisions generated by the Decision Generation Module (10) are analyzed by the Decision and Data Analysis Module (20) together with the network data used in generating the decision. Then, the Decision Reliability Analysis Module (30) performs a reliability assessment considering technical criteria such as data quality, data currency, past application success, current network status, etc. Based on the assessment result, the Decision Implementation Module (40) decides whether to implement the decision, postpone it, or submit it for operator approval. After the decision is implemented, the reliability assessment parameters are dynamically updated based on the feedback data obtained by the Network Performance Monitoring Module (60). This ensures that automatically generated network decisions are validated before implementation, reducing the likelihood of implementing erroneous decisions, increasing network service continuity, and achieving an adaptive and closed-loop decision validation mechanism that improves itself by learning from implementation results.

The invention discloses a network security supervision system based on an artificial intelligence technology, belongs to the technical field of network security, and aims to solve the problems of one-sided supervision view angle, poor traceability and weak model generalization ability caused by insufficient model generalization ability and insufficient multi-dimensional data fusion. The system comprises a data acquisition layer, a data preprocessing layer, an artificial intelligence analysis layer, a safety supervision layer and a dynamic optimization layer, solves the problem that a traditional supervision system is single in data, enhances the supervision view angle, and compared with a traditional rule base system, the analysis capability is higher, unknown threats can be identified, the traceability efficiency is greatly improved, and the method is suitable for popularization and application. In addition, the model in the application can also adapt to various scenes and has relatively strong adaptation capability, and the model is internally provided with an automatic disposal script, so that quick response of emergency alarm can be realized, and the supervision efficiency is improved.

A method and system for automatically diagnosing patients based on reduced-lead electrocardiograms (ECGs) using one or more deep neural networks are provided. In one embodiment, a method for automatically diagnosing patients using reduced-lead ECGs includes acquiring reduced-lead ECG data, wherein the reduced-lead ECG data includes fewer than twelve lead signals; determining the type of each of the fewer than twelve lead signals; selecting a deep neural network based on the type of each of the fewer than twelve lead signals; and mapping the fewer than twelve lead signals to a diagnosis using the deep neural network. In this way, the reduced-lead ECG data can be mapped to a diagnosis using an intelligently selected deep neural network, wherein the deep neural network is trained on reduced-lead ECG data including a set of ECG lead types identical to the acquired reduced-lead ECG data.