182 results about "Expert system" patented technology

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning is disclosed. The system operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.

Techniques for implementing a fuzzy logic expert system / service to control sending of a requested media file and / or control triggering of a remedial action (e.g., not sending the media file) are described. According to some examples, a computer-implemented method includes receiving a request at a content delivery service from a user for a media file, generating a piracy risk score for the media file by a fuzzy logic expert service of the content delivery service, sending the media file to the user based on the piracy risk score not exceeding a threshold, and blocking the sending of the media file to the user based on the piracy risk score exceeding the threshold.

The invention discloses a gear machine tool expert system construction method based on a generative rule, and the method comprises the steps: firstly carrying out the multi-dimensional modeling of a gear and a machine tool through an object-oriented technology, enabling a gear model to distinguish the characteristics of an axis position, a tooth curve and the like through an interface, and enabling a machine tool model to distinguish the machining technology and the precision grade through an interface; secondly, establishing a generation type rule mapping system from geometric, precision, material and functional attributes of the gear to machine tool movement, precision, process and rigidity attributes; finally, an intelligent reasoning system including forward reasoning, reverse verification, conflict coordination and self-learning iteration is constructed, and automatic and accurate matching from gear design requirements to machine tool optimization configuration is achieved. According to the method, the problems of high subjectivity and low efficiency caused by dependence on expert experience in the prior art are solved, and knowledge-driven intelligent decision making is realized.

The invention relates to the technical field of crossing of bioinformatics, artificial intelligence and plant genomics, and discloses a plant single cell gene expression prediction method, system, device and medium. A plant single cell gene expression prediction model realizes dynamic feature fusion of a DNA sequence and chromatin accessibility signals through a gated cross attention mechanism; the problem that a traditional single-mode model cannot model regulation and control dynamic association is effectively solved, and the result interpretability is enhanced; a hybrid expert system and a load balancing design are adopted to significantly improve the recognition capability of the model for rare cell types, and a decoupling prediction head design supports efficient transfer learning; a DNA long sequence processing mechanism and nucleosome scale feature coding ensure cross-species compatibility; an end-to-end automatic process and a dynamic parameter optimization framework greatly improve the practicability; a'prediction-verification 'closed-loop support system can be constructed for molecular breeding, and high-precision and interpretable prediction of plant single-cell gene expression is realized.

The invention relates to the technical field of desulfurization, in particular to a desulfurization slurry control method and device based on a self-attention mechanism, and the method comprises the steps: collecting parameters of a desulfurization system in real time, building a variable correlation thermodynamic map, and constructing a hybrid prediction model for simulating chemical reaction kinetic characteristics by using a delayed feedback mechanism; outputting a multi-target variable prediction result in the desulfurization efficiency, the SO2 concentration at an outlet and the pH value of the slurry; generating a slurry supply strategy based on the multi-target variable prediction result and a pre-constructed slurry supply strategy optimization model in combination with a preset expert system rule; and dynamically optimizing a slurry supply strategy according to the operation state of the desulfurization system. Therefore, the problems that in the prior art, due to the fact that univariate adjustment and empirical parameter setting are generally taken as the principal thing, the overall modeling capacity for the multivariate coupling relation in the desulfurization process is lacked, control response lag is likely to be caused, the adjustment precision is insufficient, robustness to external disturbance is weak, and emission stability and operation economical efficiency are difficult to consider at the same time are solved.

The invention relates to the technical field of stereo garage safety management, and discloses a garage parking space safety risk monitoring method comprising the following steps: S1, multi-dimensional information real-time acquisition: comprehensively acquiring vehicle state, equipment operation data, environmental parameters and other multi-dimensional information through sensors at three levels of vehicle, equipment and environment; basic data is provided for subsequent analysis; and S2, data fusion and dynamic calibration: carrying out fusion processing on the collected multi-dimensional data, and introducing a dynamic calibration mechanism to correct the deviation of the sensor so as to improve the accuracy and reliability of the data. According to the garage parking space safety risk monitoring method, static and dynamic risk data are processed by integrating data models such as a support vector machine and a random forest, physical risks are calculated in combination with a physical model, empirical rules are supplemented by an expert system, results are fused through a collaborative reasoning mechanism, and weights are dynamically adjusted, so that the evaluation accuracy and adaptability are improved; potential risks are accurately identified, and reliable support is provided for safe operation of the garage.

A method and a system of configuring custom operator defined rules for managing radio resources of user equipment in a wireless communication system. The system includes a plurality of User Equipment (UE), a plurality of wireless base stations configured to host Centralized Unit (CU) and Distributed Unit (DU) software, a rules engine, a first Application Programming Interface (API) wherein the rules engine is programmed with operator defined rules via the first API, and a second API coupled with the CU and DU software and configured to invoke the rules engine upon an occurrence of one or more defined events, wherein one or more of the operator defined rules is invoked via API calls to execute operator defined rules that match one or more inputs provided by at least one of the CU and DU hosted by at least one of the plurality of base stations.

The invention provides a power plant equipment sound abnormity identification and health prediction method based on granular computing and an LSTM network, and the method comprises the steps: employing an array pickup and a high-dynamic-range microphone array in a complex and high-noise background environment of a power plant, and combining an anti-interference filtering and beam forming algorithm, thereby achieving the sound abnormity identification and health prediction of the power plant equipment. According to the method, directional, multi-channel and non-contact sound acquisition is carried out on key parts of equipment, acquired sound signals are preprocessed, converted into time domain, frequency domain and time-frequency domain representations and coded into multi-dimensional numerical vectors, and compared with a rule-based expert system, the method has higher generalization ability and real-time response ability; a large language model is introduced, so that the output is closer to an engineering language and is suitable for operation and maintenance personnel to understand and execute; a self-defined knowledge base or safety semantic filtering is supported, and closed-loop deployment in an industrial field is facilitated; the system can be in butt joint with an intelligent inspection system, and full-link linkage of voice recognition, health assessment and suggestion generation is achieved.

The invention provides a sepsis multi-task prediction method and system, and belongs to the technical field of disease prediction based on a mathematical model, and the method comprises the steps: obtaining a symptom data record of a to-be-detected patient; and processing the obtained symptom data records by using a pre-trained classification model to obtain a final task result. According to the method, a multivariable time sequence in medical health record data of a patient is utilized, time representation containing sepsis pathological characteristics is obtained through a time mode of modeling data based on a self-attention encoder, and the trend of the condition of the patient changing along with time is obtained; correlating different medical features according to features including patient condition change trends, so that each feature can perceive other features mutually, and mining global feature representation of the patient; and inputting the global feature representation into a task-aware hybrid expert system, and routing the unified global feature representation into different expert systems, thereby obtaining task-level representation and realizing multi-task prediction.

A classification model for multimodal human physiological data, and a training method therefor, a classification method for multimodal human physiological data, and a device are provided. The classification model includes: a multi-headed self-attention module, a normalization module, a fusion expert system, and a decision module. The multi-headed self-attention module is configured to perform feature extraction on multimodal synchronous data. The normalization module is configured to generate normalized feature data based on the extracted feature data. The fusion expert system includes: an electroencephalogram expert subsystem, an electrocardiogram expert subsystem, an electrodermal activity expert subsystem, and a multimodal synchronous fusion expert subsystem each configured to perform a classification task based on the corresponding normalized feature data. The decision module is configured to calculate a final classification result based on the above four classification results.

The invention discloses a fracturing equipment intelligent cross-domain layout system and method based on digital twinning and transfer learning, and the system comprises a parameterized model building module which is used for building a basic model of an equipment and environment digital twinning model in a fracturing scene; the scene unit interaction module is used for solving an optimal solution set of the equipment number and the point location in the fracturing well site by taking the fracturing operation chain and the spatial constraint as a comprehensive weighted objective function; and the cross-domain scene coupling module fuses the features of the multi-source domain and the target domain through a cross-domain scene knowledge multiplexing mechanism to output a scene candidate scheme, the optimal layout scheme is output through verification and iteration of an expert system, and the system analyzes and renders to complete the intelligent cross-domain layout of the fracturing scene. According to the method, through digital twin model rehearsal simulation, a cross-domain knowledge reuse mechanism and expert system correction collaborative layout, a well site layout scheme with multiple operation areas and multi-type equipment function linkage is efficiently constructed, the well site construction period is shortened, the pressure test risk before construction is reduced, and the overall efficiency of fracturing mining operation is improved.

The application relates to the field of intelligent path planning, and discloses an intelligent fertilizing vehicle path planning method suitable for a greenhouse environment, a road surface obstacle risk value evaluation mechanism based on an expert system and AHP (analytic hierarchy process) level analysis, road surface point cloud data established by combining binocular vision algorithm and deep learning technology, and comprehensive evaluation of road surface obstacle risks is realized. The obstacle avoidance mechanism of the traditional path planning method is effectively improved, so that the fertilizing vehicle can effectively deal with the path planning scene of a muddy path and a plant-shielded path in the greenhouse. The application improves the accessibility, timeliness and safety of the path planning of the greenhouse fertilizing vehicle.

The application discloses a device pressure test method, device, storage medium and program product, relates to the technical field of pressure test, and comprises the following steps: constructing a pressure test strategy of a first system and a second system in a to-be-tested device, and performing corresponding pressure test operations; collecting first test data of the first system and second test data of the second system, and extracting first feature data of the first test data and second feature data of the second test data; generating target features based on the first feature data and the second feature data; generating a diagnosis result based on the target features by using a diagnosis model constructed based on an expert system and a machine learning model, and generating a diagnosis report based on a target report template. The application can collect pressure test data of each operating system, extract features for fusion, perform correlation analysis and automatic diagnosis by using a fusion expert system and a machine learning model, realizes automatic and accurate positioning of a performance bottleneck, and improves diagnosis comprehensiveness and accuracy.

The invention belongs to the field of artificial intelligence, and relates to an AI digital human expert system localization deployment method and device and computer equipment, and the method is based on 6G millimeter wave wireless communication, and comprises the steps: constructing a 6G millimeter wave private network and a safety control calculation base; establishing a parameter-level risk grading and data sensitivity grade classification mechanism; constructing a scene-adaptive dynamic beam management engine; a hierarchical privacy protection system based on a data sensitivity level is constructed, and quantum encryption, differential privacy, federated learning and homomorphic encryption are fused to form a differential security policy; constructing a communication-computing power-data-intelligent full-link cooperative scheduling system; and deploying a localized AI large model-expert system integrated engine, and carrying out closed-loop iterative optimization on the integrated engine. The scene self-adaptive capability is improved, resource scheduling is precise, a hierarchical privacy protection system is set, full-link collaborative closed loop is realized, and integrated iterative evolution can be carried out.

The invention provides an intelligent water and fertilizer integrated control method based on an expert system, and belongs to the technical field of agricultural informatization and intelligent agriculture. The method comprises the following steps: firstly, collecting multi-dimensional information of soil, crops and environment by using a multi-source sensor, completing structured modeling, then constructing a soil-crop-environment space-time diagram neural network water and fertilizer demand prediction model, abstracting each element of a farmland into heterogeneous nodes, presenting spatial association by virtue of a diagram structure, and constructing a soil-crop-environment space-time diagram neural network water and fertilizer demand prediction model; dynamic changes of water and fertilizer requirements are described in combination with a time module. And in a water and fertilizer decision-making link, a prediction result is input into an expert system, and a regulation and control decision fusing data intelligence and expert experience is formed through rule verification and logical reasoning correction constraint. Meanwhile, an expert rule credibility evaluation and self-updating method based on a causal Transform is provided, the contribution degree of the rule is quantified, the weight is dynamically adjusted, and self-learning evolution of the rule base is achieved; the accuracy, stability and long-term adaptability of water and fertilizer regulation and control are effectively improved.

The application discloses an e-commerce anti-user portrait system and method based on artificial intelligence and blockchains, comprising a data invasion containment module, a fuzzy user portrait module, an audit recommendation algorithm module and an intelligent comprehensive price comparison module; the data invasion containment module uses blockchain technology to clearly define the data ownership of each node, and decentralizes the data distribution to return the data ownership to the individual user, and desensitizes the authorized user data; the fuzzy user portrait module uses artificial intelligence to automatically search for irrelevant type goods or even opposite type goods; the audit recommendation algorithm module automatically audits the personalized recommendation algorithm, checks the fairness of the algorithm, and manually handles user complaints by accessing an expert system; the intelligent comprehensive price comparison module compares the prices of different e-commerce platforms and the historical prices of the same e-commerce platform, and integrates and displays the price comparison results. The application can effectively solve the problems of personal information leakage and big data killing in the e-commerce field, and improve user satisfaction.

The embodiment of the invention provides an electric power industrial control system security detection method and device, and the method comprises the steps: obtaining network traffic and log data, extracting traffic features and log features, predicting the weight of each expert model through employing an expert weight prediction model according to the extracted features, and predicting the detection task probability through employing a task prediction model, performing enhancement processing on the weight of each expert model by using a preset attention weight to obtain an enhanced weight of each expert model, calculating an expert matching degree according to the detection task probability and a preset expert ability matrix, calculating a correction weight of each expert model according to the expert matching degree and the enhanced weight of each expert model, and obtaining a correction result of each expert model; and based on the correction weight of each expert model, selecting a predetermined number of expert models, and performing detection by using the selected expert models to obtain a detection result. According to the method, the accuracy and the dynamic adaptability of routing decision and the accuracy and the integrity of a detection result of a multi-expert system in a complex electric power industrial control scene can be improved.

A method for automatic adjustment of a power grid operation mode based on reinforcement learning is provided. An expert system for automatic adjustment is designed, which relies on the control sequence of thermal power units, enabling automatic decision-making for power grid operation mode adjustment. A sensitivity matrix is extracted from the historical operating data of the power grid, from which a foundational thermal power unit control sequence is derived. An overload control strategy for lines within the expert system is devised. A reinforcement learning model optimizes the thermal power unit control sequence, which refines the foundational thermal power unit control sequence and provides the expert system with the optimized control sequence for automatic decision-making in power grid operation mode adjustment. This method offers a solution to balancing and absorption challenges brought about by fluctuations on both the supply and demand sides in high-proportion renewable energy power systems.

An expert system for innovation discovery in the field of artificial intelligence (AI) applies decision tree algorithms structured around a rules-based reasoning methodology. The system trains on innovation datasets comprising both data and non-data types, including target variables representing key attributes of innovations and proximal variables that approximate them. Through a machine learning architecture, decision nodes are configured to evaluate these variables and generate predictive models. The architecture enables continual learning via reinforcement mechanisms and communication ports that facilitate data flow from external tools, cloud storage, and software. Differentiated nodes are assigned weights, roles, and activation logic to refine decision-making and improve model accuracy. The expert system integrates human-defined heuristic rules with AI capabilities to support early-stage ideation, concept development, and innovation pattern recognition. This system can operate as an autonomous AI agent, a core reasoning engine, or as part of a digital business model in platform ecosystems to enhance innovation discovery, reduce hallucinations, and support transparent, verifiable AI outcomes.

The invention discloses an automatic assembly process generation method based on Inte3D and InteAI collaboration, and the method comprises the steps: obtaining a related file of a to-be-processed part input by a user, carrying out the preprocessing of the related file of the to-be-processed part, so as to obtain a preprocessed related file, inputting the preprocessed related file into a pre-established process generation model, and carrying out the processing of the to-be-processed part. And the assembly process of the to-be-machined part is obtained. The technical problem that an existing method based on a three-dimensional process planning system excessively depends on geometric information of a model and is disjointed with an actual production scene can be solved; and the technical problems that an existing method based on an industrial intelligent platform neglects implicit knowledge and the robustness of a process scheme is insufficient are solved. And an existing method based on an expert system is difficult to adapt to rapid iteration of a product, and the novel scene rule matching success rate is low. And the technical problems of assembly interference and unreasonable parameters due to the lack of closed-loop optimization in the existing three methods are solved.

The method for realizing the air separation equipment production technology intelligent question-answering machine driven by the air separation field knowledge graph mainly includes three stages. Firstly, the HanLP-kf language processing package and the Neo4j database are used to realize the standardized representation and storage of the air separation equipment production technology field knowledge. Secondly, the production technology field knowledge graph is constructed by taking the production technology field literature as the data source. Thirdly, the Multi-Raspberry Pi network acquisition system is used to acquire real-time production data in the distributed air separation equipment production field, so as to construct the dynamic real-time knowledge graph of the air separation production technology field. Finally, the Naive Bayes classification method is used to enable the air separation equipment production technology intelligent question-answering machine to analyze and feed back various professional problems input by the user interface. The method can effectively overcome the shortcomings of the traditional knowledge base and expert system in knowledge updating and iteration, and through the self-learning ability of the question-answering machine, the knowledge graph is managed and more complex and dynamically updated knowledge is applied, so as to provide the technical management personnel participating in the air separation equipment production with the field knowledge advancing with the times.

The present invention describes how a workgroup expert system can be established to mimic a real world task-expert and possess the equal four expert-Human-Intelligent (expert-HI) Problem-Solving (PS) competencies, including: 1) real-time concurrent workgroup-AI PS-processing, 2) real-time semantic workgroup-AI PS-transactions, 3) real-time task-domain workgroup-AI PS-collaborations and 4) real-time fine-grained adaptive workgroup-AI PS-services, based on multi-node workgroup architectures with derived workgroup-software methods and developed workgroup-system disciplines. Therefore, according to the Turing Test, the workgroup expert-task system should be deemed "Strong-AI-PS competent" for solving any task that is handled by one task expert with the help of functional processors, while all the current nodes-service-infrastructures with four node-AI-PS competencies can only mimic a group of real world functional processors with pre-developed logic-modelled processor-Human-Intelligent (processor-HI) PS-competencies for solving a pre-defined / specific multi-function-modelled task-oriented problem and should be deemed "Weak-AI-PS competent".

The invention provides a multispectral detection method. The method comprises the following steps: firstly, acquiring visible light-infrared image pairs under severe weather conditions (dark light, rainy days, snowy days, foggy days and the like); secondly, inputting the visible light-infrared image pair into a double-flow feature extraction network to respectively extract multi-scale key features of each modal; thirdly, constructing a lightweight feature fusion network based on a hybrid expert system, evaluating the contribution degree of each modal feature to a detection effect, and extracting complementary features; and finally, designing a complementary feature multi-scale feature aggregation network and a target classification positioning detection head, and obtaining target position and category information. The method can be applied to target detection under extreme weather conditions.

Provided are a model training method, a vehicle control method, and a related apparatus, which may be applied to the field of artificial intelligence. The method includes: obtaining road condition information of a target vehicle; obtaining target information based on the road condition information by using a first neural network model, where the target information is a driving intention prediction of the target vehicle, a driving route prediction, or an interaction behavior prediction between the target vehicle and an environment; and updating the first neural network model based on the target information by using an expert system or a result obtained through processing the road condition information and a label corresponding to the road condition information by the expert system.

This invention discloses an unsupervised industrial defect detection method, system, and apparatus based on a multimodal expert system, comprising: acquiring an unlabeled industrial defect image dataset and text prompts; inputting the dataset into a multimodal expert system to obtain corresponding prediction results; filtering preliminary annotation results; filtering the preliminary annotation results based on prior constraints to obtain refined labels; inputting the industrial defect image dataset and refined labels into a self-supervised model for training, and outputting pseudo-labels after training; performing dual filtering on the pseudo-labels using dynamic thresholds and prior constraints; inputting the industrial defect image dataset and the pseudo-labels obtained from the current training with the dual-filtered dataset into the previously trained self-supervised model for retraining, outputting pseudo-labels, and returning to perform dual filtering until a well-trained self-supervised model is obtained; using the trained self-supervised model to predict the industrial defect images to be tested, and obtaining the corresponding industrial defect detection results. This improves the accuracy and efficiency of detection.

The present application relates to the technical field of artificial intelligence, in particular to a multi-modal human physiological data classification model, a training and classification method and equipment, aiming to solve the classification problem under the condition of multi-modal physiological data loss. The classification model comprises a multi-head self-attention module, a normalization module, a fusion expert system and a decision module. The fusion expert system comprises four expert subsystems of electroencephalogram, electrocardiogram, skin electricity and multi-modal synchronous fusion. The multi-head self-attention module is used for feature extraction of multi-modal synchronous data; the normalization module is used for generating normalized feature data according to the extracted features; the four expert subsystems of electroencephalogram, electrocardiogram, skin electricity and multi-modal synchronous fusion are respectively used for performing classification tasks according to the normalized feature data corresponding to the electroencephalogram, electrocardiogram, skin electricity and multi-modal synchronous data; and the decision module is used for calculating a final classification result according to the four classification results. The present application can improve the accuracy of the classification result under the condition of multi-modal data loss.

The invention provides a water turbine speed regulation expert system based on a knowledge graph, and the system comprises a fault detection module which carries out the real-time monitoring and prediction of the operation state of a speed regulation system, and builds a fault recognition mechanism through constructing a fault knowledge graph of a hydroelectric generating set speed regulation system, and integrating and analyzing historical fault data, expert knowledge and operation experience; the fault removal module is used for clearly displaying association and causal relationships among various faults by constructing a knowledge graph, providing a fault analysis view for operation and maintenance personnel and formulating a processing scheme; the operation and maintenance decision-making module is used for integrating various operation and maintenance data, fault records and processing experiences by constructing a fault knowledge graph of the hydroelectric generating set speed regulation system, and providing a scientific decision-making basis for operation and maintenance personnel; and the intelligent speed regulation optimization module integrates water turbine type characteristics, a water hammer model and pipeline characteristic data through a knowledge graph, formulates a personalized speed regulation strategy and integrates safety constraints, the challenges faced by the speed regulation system are coped through the system, and the stability and efficiency of power production are improved.

The application discloses a distributed operation and maintenance management system of energy storage power station based on Internet of Things, and relates to the technical field of power system automation and safe operation and maintenance. The passive intelligent locking device adopts passive radio frequency coupling power supply design, is internally provided with a radio frequency receiving coil and an energy storage capacitor, obtains electric energy by receiving radio frequency signals emitted by an intelligent computer key, and is internally further provided with a secondary locking mechanism. The secondary locking mechanism comprises a mechanical blade structure and a magnetic pin structure. An operation order expert system based on natural language processing is embedded in an operation and maintenance management cloud platform, is used for analyzing unstructured operation instructions and automatically generating standard operation orders, and simultaneously performs five-prevention logic verification on the operation orders in combination with a logic locking program. The application has the advantages that the secondary locking structure of the mechanical blade and the magnetic pin greatly improves the anti-technology opening time, and in combination with AES-256 encrypted communication, the physical and information dual safety is ensured.

The invention relates to the technical field of artificial intelligence and false information detection, in particular to a multi-source false news detection method and system based on multi-modal fusion, and the method comprises the steps: carrying out the feature extraction of a text, including sentence-level embedding and word-level embedding, and screening features through a text expert system in combination with a weighting coefficient output by a text gating module; carrying out feature extraction on the image by using a masking automatic encoder to extract deep features, and screening the features through an image expert system in combination with a weighting coefficient output by an image gating module; using a target detection algorithm to extract a subgraph of an important region of the image, inputting the text, the complete image and the subgraph into CLIP to obtain features, calculating a similarity matrix of the text and the image region through a cross-modal alignment module, and splicing the alignment features with the output features of the text expert system and the output features of the image expert system to obtain fusion features; and carrying out modal re-correction on the fusion features by using a dual Transform structure. According to the invention, the accuracy and robustness of multi-modal false news detection are improved.