62 results about "Network mining" patented technology

The invention discloses a mining Trojan horse detection system based on flow analysis, which relates to the field of computer network security and comprises a connectionmining pool mining Trojan horsebehavior detection subsystem and a p2p mining network mining Trojan horse behavior detection subsystem. According to the invention, the static pcap data packet or the real-time flow is used as the input, two modes of detecting and connecting mine pool mining and p2p mining can be selected, the mining flow is analyzed through the field feature extraction or communication flow feature extraction and identification of the system, and the alarm information is output to the user. Aiming at the conditions of plaintext communication and ciphertext communication, the system has the capability of quickly processing mass data, and can meet the requirements of personal hosts and enterprise-level users at the same time.

The invention provides a multi-modal remote sensing image data detection method and system, electronic equipment and a storage medium, and the method comprises the steps: inputting multi-modal remote sensing image data of different time sequences into a multi-modal feature mining network, and outputting the fusion vector features of the multi-modal remote sensing image data of each time sequence, and inputting the plurality of fusion vector features into a change detection network, and identifying whether the multi-modal remote sensing image data with different time sequences have differences or not by the change detection network. The multi-modal feature mining network constructed by the invention is used for mining and fusing the features of the multi-modal remote sensing image data, so that the diversity, multi-time sequence and multi-feature requirements of a training data set are met, and the accuracy of network feature mining can be improved; and the abnormal states of the multi-modal remote sensing images with different time sequences are detected by constructing a multi-modal feature mining network and a change detection network, so that support is provided for researching the development trend of a detected area.

The invention relates to the technical field of intelligent service pushing, and particularly discloses an intelligent service recommendation method based on a neural network mining model, which comprises the following steps: acquiring user data and preprocessing the user data; taking the preprocessed user data as data source input, and constructing a neural network model in combination with an activation function, training data and adjusting errors; and calculating a utility function of the user according to data output of the constructed neural network model, calculating user similarity anduser interestingness of the user to a service product in combination with a recommendation algorithm, constructing a hybrid service recommendation model, and establishing and displaying a hybrid service recommendation list by the constructed hybrid service recommendation model. According to the service intelligent recommendation method based on the neural network mining model, the coverage serviceproduct range is wider, the same set of model is utilized, the service recommendation standard is unified, and the priority ranking problem of service product recommendation can be solved while the accuracy and timeliness of service product recommendation are met.

The invention discloses a large-scale complex network relational map mining and analysis platform and method and a compute readable storage medium, which are used to solve the problem of the large-scale application analysis caused by the multi-source heterogeneous data fusion in complex network depth mining. The invention solves the problem of analysis and mining of large-scale network from the viewpoint of complex network system, constructs a research map relation network mining analysis platform, at that same time, provides the practical application analysis and display of the complex network such as actual network key node retrieval, multi-source data fusion, network key node and hidden node mining and the like for the existing service aspect.

The invention provides a multi-disease variable site analysis platform based on a function network. The multi-disease variable site analysis platform comprises a variable gene sequencing detection module, a function enrichment analysis module, a function network construction module, a function network mining module and a shared molecular network identification module, wherein the variable gene sequencing detection module is used for finishing the fundamental analysis of sequencing data; the function enrichment analysis module is used for utilizing a function enrichment analysis tool to analyze a variable gene function; the function network construction module is used for constructing the function network according to a function enrichment analysis result; the function network mining module is used for screening stable network modules from the function network; and the shared molecular network identification module is used for identifying molecular modules shared by different diseases according to the network module. The multi-disease variable site analysis platform analyses differences of different diseases on an aspect of genomic level, establishes an incidence relationship among the diseases from the perspective of molecular function, can systematically identity a shared modular function module among different diseases, effectively analyzes the nosogenesis of similar phenotype diseases, discloses differences between the diseases from the level of genome, increases the comprehensive understanding of the diseases and is favorable for clinic diagnosis and treatment.

The invention relates a mining method of coals stored under waters, buildings or railways, which is characterized in that network mining, permanent support and integrated construction are carried out to coal strips and blocks stored under the waters, the buildings orthe railways; the network mining includes that a room, five to six meters wide from the bottom to the top is mined in a coal mine under the waters, the buildings or the railways, 10 to 20m posts are left and the room and the posts form network shape; the permanent support includes that the mined room is treated with the permanent support in time, and the permanent support has large intensity to guarantee that the deformation of the top plate and the two sides is comparatively small and ground above the waters, or of the buildings or the railways does not sink; the integrated construction includes that the technologies of hole drilling, explosion, coal loading and transport are adopted and equipment with integrated support is used for carrying out construction to the mined room. The mining method of the invention has the advantages that resource recovery rate is improved, investment is low and return rate is high; and not only an underground treatment space is provided for the recrements of a mine well, but also filling materials are provided for the mining under the waters, the buildings or the railways, thereby realizing the advantages of safe production, etc.

This invention relates to building a landmark database from web data. In one embodiment, a computer-implemented method builds a landmark database. Web data including a web page is received from one or more websites via one or more networks. The web data is interpreted using at least one processor to determine landmark data describing a landmark. At least a portion of the landmark data identifies a landmark. Finally, a visual model is generated using the landmark data. A computing device is able to recognize the landmark in an image based on the visual model.

The invention relates to a method for mining comparable network language materials. The method includes acquiring source language web pages by the aid of network crawlers and preprocessing the source language web pages to obtain source language documents; analyzing probabilities of cross-language topics of the source language documents and generating corresponding target language query phrases; submitting the target language query phrases to search engines and selecting front N documents to form a target language candidate similar document set; computing similarity degrees of the source language documents and target language candidate similar documents, sieving documents with high similarity degrees and constructing a comparable language material bank. The invention further discloses a device for implementing the method for mining the comparable network language materials. The method and the device have the advantages that the problem of ambiguity or long time consumption due to vocabulary translation can be solved; the source language documents come from specific website contents acquired by the network crawlers, the target language documents come from the integral internet, and accordingly the source language document utilization rate can be effectively increased; the source language documents are matched with the target language similar documents by the aid of topic distribution similarity, and accordingly the language material bank construction accuracy can be improved.

The invention provides a coordinate inversion method and system. The coordinate inversion method comprises the following steps: S1, receiving global positioning system (GPS) data of a spatial position and network mining data, comparing the attributes of the GPS data and the network mining data, and finding out the data with the same attribute; S2, confirming a plurality of sample points in the data with the same attribute, and calculating transformation parameters according to the plurality of sample points; and S3 carrying out coordinate inversion on the network mining data according to the transformation parameters. Compared with the prior art, the coordinate inversion method and system provided by the invention are high in degree of automation and processing efficiency.

The invention provides a target detection method. The method comprises the following steps: extracting image features to generate a feature map; performing up-sampling on the feature map to obtain an amplified feature map; connecting the amplified feature map to a category prediction head, a width and height prediction head and a central point offset prediction head; adding a category attention network into the category prediction head, and mining effective information between targets which are far away from each other within the category and between the categories but are semantically related; supervising training of each prediction head through supervising information generated by encoding a real target frame; and frame-selecting an identification object in the image to be detected according to a result output by each prediction head, and marking a classification result. According to the method, category attention for further judgment of target categories and scale adaptive coding for frame regression are combined, so that the network can associate intra-class and inter-class features, and effective information between intra-class and inter-class targets which are far away from each other and are semantically related is mined, meanwhile, more accurate frame selection can be carried out according to the scale change of the detection target, so that the detection accuracy and the frame selection precision are improved.