5results about How to "Reduce sparsity" patented technology

This invention provides a drug interaction prediction method based on a bidirectional cross-view attention network, belonging to the field of drug interaction prediction technology. The method first constructs a Morgan fingerprint similarity view of the drug, an original DDI view, and a multi-scale diffusion view based on personalized PageRank. Then, a graph convolutional network with shared weights is used to co-encode the multiple views, generating a unified drug embedding representation. Finally, a bidirectional cross-view attention mechanism is used to achieve fine-grained interaction and alignment between the structural and attribute views, and interaction prediction is completed via a multilayer perceptron. This invention effectively alleviates the sparsity problem of the DDI network through multi-scale topology enhancement and achieves complementary enhancement between views using bidirectional cross-view attention, significantly improving the accuracy and generalization ability of drug interaction prediction. It can provide reliable technical support for drug development screening and clinical combined drug safety assessment.

The invention relates to the technical field of image processing, and discloses an operation scene real-time dynamic reconstruction method and system based on three-dimensional Gaussian sputtering. The method comprises the following steps: extracting spatial-temporal characteristics of a soft tissue deformation Gaussian subset through a HexPlane spatial-temporal encoder; fusing the decoded feature vectors to generate a soft tissue deformation parameter set; a deformation field Jacobian matrix of Gaussian elements in the soft tissue deformation Gaussian subset is calculated, a local volume strain rate is obtained through determinant operation, when the strain rate exceeds a preset threshold value, self-adaptive densification operation is executed, and new Gaussian elements inherit parent Gaussian element deformation parameters and are updated to a corresponding set; and combining the updated parameters to carry out probabilistic boundary allocation and transmissivity perception rendering to obtain a reconstruction result. The accuracy, the physical authenticity and the stability of real-time dynamic reconstruction of the operation scene are improved.

This invention belongs to the field of technology for technology achievement promotion, and particularly relates to a method for mining potential demand for technology achievements based on hypergraph reconstruction technology, including: S1, constructing a demander-technology achievement interaction graph; S2, constructing a hypergraph association matrix of demanders and a hypergraph association matrix of technology achievements; S3, using a graph neural network to capture the direct dependencies between demanders and technology achievements; using a hypergraph neural network to capture the global implicit dependencies between demanders and technology achievements; S4, combining the direct dependency encoding of demanders at each layer with the global implicit dependency encoding to obtain enhanced embeddings of demanders and technology achievements at each layer; S5, based on and calculating the demander S... i Select each scientific and technological achievement C j The probability prediction value is calculated, and the top k scientific and technological achievements are used as the recommendation results. This method can effectively address the problem of data sparsity.

The application relates to a federated learning assisted edge caching method based on an AE-DDPM model, and comprises the following steps: an edge computing network system model is established, including a base station, a remote cloud server and users; an AE-DDPM model based on federated learning is used for training; global prediction content popularity is obtained, and the most popular N contents are cached according to the cache capacity of the base station; the application extracts a user data potential feature vector through an AE model, reduces data dimension and sparseness, and then learns data distribution through a DDPM model to generate high-quality content popularity prediction; the application deploys a cache unit on an edge node, enables users to quickly obtain the pre-cached contents on the node, effectively improves the cache hit rate, reduces the time delay of the users in obtaining the contents, significantly improves communication efficiency, and simultaneously reduces the risk of user privacy leakage.

The invention relates to the technical field of artificial intelligence, can be applied to the medical field and the financial science and technology field, and discloses a graph migration-based recommendation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining original business data, constructing a heterogeneous graph based on the original business data, and obtaining auxiliary features; mapping the initial features and the auxiliary features of the nodes in the heterogeneous graph to obtain graph node embedding and auxiliary feature embedding; semantic information in auxiliary feature embedding is injected into graph node embedding to obtain migration enhancement graph embedding; embedding the migration enhanced graph to carry out graph volume accumulation to obtain enhanced graph features; embedding and fusing the enhanced image features and the auxiliary features to obtain target fusion features; and predicting preferences of the user on the articles based on the target fusion features to obtain preference information, and recommending the articles to the user based on the preference information. According to the method, the problem of data sparsity is effectively relieved, and the accuracy and robustness of a recommendation system in a cold start scene are improved.