In this era of exponential growth in the scale of data, information overload has become an urgent problem, and the use of increasingly flexible sensor cloud systems (SCS) for data collection has become a mainstream trend. Recommendation algorithms can search massive data sets to uncover information that meets the needs of users based on their interests. To improve the accuracy of recommendation scoring, this paper proposes a score prediction algorithm that combines deep learning and matrix factorization. To address the problem of sparse scoring data, our study employs a sensor cloud system to collect data information, preprocesses the collected information, and then uses a deep learning model combined with explicit and implicit feedback to generate recommendations. The proposed algorithm, MF-NeuRec, combines fusion matrix decomposition and the NeuRec model score prediction algorithm. The algorithm employs user-based and item-based NeuRec algorithms to extract the feature vectors of users and items under implicit feedback data. The obtained user and item feature vectors are integrated in a certain ratio through the use of matrix decomposition under the display feedback data. The user and item feature vectors obtained by the algorithm are merged and analyzed to predict how users will rate items. Experiments demonstrate that the algorithm can improve the accuracy of recommendations.
In recent years, the number of fraud cases in basic medical insurance has increased dramatically. We need to use a more efficient method to identify the fraudulent users. Therefore, we deploy the cloud edge algorithm with lower latency to improve the security and enforceability in the operation process. In this paper, a new feature extraction method and model fusion technology are proposed to solve the problem of basic medical insurance fraud identification. The feature second-level extraction algorithm proposed in this paper can effectively extract important features and improve the prediction accuracy of subsequent algorithms. In order to solve the problem of unbalanced simulation allocation in the medical insurance fraud identification scenario, a sample division method based on the idea of sample proportion equilibrium is proposed. Based on the above methods of feature extraction and sample division, a new training and fitting model fusion algorithm (tree hybrid bagging, THBagging) is proposed. This method makes full use of the balanced idea of the tree model algorithm based on Boosting to fuse, and finally achieves the effect of improving the accuracy of basic medical insurance fraud identification.
Usually, in addition to the main content, web pages contain additional information in the form of noise, such as navigation elements, sidebars and advertisements. This kind of noise has nothing to do with the main content, it will affect the tasks of data mining and information retrieval so that the sensor will be damaged by the wrong data and interference noise. Because of the diversity of web page structure, it is a challenge to detect relevant information and noise in order to improve the true reliability of sensor networks. In this paper, we propose a visual block construction method based on page type conversion (VB-PTC). This method uses a combination of site-level noise reduction based on hashtree and page-level noise reduction based on linked clusters to eliminate noise in web articles, and it successfully converts multi-record complex pages to multi-record simple pages, effectively simplifying the rules of visual block construction. In the aspect of multi-record content extraction, according to the characteristics of different fields, we use different extraction methods, combined with regular expression, natural language processing and symbol density detection methods which greatly improves the accuracy of multi-record content extraction. VB-PTC can be effectively used for information retrieval, content extraction and page rendering tasks.
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