For the front-end, large-scale data acquisition is usually realized through paging request mechanism, the efficiency of this mechanism is limited by the browser mechanism, server processing capability and network conditions. In order to achieve efficient processing and display of a large number of geographic data nodes in the front-end big data visualization platform, the scheme proposes a data loading optimization mechanism based on paging request mechanism, which comprises three modules: a detector, a record library and a recommender. The detector is used to detect request conditions in specific environments, the record library stores the detection results, and the recommender returns appropriate paging request results. The scheme not only improves the data processing efficiency, but also enhances the user interaction experience. The scheme can be used in the field of data visualization in various industries, especially for the rapid visualization of geographic data.

This paper focuses on the precise identification of potential cloud computer users utilizing telecom big data. By integrating multi-source heterogeneous data and leveraging key technical capabilities, including raw bitstream network data parsing and the construction of an innovative O-domain hierarchical tagging architecture, we develop a multidimensional user behavior profiling model. This model enables accurate identification of cloud computer usage patterns for both enterprise and individual users. Key innovations include: (1) a novel O-domain tagging framework that substantially enhances scenario classification accuracy; (2) an attentivestacking fusion model that dynamically prioritizes telecomspecific behavioral features to optimize prediction performance; and (3) a validated analysis plan demonstrating superior conversion outcomes and reduced operational costs in real-world deployments. The proposed attentive-stacking fusion model, trained on behavioral characteristics unique to telecom scenarios, significantly enhances the prediction accuracy of potential cloud computer user groups. Comparative experiments confirm that the analysis plan efficiently identifies high-conversion-potential cloud computer user segments, effectively addressing the challenge of high customer acquisition costs inherent in traditional marketing methods. This research establishes a novel pathway for telecom operators to leverage big data for targeted user mining, marketing expenditure reduction, and conversion efficiency improvement.

Due to the implementation of national security-related laws and regulations, data privacy protection and ownership issues have attracted much attention, meanwhile, the rise of technologies such as 5G, IOT, big data, and edge computing has promoted the digital transformation of data as a factor of production to empower social governance. At present, traditional machine learning still uses the method of data-centered large models for training and reasoning. This method brings about data fragmentation and island distribution and other problems, which have become the key problems restricting the popularization of Artificial Intelligence (AI) technology applications. In this paper, we explore a federated learning model for user complaint warning algorithm based on big data and other enterprise side data. The experimental result also shows that the prediction accuracy of the federated learning model and the traditional logistic regression model are within an acceptable range on the premise of ensuring user privacy and data security.

In quantitative trading, stock prediction plays a crucial role in portfolio optimization as it directly impacts the actual level of return. However, the trading market is complex, making return prediction a challenging task. To address this issue, existing works have utilized various technical indicators as inputs to enhance predictive accuracy. However, these indicators are often proprietary and kept confidential by quantitative funds and researchers, limiting their accessibility. In this paper, we propose a federated learning-based method that leverages multiple parties’ technical indicators for stock return prediction without disclosing them. The results demonstrate that the proposed method outperforms traditional methods in terms of prediction accuracy. Additionally, the proposed method achieves higher portfolio return through portfolio optimization using the Mean-variance Optimization model compared to traditional approaches. The proposed method offers a promising solution for stock return prediction while maintaining the confidentiality of technical indicators.

In the field of clinical chronic diseases, common prediction results (such as survival rate) and effect size hazard ratio (HR) are relative indicators, resulting in more abstract information. However, clinicians and patients are more interested in simple and intuitive concepts of (survival) time, such as how long a patient may live or how much longer a patient in a treatment group will live. In addition, due to the long follow-up time, resulting in generation of longitudinal time-dependent covariate information, patients are interested in how long they will survive at each follow-up visit. In this study, based on a time scale indicator-restricted mean survival time (RMST)-we proposed a dynamic RMST prediction model by considering longitudinal time-dependent covariates and utilizing joint model techniques. The model can describe the change trajectory of longitudinal time-dependent covariates and predict the average survival times of patients at different time points (such as follow-up visits). Simulation studies through Monte Carlo cross-validation showed that the dynamic RMST prediction model was superior to the static RMST model. In addition, the dynamic RMST prediction model was applied to a primary biliary cirrhosis (PBC) population to dynamically predict the average survival times of the patients, and the average C-index of the internal validation of the model reached 0.81, which was better than that of the static RMST regression. Therefore, the proposed dynamic RMST prediction model has better performance in prediction and can provide a scientific basis for clinicians and patients to make clinical decisions.

Traditional recommendation systems model user preferences based on past historical behaviors, thus unable to obtain dynamic user preferences. The conversational recommendation system (CRS) combines the conversational module with the recommendation module and overcomes the limitations by directly asking the user's preference for attributes. However, the existing CRS methods lack effective information propagation among various modules, making the model lack the basis for making correct decisions. In this paper, we propose an Employer-Employee network, which decomposes the actions into two stages, which are completed by two networks respectively. The Employer Network is responsible for analyzing information and making decisions (query or recommend), and the Employee Network is responsible for collecting information and performing tasks. Our contributions can be highlighted in three aspects: We first emphasize the importance of information propagation among multiple modules in the conversational recommendation system. Secondly, we propose an Employer-Employee (EE) network, which transforms each turn of action into a two-stage decision-making task handed over to two networks to complete. Thirdly, we conduct experiments on multiple datasets, and the experimental results show that our model achieves competitive performance compared with state-of-the-art baselines.