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- 저자 : Ruochen Huang (검색결과 2 건)
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Ruochen Huang,Zhiyuan Wei,Wei Feng,Yong Li,Changwei Zhang,Chen Qiu,Mingkai Chen 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.4
As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.
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Video Ranking Model: a Data-Mining Solution with the Understood User Engagement
Chen, Yongyu,Chen, Jianxin,Zhou, Liang,Yan, Ying,Huang, Ruochen,Zhang, Wei Korea Multimedia Society 2014 The journal of multimedia information system Vol.1 No.1
Nowadays as video services grow rapidly, it is important for the service providers to provide customized services. Video ranking plays a key role for the service providers to attract the subscribers. In this paper we propose a weekly video ranking mechanism based on the quantified user engagement. The traditional QoE ranking mechanism is relatively subjective and usually is accomplished by grading, while QoS is relatively objective and is accomplished by analyzing the quality metrics. The goal of this paper is to establish a ranking mechanism which combines the both advantages of QoS and QoE according to the third-party data collection platform. We use data mining method to classify and analyze the collected data. In order to apply into the actual situation, we first group the videos and then use the regression tree and the decision tree (CART) to narrow down the number of them to a reasonable scale. After that we introduce the analytic hierarchy process (AHP) model and use Elo rating system to improve the fairness of our system. Questionnaire results verify that the proposed solution not only simplifies the computation but also increases the credibility of the system.
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