Department of Information Management, National Chung Cheng University; Director of Chang-Hua Hospital, Chang-Hua County 51341, Taiwan.
Department of Information Management, National Yunlin University of Science & Technology, Douliu 64002, Taiwan.
Int J Environ Res Public Health. 2019 Apr 6;16(7):1233. doi: 10.3390/ijerph16071233.
Internet usage has increased dramatically in recent decades. With this growing usage trend, the negative impacts of Internet usage have also increased significantly. One recurring concern involves users with Internet addiction, whose Internet usage has become excessive and disrupted their lives. In order to detect users with Internet addiction and disabuse their inappropriate behavior early, a secure Web service-based EMBAR (ensemble classifier with case-based reasoning) system is proposed in this study. The EMBAR system monitors users in the background and can be used for Internet usage monitoring in the future. Empirical results demonstrate that our proposed ensemble classifier with case-based reasoning (CBR) in the proposed EMBAR system for identifying users with potential Internet addiction offers better performance than other classifiers.
近年来，互联网的使用呈爆炸式增长。随着这种使用趋势的增长，互联网使用的负面影响也显著增加。其中一个反复出现的问题涉及到有网瘾的用户，他们的互联网使用已经过度，扰乱了他们的生活。为了检测有网瘾的用户并及早纠正他们的不当行为，本研究提出了一个基于安全 Web 服务的 EMBAR（基于案例推理的集成分类器）系统。该 EMBAR 系统在后台监控用户，可用于未来的互联网使用监控。实验结果表明，与其他分类器相比，我们在 EMBAR 系统中提出的基于案例推理（CBR）的集成分类器在识别有潜在网瘾的用户方面具有更好的性能。
