کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6872847 1440625 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fast and peer-to-peer vital signal learning system for cloud-based healthcare
ترجمه فارسی عنوان
سیستم یادگیری سریع سیگنال حیاتی و سریع برای مراقبت های بهداشتی مبتنی بر ابر
کلمات کلیدی
همکار به هم، نیمه مدل، دستگاه یادگیری شدید سیستم مراقبت بهداشتی هوشمند، ابر،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
Wearable devices in the Internet of Things (IoT) make home-based personal healthcare systems popular and affordable. With an increasing number of patients, such healthcare systems are challenged to store and process enormous volumes of data. Some medical institutions employ Cloud services to meet requirements of analyzing big data without considering sharing their own knowledge which could increase diagnostic accuracy. In order to obtain such collaborative healthcare community in the Cloud environment, we propose a peer-to-peer (p2p) learning system which is fast, robust and learning-efficient. Our proposed system continuously collects vital biosignals from wearable devices of users (e.g., chronic patients living alone at home) and analyzes the biosignals in real-time with Extreme Learning Machine (ELM). The traditional centralized learning models suffer in having huge communication costs to share massive amounts of personal vital biosignal data among the institutions for the training purpose. Our proposed p2p learning model can overcome this limitation by allowing every institution to maintain its own raw data while also being updated by other institutions' shared knowledge a.k.a semi-model which is lightweight output during the training process, as well as being smaller than raw data. The extensive experimental analysis demonstrates that our proposed p2p learning model is efficient in learning and sharing for patient diagnosis. We also show the potential impact under different network topologies, network sizes and the number of learning peers.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 88, November 2018, Pages 220-233
نویسندگان
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