Academic Journal
Deep edge intelligence-based solution for heart failure prediction in ambient assisted living
| العنوان: | Deep edge intelligence-based solution for heart failure prediction in ambient assisted living |
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| المؤلفون: | Md. Ishan Arefin Hossain, Anika Tabassum, Zia Ush Shamszaman |
| المصدر: | Discover Internet of Things, Vol 3, Iss 1, Pp 1-17 (2023) |
| بيانات النشر: | Springer, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Computer software |
| مصطلحات موضوعية: | Heart disease prediction, Edge intelligence, machine learning, IoT, Deep learning, Feed forward network, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765 |
| الوصف: | Abstract Heart failure and heart disease prediction in real-time is a highly significant necessity for the patients living under the observation of Internet of Things-based Ambient Assisted Living systems because cardiovascular diseases are the most common fatal chronic diseases. Most of the solutions regarding heart disease prediction in the Internet of Things-based medical systems are relying on server-based predictive analysis which can appear to be complex for generating real-time prediction notifications and unreliable in case of any network interruption occurrences. The suggested edge-based solution for the prediction of heart disease from collected sensor data in real-time using a proposed lightweight deep learning technique called Oversampled Quinary Feed Forward Network (OQFFN) provides a less complex framework and more reliable notification system in case of network failure for the disease prediction which also reduces the need of forwarding all the data to the server resulting in reduced network bottleneck. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2730-7239 |
| Relation: | https://doaj.org/toc/2730-7239 |
| DOI: | 10.1007/s43926-023-00043-4 |
| URL الوصول: | https://doaj.org/article/5473407e84524c028d142c8e6188a4f0 |
| رقم الانضمام: | edsdoj.5473407e84524c028d142c8e6188a4f0 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 27307239 |
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| DOI: | 10.1007/s43926-023-00043-4 |