| Project/Area Number |
20K11955
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Iwate Prefectural University |
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Principal Investigator |
FUJITA HAMIDO 岩手県立大学, 公私立大学の部局等, 特命教授 (30244990)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2022: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2021: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2020: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 機械学習 / 知能システム / machine learnig / Health care prediction / 知能情報学関連 / Machine Learning / Health Care System / Deep Learning / Health care System |
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| Outline of Research at the Start |
Activity Recognition on Data Streams using Ensemble Learning for Health warning predictions is project to study several ensemble learning techniques to learn traceable and machine understanding representations and deep neural architectures to recognize entities and relations in data gathered from multi-sensing environment like ECG signals, hear beating and else. Successively, hybrid deep learning and ensemble techniques are to be examined to improve the health recognition on different extracted features. These are to be fused as aggregated prediction system for risk analysis in health care.
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| Outline of Final Research Achievements |
In this project, have used ensemble deep learning techniques by constructing Deep Neural Networks (DNNs) based on assembled CNN in architecture of two GPUs in cross layered connection. In addition, we have one GPU system, running as backup for training experiments using large scale data for comparison purpose. I could achieve good research results using zero shot learning on multi-variate data. Also, I have trained the deep learning architecture on dynamic data, and image data. The result was promising and therefore, we published it in International Journals.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の研究結果は、機械学習技術を用いた健康管理システムにおける早期予測、特に、心不全の早期予測に対して新たな知見を与えるものである。
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