인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Edge computing, the Internet of Things (IoT), and artificial intelligence are game changers for emerging delay-sensitive and crisp-response applications and services. Smart systems, such as smart cities, smart health, are the major beneficiaries of these technologies. The incorporation of Edge computing and Artificial Intelligence of Things (AIoT) in healthcare has transformed patient monitoring, diagnosis, and treatment through real-time data acquisition and intelligent analysis. This study proposed an edge computing integration architecture for Parkinson's disease prediction in healthcare systems. The system integrates smart sensors, wearable devices, edge computing, and cloud infrastructure to facilitate ongoing health monitoring and data-driven decision-making. A hybrid machine learning model combining convolutional neural networks (CNN) with bidirectional LSTM is developed. Integrated with an edge-based integration architecture, it enables real-time patient health monitoring, accurate disease prediction and assisted data-driven decision-making. The experimental finding clearly supports the effectiveness of the proposed model, demonstrated by its evaluation metrics: an accuracy of 0.90, precision of 0.93, recall of 0.90, and an F1 score of 0.91. These values collectively demonstrate the model's strong predictive capability and balanced performance across key evaluation measures. The architecture achieves low latency, scalability, privacy, and seamless coordination between healthcare units.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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