Anomaly Detection Using Data Rate of Change on Medical Data
作者机构：AI Convergence Research InstituteChosun UniversityGwangju61452Republic of Korea Interdisciplinary Program of Architectural StudiesChonnam UniversityGwangju61186Republic of Korea AINTCHAIN SOFT Co.Ltd.Mokpo-si58750Republic of Korea BK21 Human Resources Development Project GroupChosun UniversityGwangju61452Republic of Korea
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2024年第80卷第9期
页 面：3903-3916页
核心收录：
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Anomaly data anomaly detection medical anomaly data cyber security rate of change
摘 要：The identification and mitigation of anomaly data,characterized by deviations from normal patterns or singularities,stand as critical endeavors in modern technological landscapes,spanning domains such as Non-Fungible Tokens(NFTs),cyber-security,and the burgeoning *** paper presents a novel proposal aimed at refining anomaly detection methodologies,with a particular focus on continuous data *** essence of the proposed approach lies in analyzing the rate of change within such data streams,leveraging this dynamic aspect to discern anomalies with heightened precision and *** empirical evaluation,our method demonstrates a marked improvement over existing techniques,showcasing more nuanced and sophisticated result ***,we envision a trajectory of continuous research and development,wherein iterative refinement and supplementation will tailor our approach to various anomaly detection scenarios,ensuring adaptability and robustness in real-world applications.
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