Double Exponential Smoothing Slime Mould Algorithm For Disease Detection In Iot Healthcare System
學年 113
學期 1
出版（發表）日期 2025-01-31
作品名稱 Double Exponential Smoothing Slime Mould Algorithm For Disease Detection In Iot Healthcare System
作品名稱（其他語言）
著者 Tzu-Chia Chen
單位
出版者
著錄名稱、卷期、頁數 The European Physical Journal Plus 140, 90
摘要 This paper presents an algorithm, called the double exponential smoothing slime mould algorithm (DeSSMA), which is formulated to train deep learning models for the precise detection of diseases in patients. The DeSSMA is designed by integrating the principles of double exponential smoothing with the slime mould algorithm. The parameters, including energy depletion, link lifetime (LLT), and distance, are considered by the proposed DeSSMA as objectives aimed at optimizing data routing efficiency. In the base station, a deep residual network (DRN) is trained using the proposed DeSSMA algorithm, which is utilized for disease detection following the processes of data preprocessing, augmentation, and feature selection. Finally, performance evaluation of the DeSSMA-DRN framework is conducted using metrics such as energy consumption, LLT, accuracy, sensitivity, specificity, and receiver operating characteristic. The findings reveal that the proposed framework achieved a minimal energy depletion rate of 0.412 (J), an LLT rate of 0.318, an increased accuracy rate of 0.959, a high sensitivity rate of 0.967, and a specificity rate of 0.931.
關鍵字
語言 en_US
ISSN 2190-5444
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者
審稿制度 是
國別 DEU
公開徵稿
出版型式 ,電子版
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