Double Exponential Smoothing Slime Mould Algorithm For Disease  Detection In Iot Healthcare System

doi:10.1140/epjp/s13360-025-06032-6

機構典藏 > College of Engineering > Department of Artificial Intelligence > Journal Article > Item 987654321/129364

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/129364

Title:	Double Exponential Smoothing Slime Mould Algorithm For Disease Detection In Iot Healthcare System
Authors:	Chen, Tzu-Chia
Date:	2025-01-31
Issue Date:	2026-07-02 12:05:25 (UTC+8)
Publisher:	Springer Nature
Abstract:	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.
Relation:	The European Physical Journal Plus 140, 90
DOI:	10.1140/epjp/s13360-025-06032-6
Appears in Collections:	[Department of Artificial Intelligence] Journal Article

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	6	View/Open

Loading...