为医疗保健4.0调整人工神经网络。正弦余弦算法

Nemanja Milutinovic, S. Čabarkapa, M. Zivkovic, Milos Antonijevic, Djordje Mladenovic, N. Bačanin
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引用次数: 0

摘要

从2015年到2022年,医疗4.0将对医疗服务产生革命性影响。它包括机器学习(ML)、物联网(IoT)、雾计算和云计算。利用物联网提供的机器学习方法,采用雾和云计算原理,提高了医疗保健模型的性能和准确性。这些概念结合在一起,在研究人员的应用中脱颖而出,因为它们与最佳结果一起占主导地位。受正弦和余弦函数的数学特性的启发,正弦余弦算法(SCA)生成许多初始随机候选解,其目标是向外波动或向理想答案波动。元启发式算法可用于优化医疗保健4.0所依赖的人工神经网络(ANN)。已在该领域的四个不同数据集上对该解决方案进行了测试,并将这些测试的结果与使用与建议解决方案相同数据集的其他混合解决方案的结果进行了比较。结果支持这种新方法,因为它比所有的测试都具有普遍的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tuning Artificial Neural Network for Healthcare 4.0. by Sine Cosine Algorithm
From 2015 to 2022, healthcare 4.0 has made revolutionary impacts on health services. It includes machine learning (ML), internet of things (IoT), fog computing and cloud computing. The utilization of machine learning approaches supplied by IoT advances employing fog and cloud computing principles improves the performance and accuracy of healthcare models. These concepts bounded together are distinguished in their application with the researchers as they dominate alongside the best results. Inspirited by the mathematical traits of sine and cosine functions, the sine cosine algorithm (SCA) generates numerous initial random candidate solutions with the goal of fluctuation outwards or towards the ideal answer. The metaheuristic algorithm can be applied for optimization of an artificial neural network (ANN) on which the Healthcare 4.0 relies. The solution has been tested on four diverse datasets in this field as well as the results of those tests have been compared to those of other hybrid solutions with the use of same datasets as the suggested solution. The results are in the favor of the novel method, as it obtains general advantage over all tests.
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