Khaled Hassan, Abu-Elnasr Osama, Elmougy Samir, Tolba A S
Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
Complex Intell Systems. 2021 Sep 7:1-12. doi: 10.1007/s40747-021-00508-5.
In recent years, the adoption of machine learning has grown steadily in different fields affecting the day-to-day decisions of individuals. This paper presents an intelligent system for recognizing human's daily activities in a complex IoT environment. An enhanced model of capsule neural network called 1D-HARCapsNe is proposed. This proposed model consists of convolution layer, primary capsule layer, activity capsules flat layer and output layer. It is validated using WISDM dataset collected via smart devices and normalized using the random-SMOTE algorithm to handle the imbalanced behavior of the dataset. The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93).
近年来，机器学习在不同领域的应用稳步增长，影响着个人的日常决策。本文提出了一种在复杂物联网环境中识别人类日常活动的智能系统。提出了一种名为1D-HARCapsNe的胶囊神经网络增强模型。该模型由卷积层、初级胶囊层、活动胶囊扁平层和输出层组成。使用通过智能设备收集的WISDM数据集对其进行验证，并使用随机SMOTE算法进行归一化处理，以处理数据集的不平衡问题。实验结果表明，所提出的1D-HARCapsNet具有潜力和优势，其准确率达到98.67%，精确率为98.66%，召回率为98.67%，F1值为0.987，与传统胶囊网络（准确率90.11%，精确率91.88%，召回率89.94%，F1值0.93）相比，性能有了显著提升。
