An Ensemble of Deep Learning with Optimization Model for Activity Recognition in the Internet of Things Environment
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 4 |
| Year of Publication : 2023 |
| Authors : C. Nithyeswari, G. Karthikeyan |
10.14445/23488379/IJEEE-V10I4P109
How to Cite?
C. Nithyeswari, G. Karthikeyan, "An Ensemble of Deep Learning with Optimization Model for Activity Recognition in the Internet of Things Environment," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 4, pp. 91-104, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I4P109
Abstract:
Lately, IoT (Internet of Things) based m-healthcare application is rising to give real-time servicing in the present global lifestyle. Cloud-based healthcare architecture provides best results than traditional approaches. Currently, Integrating IoT devices in the medical environment plays a crucial role in handling a massive amount of medical data. Thus, researcher workers aimed to automate the procedure of diagnosis and detecting diseases with the help of could computing techniques. Furthermore, deep learning and machine learning techniques used in the healthcare field allow healthcare professionals to focus, monitor, highlight and diagnose the region of the problem and present the accurate and required solution in a short duration. Therefore, this paper presents Artificial Bee Colony Optimization with Ensemble Deep Learning based Disease Diagnosis (ABCO-EDLDD) in the IoT atmosphere. The proposed ABCO-EDLDD procedure effectively identifies the existence of diseases in the IoT atmosphere. At the initial stage, the ABCO-EDLDD technique transforms the input data gathered by the IoT devices in different ways. Next, the ABCO algorithm is utilized for the optimal selection of feature subsets. For disease classification, an ensemble of DL models such as Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), and Bidirectional Long Short Term Memory (BiLSTM) model. Finally, the RMSProp optimizer is used for the optimum tuning of the DL models. The experimental evaluation of the ABCO-EDLDD algorithm takes place by implementing two medical datasets: the HAPT dataset and the heart disease dataset. The experimental results reported the improved performance of the ABCO-EDLDD procedure over other current techniques.
Keywords:
Healthcare, Internet of Things, Deep learning, Disease diagnosis, Feature selection, Ensemble learning.
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