Whale Optimization Algorithm with Deep Learning-Based Usability Recommendation Model for Medical Mobile

Whale Optimization Algorithm with Deep Learning-Based Usability Recommendation Model for Medical Mobile
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© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : P. Sumathi, N. Malarvizhi
DOI :  10.14445/22315381/IJETT-V70I5P227

How to Cite?

P. Sumathi, N. Malarvizhi, "Whale Optimization Algorithm with Deep Learning-Based Usability Recommendation Model for Medical Mobile," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 251-257, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P227

Abstract
Mobile applications or `apps` assist people in managing health and chronic illnesses have become common and gained significant attention. But in-depth analysis is needed to know about the acceptability and usability for low-income, ethnically/nationally different populations who face a disproportionate chronic disease problem and its complications. The study intends to examine the usability of mobile medical applications to avail development and tailoring of patient-facing apps for various populations. In this view, this study develops a novel Whale Optimization Algorithm with long short-term memory (WOA-LSTM) Based Usability Recommendation Model for Medical Mobile Applications. A survey was conducted using a set of questionnaires for software quality assurance and software quality management practices. In this case, the LSTM model is employed for the recommendation process. The hyperparameters involved in the LSTM model are optimally tuned by WOA, resulting in improved performance. The WOA-LSTM model produces context-specific judgment and generalized suggestions for App recommendation in related extreme samples. A series of experiments were carried out to determine the effective performance of the WOA-LSTM model, and the results pointed out the efficient outcomes on user recommendation.

Keywords
Deep learning, Usability recommendation, Medical mobile application, Recommendation model, Whale optimization.

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