Abstract
AbstractIndoor environment pollution is one of the significant concerns related to the individual's health of every category. As a majority of the population is spending their time indoors, the research on environment monitoring is realized on priority. In this research, the correlation between the indoor environment and health is determined to predict the health severity of pregnant females in real‐time. The proposed solution employs IoT‐enabled smart sensors for data acquisition. The accumulated data is processed over the fog‐cloud platform for data quantification. To classify the irregular events from the processed data, the machine learning‐assisted Weighted‐Naïve Bayes (W‐NB) classification technique is deployed on the fog layer. Furthermore, a deep learning‐inspired multilayered convolutional neural network‐long short‐term memory (M‐CNN‐LSTM) model is proposed to determine the scale of health severity. Moreover, a two‐phased alert generation mechanism is proposed to deal with the case of a medical emergency by generating notifications to the concerned medical specialists. The performance of the system is validated by performing several experimental simulations over the environmental and health datasets comprised of nearly 38 356 instances. The performance of the proposed solution is evaluated in terms of event classification, severity prediction, temporal delay efficiency, and overall performance stability. The calculated outcomes registered considerable performance over the several state‐of‐the‐art decision‐making approaches.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Transactions on Emerging Telecommunications Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.