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Article

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

by 1,*, 1 and 2,*
1
National Institute of Technical Teacher’s Training and Research, Chandigarh 160019, India
2
ESTGOH, Polytechnic of Coimbra, Rua General Santos Costa, 3400-124 Oliveira do Hospital, Portugal
*
Authors to whom correspondence should be addressed.
Academic Editor: Periklis Chatzimisios
Sensors 2022, 22(3), 1008; https://doi.org/10.3390/s22031008
Received: 13 November 2021 / Revised: 22 January 2022 / Accepted: 25 January 2022 / Published: 28 January 2022
(This article belongs to the Section Internet of Things)
Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset. View Full-Text
Keywords: indoor air quality; fuzzy inference system; pollution; optimization; public health indoor air quality; fuzzy inference system; pollution; optimization; public health
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MDPI and ACS Style

Saini, J.; Dutta, M.; Marques, G. ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment. Sensors 2022, 22, 1008. https://doi.org/10.3390/s22031008

AMA Style

Saini J, Dutta M, Marques G. ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment. Sensors. 2022; 22(3):1008. https://doi.org/10.3390/s22031008

Chicago/Turabian Style

Saini, Jagriti, Maitreyee Dutta, and Gonçalo Marques. 2022. "ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment" Sensors 22, no. 3: 1008. https://doi.org/10.3390/s22031008

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