الفهرس 
Predicting indoor air quality in buildings using internet of things and deep learning
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Abstract
Humans spend most of their lifetime indoor thus, it is important to keep indoor air quality within acceptable levels. As a result, many initiatives were evolved by multiple research centers or through academic studies to address the harmful effects of increased indoor pollutants on public health. This study introduces a system for monitoring different air parameters to evaluate the indoor air quality (IAQ) and to provide real time readings. The proposed system aims to enhance planning and controlling measures and to increase both safety and occupants{u2019} comfort. The system combines microcontrollers and electronic sensors to form an Internet of Things (IoT) solution that collects different indoor readings. The readings are then compared with outdoor readings for the same experiment period and prepared for further processing using Artificial Intelligence (AI) models. Results showed the IoT device high effectiveness in transferring data via Wi-Fi with minimum disruptions and missing data. The developed model was able to predict multiple air parameters with acceptable accuracy. It can be concluded that the proposed system proved itself as a powerful forecasting and management tool for monitoring and controlling IAQ