Mosques Smart Domes System using Machine Learning Algorithms

TL;DR

This paper develops a machine learning-based system to automatically control mosque domes for improved ventilation and hygiene, using weather data and temperature to enhance comfort and reduce bacteria spread.

Contribution

Introduces a novel smart mosque dome control system using machine learning algorithms, specifically Decision Tree and kNN, for automatic ventilation management.

Findings

Decision Tree achieved 98% accuracy in predicting dome states.

kNN achieved 95% accuracy in the same task.

The system can effectively improve mosque air quality and comfort.

Abstract

Millions of mosques around the world are suffering some problems such as ventilation and difficulty getting rid of bacteria, especially in rush hours where congestion in mosques leads to air pollution and spread of bacteria, in addition to unpleasant odors and to a state of discomfort during the pray times, where in most mosques there are no enough windows to ventilate the mosque well. This paper aims to solve these problems by building a model of smart mosques domes using weather features and outside temperatures. Machine learning algorithms such as k Nearest Neighbors and Decision Tree were applied to predict the state of the domes open or close. The experiments of this paper were applied on Prophet mosque in Saudi Arabia, which basically contains twenty seven manually moving domes. Both machine learning algorithms were tested and evaluated using different evaluation methods. After comparing the results for both algorithms, DT algorithm was achieved higher accuracy 98% comparing with 95% accuracy for kNN algorithm. Finally, the results of this study were promising and will be helpful for all mosques to use our proposed model for controlling domes automatically.