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.

中文翻译：

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使用机器学习算法的清真寺智能圆顶系统

世界上数以百万计的清真寺正面临着通风和除菌困难等问题，尤其是在高峰时段，清真寺内的拥挤导致空气污染和细菌传播，此外还有难闻的气味和不适感。祈祷时间，在大多数清真寺中，没有足够的窗户使清真寺通风良好。本文旨在通过使用天气特征和外部温度构建智能清真寺圆顶模型来解决这些问题。机器学习算法，例如 k 最近邻和决策树，被用于预测圆顶打开或关闭的状态。本文的实验应用于沙特阿拉伯的先知清真寺，该清真寺基本上包含 27 个手动移动的圆顶。两种机器学习算法都使用不同的评估方法进行了测试和评估。比较两种算法的结果后，与 kNN 算法的 95% 精度相比，DT 算法实现了更高的 98% 精度。最后，这项研究的结果很有希望，将有助于所有清真寺使用我们提出的模型来自动控制圆顶。