# Movement Control of Smart Mosque's Domes using CSRNet and Fuzzy Logic Techniques

arXiv: https://arxiv.org/abs/2410.18123 · HTML: https://mlsanity.com/paper/2410.18123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 171 references that are in the map.
- First posted on arXiv 13 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Accelerating Innovation Through Analogy Mining](https://mlsanity.com/paper/1706.05585/) (2017)
- [The Journal Coverage of Web of Science and Scopus: a Comparative Analysis](https://mlsanity.com/paper/1511.08096/) (2015)
- [A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases](https://mlsanity.com/paper/1305.0377/) (2013)
- [Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A Comparative Study on a Novel Dataset](https://mlsanity.com/paper/1411.3519/) (2014)
- [A framework to discover potential ideas of new product development from crowdsourcing application](https://mlsanity.com/paper/1502.07015/) (2015)
- [Printed Arabic Text Recognition using Linear and Nonlinear Regression](https://mlsanity.com/paper/1702.01444/) (2017)
- [CASP-DM: Context Aware Standard Process for Data Mining](https://mlsanity.com/paper/1709.09003/) (2017)
- [Refining Student Marks based on Enrolled Modules Assessment Methods using Data Mining Techniques](https://mlsanity.com/paper/2009.06381/) (2020)
- [Mosques Smart Domes System using Machine Learning Algorithms](https://mlsanity.com/paper/2009.10616/) (2020)
- [Intelligent Risk Alarm for Asthma Patients using Artificial Neural Networks](https://mlsanity.com/paper/2009.12175/) (2020)

## Abstract (arXiv)

Mosques are worship places of Allah and must be preserved clean, immaculate, provide all the comforts of the worshippers in them. The prophet's mosque in Medina/ Saudi Arabia is one of the most important mosques for Muslims. It occupies second place after the sacred mosque in Mecca/ Saudi Arabia, which is in constant overcrowding by all Muslims to visit the prophet Mohammad's tomb. This paper aims to propose a smart dome model to preserve the fresh air and allow the sunlight to enter the mosque using artificial intelligence techniques. The proposed model controls domes movements based on the weather conditions and the overcrowding rates in the mosque. The data have been collected from two different resources, the first one from the database of Saudi Arabia weather's history, and the other from Shanghai Technology Database. Congested Scene Recognition Network (CSRNet) and Fuzzy techniques have applied using Python programming language to control the domes to be opened and closed for a specific time to renew the air inside the mosque. Also, this model consists of several parts that are connected for controlling the mechanism of opening/closing domes according to weather data and the situation of crowding in the mosque. Finally, the main goal of this paper has been achieved, and the proposed model has worked efficiently and specifies the exact duration time to keep the domes open automatically for a few minutes for each hour head.