## 5 Discussion  
The present study introduced a pioneering approach for estimating visual comfort in indoor environments, aiming to provide a non-intrusive and practical solution that delivers accurate results without compromising accuracy. By incorporating mathematical visual comfort estimation into a regression model, the proposed method exhibited superior performance compared to a traditional mathematical model, achieving an impressive improvement of 99%. Moreover, it required fewer computational resources and demonstrated a remarkable 95% faster processing time. Figure 19: November 2022. Figure 20: December 2022. In comparison to previous studies in this field, the findings of this research highlight several significant advancements. Firstly, the statistical approach presented in [19] offered valuable insights into evaluating thermal and visual comfort in free-running buildings. However, its assumption of a uniform distribution of light levels throughout the space limited its ability to capture the dynamic and spatially varying nature of visual comfort conditions. In contrast, our proposed approach overcame this limitation by incorporating mathematical visual comfort estimation, resulting in a more accurate assessment of visual comfort. The method proposed in [17] for predicting and optimizing long-term visual comfort in daylight spaces is noteworthy for considering both instantaneous and cumulative visual comfort over time. Nevertheless, it heavily relied on detailed information about the building design, such as window orientation, shading devices, and other relevant features. Conversely, our approach offered a more practical solution that did not require extensive architectural data while still achieving accurate results. Additionally, the calculation of the glare rating index, as outlined in [19], often necessitates a combination of objective measurements and subjective evaluations, which can be challenging to obtain. In contrast, our approach sidestepped the need for subjective evaluations, simplifying the process and increasing its applicability. The utilization of artificial neural networks (ANNs) for predicting daylight, visual comfort metrics, and view quality in the early design stages, as proposed in [18], demonstrated promising results, with an average accuracy close to 97%. However, the acquisition of the glare index remains a challenge due to the subjective evaluations required. In comparison, our approach incorporated a mathematical estimation of visual comfort into a regression model, addressing the limitations associated with subjective evaluations and providing a more comprehensive solution. In summary, the present study introduced an innovative approach for estimating visual comfort in indoor environments, offering several advantages over existing methodologies. By incorporating mathematical visual comfort estimation into a regression model, our method demonstrated remarkable improvements in accuracy, computational efficiency, and processing time. These findings highlight the potential of our approach to contribute significantly to the field of visual comfort assessment. Future research can explore the integration of additional parameters or the extension of the method to different types of indoor environment to further enhance its applicability and performance. Within this context, the proposed method for visual comfort assessment in indoor spaces has practical implications for various stakeholders. It could enhance occupant well-being and satisfaction by providing visually comfortable environments, support architects and facility managers in optimizing building design and operation, contribute to energy efficiency and sustainability efforts, enable building performance evaluation and comparisons, ensure compliance with standards and regulations, and achieve cost and resource efficiency with its improved performance and faster processing time.