## 1 Introduction
This solution produced remarkable results, and obtained 93.03% and 93.15% improvement, respectively, over the RSS ratio and RSS matching methods. Besides reducing positioning errors, the analysis and optimization of other parameters, such as the receiver angle [19] and the LED-ID detection accuracy [20], also contribute significantly to the quality of the system, especially when carried out with the support of ML. However, the influence of reflected waves and noises has not been deeply concerned in these articles. In our previous work [21], the detrimental effects of reflected waves on the VLC-based positioning accuracy as well as incomplete solutions in recent studies have been analyzed in great detail. From this constraint, we proposed a novel method (using kNN-RF) to decrease positioning error in the areas outside the center in the multipath reflection environment without signal pre-processing. We also utilized the importance rate function to reduce computational time, but we found that if we removed too many features in an effort to minimize execution time, the positioning accuracy was reduced. In this article, to achieve a much higher positioning accuracy and faster computational time under the negative influence of multipath reflection and noises, we suggest an innovative indoor positioning solution. This solution is based on the signal pre-processing technique and dual-function ML algorithms that contain machine learning classification (MLC) and machine learning regression (MLR) functions. The algorithms used were support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (kNN). The obtained results proved that the proposed technique can produce the mean positioning accuracy of 8.75 cm with SVM compared to 19.3 cm with kNN-RF in our previous work [21]. Depending on the selected algorithm, the CPU time also showed a significant improvement with 78.26% in the best case. The encouraging results can be applied to many different indoor environments, from public places (such as supermarkets, theaters, museums, and shopping centers) to private places (such as factories and warehouses). In addition, VLC-based positioning systems can be used for smart home applications and can be used in conjunction with smart canes and smart wheelchairs [8; 21]. VLC-based positioning systems can be used to locate the current position of a person carrying an optical sensor, and to help them determine the route to their destination inside a building. Obviously, applications with VLP using the proposed method are very diverse and useful in the world today. The main features of our solution can be summarized as follows:  
* We first performed noise reduction by using outlier and average filtering. These processes include noise filtering and elimination of low-intensity reflected signals. This type of signal pre-processing improves the accuracy of the later position estimation process. * Then these data were classified using the classification function of ML algorithms. Data points were assigned to one of two areas: the center area or the edge area. The area division was based on the correlation between the actual location of the receiver on the floor and the RSS from four groups of LED lights suspended from the ceiling. Data division by region is a unique idea that not only significantly reduces total execution time but also contributes to the improvement in positioning accuracy, due to the signal integrity within each individual area. * After noise reduction and area division, the regression function of the ML algorithms was used to predict the location of the receiver. The results show that the proposed solution greatly improved the execution time and positioning accuracy, despite being influenced by many adverse factors, including noises and reflected waves.