Deployment of Tile Using Machine Learning

The deployment of PZT-based tiles is crucial, coupled with the use of available renewable energy sources. According to this viewpoint, the purpose of this study is to determine the poIr optimization (PO)and tile deployment in University Rajasthan's residential campus, Jaipur. In accordance with the frequency of movement among the campus' four key sites (Entrance gate, back gate, Nescafe, and food outlet) are major places of movement. In the study Support vector regression (SVR), Random Forest, kernel, and nearest-neighbor (k-NN), and Linear regression (LR) are four different machine learning techniques employed for PO. These techniques are used to optimize tile power in relation to sensor quantity and subject weight. The data was acquired using the prototype tile model and scaled up using generative adversarial modelling (GAN). Two alternative statistical criteria (MSE and accuracy) are discussed in the paper to assess the efficacy of these algorithms. According to the results, the MSE values for each algorithm range from 80 to 4493, respectively. In every instance, LR showed the poorest MSE result. Model accuracy was expected to range from 0.93 to 0.9946 by the MSE of all models. In conclusion, the present paper is reporting that SVM machine learning algorithm can used for power prediction of model due to least MSE and high accuracy 99%. The tile deployment was also anticipated by the LR algorithm, and as a result, the tile could be placed on the university's main entrance gate because the frequency of movement is more as compared to other places.