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Controlling noise pollution in smart cities is a big challenge nowadays due to rise in urbanization and industrialization. As population mass grows, the celebration of yearly festivals such as Dussehra in Bhubaneswar city is also getting popular. However, since this sound pollution is creating a risk to human health, regular monitoring is strictly needed. In this work, the noise pollution level of Bhubaneswar smart city during Dussehra 2020 is predicted using different supervised machine learning (ML) prediction models. The input parameters considered for this work are area or zones of Bhubaneswar city, time at which sound level recorded, equivalent continuous sound level (Leq in dBA), and noise level (high/low compared to the standard value). The data collected for training phase and testing phase by using different ML models is taken from State Pollution Control Board, Odisha, India, for the years 2015–2020. The supervised ML models taken in this work are Decision Tree (DT), Neural Network (NN), k-Nearest Neighbor (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). The predictions of the models are evaluated using Orange 3.26 data analytics tool. From the results, it was found that DT and RF show a higher classification accuracy, 92.5%, than that of other ML models. Moreover, it is observed that the probability of prediction of noise pollution level for the testing dataset for DT is higher for high noise level and for RF is higher for low noise level than other prediction models.

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Artificial neural network (ANN) is an important tool, which is used in numerous fields, such as computer vision, pattern recognition, signal processing, solving optimization problems, and voice analysis and synthesis. Many real-life problems, where the future events play a vital role, are based on the past history. For example, predicting the behavior of stock market indices and electrical load forecasting. In this paper, we establish an efficient learning algorithm for periodic perceptron (PP) in order to test in realistic problems, such as the XOR function and the parity problem. Here, the periodic threshold output function guarantees the convergence of the learning algorithm for the multilayer perceptron. By using the binary Boolean function and the PP in single and multilayer perceptron, XOR problem is solved. The performance of PP is compared with multilayer perceptron and the result shows superiority of PP over the multilayer perceptron.