Analysis of Noise Pollution during Dussehra Festival in Bhubaneswar Smart City in India: A Study Using Machine Intelligence Models

Abstract

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.

A Cloud Based Framework for Identification of IoT Devices at Smart Home Using Supervised Machine Intelligence Model

“…The simulation is performed in Orange data analytics tool [14] on a machine of 8GB RAM and a Core-i3 processor. The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] for the selection of the best model using the performance metrics like AUC, CA, F1, precision and recall. The performance metrics can also be referred from [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

“…The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] for the selection of the best model using the performance metrics like AUC, CA, F1, precision and recall. The performance metrics can also be referred from [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. However, we mainly focus on the CA of the models for selecting the best model for the proposed system to classify the device category.…”

A Cloud Based Machine Intelligent Human Activity Recognition System Using Internet of Things to Support Elderly Healthcare

“…The performance of the model is assessed in Orange tool [15] that is installed in a machine with 64 bit OS, 2.4 GHz processor speed, and 8 Gb ram. The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] for the selection of the best model using the performance metrics like AUC, CA, F1, precision (PR) and recall. The description of performance metrics can be referred from [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31].…”

“…The supervised models considered for this simulation are RF, kNN, NN, SVM, Tree, NB, AB, and LR [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] for the selection of the best model using the performance metrics like AUC, CA, F1, precision (PR) and recall. The description of performance metrics can be referred from [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. CA is mainly considered to classify the activity accurately.…”

A Machine Intelligent Framework for Detection of Rice Leaf Diseases in Field Using IoT Based Unmanned Aerial Vehicle System

“…The AI technology has many algorithms for solving the classification, regression, and clustering problems. The algorithms mostly used in machine learning (ML) [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are supervised, unsupervised, and hybrid. So, ML is also an important component of the proposed design where the classifier installed in the cloud will detect the actual disease.…”