It is always important to monitor the quality of air that we inhale to protect ourselves from the respiratory diseases. In this paper, we present different regression models to forecast air quality index (AQI) in particular areas of interest. Support vector regression (SVR) and linear models like multiple linear regression consisting of gradient descent, stochastic gradient descent, mini-batch gradient descent were implemented. In these models, the air quality index (AQI) is dependent on pollutant concentrations of NO₂, CO, O₃, PM_2.5, PM₁₀ and SO₂. Among these models, support vector regression (SVR) exhibited high performance in terms of investigated measures of quality.

Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multiple linear regression (MLR) consisting of batch gradient descent (BGD), stochastic gradient descent (SGD), mini-BGD (MBGD) and CGD algorithms, and support vector regression (SVR), are implemented. In these models, the AQI is the dependent variable and the concentrations of NO ₂ , CO, O ₃ , PM _2.5 , SO ₂ , and PM ₁₀ for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.

This paper aims to establish a non-linear regression model based on Mamdani fuzzy inference system and examine the performance of the model based on various metrics such as correlation, index of agreement, mean absolute percentage error, mean absolute error and root mean square error. The data used in this operation is collected from Delhi Punjabibagh with pollutants spanning from Nitrogen dioxide to particulate matter 10μm with ozone and carbon monoxide considered to comprehensively cover the base of inputs. The resultant air quality index is then measured with the calculated ones based on the metrics discussed above.

Background/Objectives: Object identification in colour images is a complex task, especially when the background and lighting conditions of the environment are uncontrolled. Methods/Statistical analysis: This paper proposes an object identification algorithm to identify the light green tea leaves from the tea plants. Findings: In this paper we examine the wavelet transform, one of the most recent mathematical tools related to image processing, for identifying our desired object. Here, we concentrated on identifying the light green tea leaves. The paper is divided into two main parts. In the first part, we illustrated the compression for a colour image, by using two different wavelets transforms i.e. the Haar wavelet and Integer wavelet transform (IWT). In this we have analysed the behaviour of these two wavelet transform with different tea leaves images and identified the most appropriate wavelet transform that can perform better process to compress the given colour image. To analyse the performance of the wavelet transform with the colour images, we calculated Peak signal to noise ratio (PSNR) and compression ratio. The second part deals with Segmentation, which includes edge segmentation and clustering i.e. K-means. Applications/Improvements: The enhancement has been performed for edges. K-means clustering is used to separate the colours from the image, whereas edge based segmentation gives the shapes of the desired regions. Finally the light green tea leaves are identified by using above mentioned methods. These theoretical aspects are illustrated through MATLAB.