scholarly journals Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Akash Saxena ◽  
Shalini Shekhawat
Keyword(s):  
Support Vector Machine ◽  
Air Quality ◽  
Industrial Development ◽  
Learning Algorithm ◽  
Ambient Air ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Mathematical Framework ◽  
Primary Concern

With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.

scholarly journals Review on air pollution of Delhi zone using machine learning algorithm

2021 ◽  
Author(s):  
Anurag Sinha ◽  
Shubham Singh
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Learning Algorithm ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Support Vector ◽  
New Delhi ◽  
Data Set ◽  
The Future

The issue of pollution in urban cities is a major problem these days especially in cities like the New Delhi is detected with more number of toxic gases in air, which has deduced the air quality of New Delhi. Thus, predictive analytics play a significant role in predicting the future instances of air quality based on the historical data. Forecasting the air quality of these cities is mandatory to overcome its consequences. Several machines learning algorithm is widely used these days to predict the future instances. Such as random forest, support vector machine, regression, classification, and so on. Main pollutants which present in the air are PM2.5, PM10, CO, NO2 , SO2 and O3 . In this paper we have focused mainly on data set of New Delhi for predicting ambient air pollution and quality using several machines learning algorithm.

scholarly journals Impact of COVID-19 induced lockdown and unlock down phases on the ambient air quality of Delhi, capital city of India

Urban Climate ◽  
2021 ◽  
pp. 100945
Author(s):  
Mayank Pandey ◽  
M.P. George ◽  
R.K. Gupta ◽  
Deepak Gusain ◽  
Atul Dwivedi
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Capital City ◽  
Ambient Air Quality

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Ambient Air Quality Assessment with Particular Reference to Particulates in Western part of Jharia Coalfield, India

2015 ◽  
Vol 10 (2) ◽  
pp. 523-528 ◽  
Author(s):  
Gurdeep Singh ◽  
Amarjeet Singh
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Environmental Parameters ◽  
Ambient Air ◽  
Primary Energy ◽  
Underground Mines ◽  
Opencast Mining ◽  
Ambient Air Quality ◽  
Jharia Coalfield

India is in the list of fastest growing countries of the world. India's energy needs are also increasing due to population and industrial growth for improving quality of living style. In India, coal is major input infrastructure industries for example Power plants, Steel plants and Cement industries. India’s 52% of primary energy is coal dependent1. 66% of India's power generation depends upon coal production1. Jharia Coalfield (JCF) is falling in the Lower Gondwana Coalfields of India. The area of the JCF is about 450 km2. It is important for the major supply of precious coking coal required for steel plants in India. It is located in Dhanbad district of Jharkhand state of India, The latitude is 23° 39' to 23° 48' N and longitude is 86° 11' to 86° 27' E for the Jharia coalfield. Based on environmental parameters, all the 103 mines of BCCL have been grouped under 17 Clusters. A cluster consists of a group of mines with mine lease boundary lying in close vicinity and includes-Operating mines, Abandoned/ closed mines and proposed projects.The focused study area is in the western part of the Jharia coalfield is named as Cluster XV group of mines of BCCL consists of four mines, Kharkharee Colliery (UG), Dharmaband Colliery (UG), Madhuband Colliery (UG) and Phularitand Colliery (UG) .The present study was carried out with the objective to measure the ambient air quality of the study area with reference to particulate matter (SPM, PM10 & PM2.5). Ambient air monitoring results have shown that the observe air quality were found within the limit prescribed by MoEF / CPCB. It may due to Underground mines as there are pollution causing lesser activities involved in the UG mining process compared to opencast mining. Implementation of Master plan for Jharia coalfields for environmental management has also improve the air quality in the area10,11.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model

2020 ◽  
Vol 14 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Hai-Bang Ly ◽  
Binh Thai Pham
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Liquid Limit ◽  
Support Vector ◽  
Plastic Limit ◽  
Svm Model ◽  
Soil Shear Strength

Background: Shear strength of soil, the magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering. Objective: The main objective of this study is dedicated to the development of a machine learning algorithm, namely Support Vector Machine (SVM) to predict the shear strength of soil based on 6 input variables such as clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit. Methods: An important number of experimental measurements, including more than 500 samples was gathered from the Long Phu 1 power plant project’s technical reports. The accuracy of the proposed SVM was evaluated using statistical indicators such as the coefficient of correlation (R), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) over a number of 200 simulations taking into account the random sampling effect. Finally, the most accurate SVM model was used to interpret the prediction results due to Partial Dependence Plots (PDP). Results: Validation results showed that SVM model performed well for prediction of soil shear strength (R = 0.9 to 0.95), and the moisture content, liquid limit and plastic limit were found as the three most affecting features to the prediction of soil shear strength. Conclusion: This study might help in quick and accurate prediction of soil shear strength for practical purposes in civil engineering.

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