Air pollution is one of the major environmental worries in recent time. Abrupt increase in the concentration of any gas leads to air pollution. The cities are mostly affected due to the abundance of population there. One of the worst gaseous pollutants is OZONE (O3). In this paper, we propose three predictive models for estimation of concentration of ozone gases in the air which are Random Forest, Multivariate Adaptive Regression Splines and Classification and Regression Tree. Evaluation of the prediction models indicates that the Multivariate Adaptive Regression Splines model describes the dataset better and has achieved significantly better prediction accuracy as compared to the Random Forest and Classification and Regression Tree. A detailed comparative study has been carried out on the performances of Random Forest, Multivariate Adaptive Regression Splines and Classification and Regression Tree. MARS gives the result by considering less variables as compared to other two. Moreover, Random Forest takes a little more time for building the tree as the elapsed time was calculated to 45 s in this case. In addition, variable importance for each model has been predicted. Observing all the graphs Multivariate Adaptive Regression Splines gives the closest curve of both train and test set when compared. It can be concluded that multivariate adaptive regression splines can be a valuable tool in predicting ozone for future. © Springer International Publishing AG 2018.

Sanjiban Sekhar Roy

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Pratyush C

Barna C.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Predicting Ozone Layer Concentration Using Multivariate Adaptive Regression Splines, Random Forest and Classification and Regression Tree

Soft Computing Applications Advances in Intelligent Systems and Computing

Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.

International Journal of Engineering Research in Africa

Classifying Spam Emails Using Artificial Intelligent Techniques

After lung cancer, breast cancer is known to be the greatest cause for death among females [20]. The improving effectiveness of machine learning approaches is being given a lot of importance by medical practitioners for breast cancer diagnosis. The paper proposes an effective hybridized classifier for breast cancer diagnosis. The classifier is made by combining an unsupervised artificial neural network (ANN) method named self organizing maps (SOM) with a supervised classifier called stochastic gradient descent (SGD). Also a comparative analysis is performed between the proposed approach and three supervised state of the art machine learning techniques decision tree (DTs), random forests (RF) and support vector machine (SVM). Initially SGD method is used in isolation for the classification task and then it is made to perform the classification after being hybridized with the unsupervised ANN technique on Wisconsin Breast Cancer Database (WBCD) [10]. The comparison is based up on classification accuracy that is produced by generating a confusion matrix. For verifying consistency of accuracy values, the classification task was repeated with Internet Advertisements Dataset [11]. The results of the classification experimentation using hybridization of SOM with SGD are much more superior to SGD in isolation. All the accuracy values have been computed after achieving a ten-fold cross validation on the both the datasets to further verify the classifier's performance. © 2015 IEEE.

2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

An effective hybridized classifier for breast cancer diagnosis

The diagnosis of the arythema disease is a real difficulty in dermatology. It causes redness induced in the lower level of the skin by hyperemia of the capillaries. It can harm several skin damages, inflammations. In this paper, we have put our efforts to design a diagnostic approach based on Support Vector Machine (SVM) with linear kernel by classifying the erythemato-squamous disease. SVM being a large margin classifier is a powerful pattern recognition and machine learning methodology that is widely used for both linear and non-linear classification problems. Comparing testing on different kernel methods, we have noticed that our method gives the better accuracy. Choosing the optimal value of the parameters is a crucial criterion and this was achieved by performing 3 fold cross-validations. © 2015 IEEE.

2015 International Conference on Computing Communication Control and Automation

A Novel Diagnostic Approach Based on Support Vector Machine with Linear Kernel for Classifying the Erythemato-Squamous Disease

Predicting stock exchange rates is receiving increasing attention and is a vital financial problem as it contributes to the development of effective strategies for stock exchange transactions. The forecasting of stock price movement in general is considered to be a thought-provoking and essential task for financial time series' exploration. In this paper, a Least Absolute Shrinkage and Selection Operator (LASSO) method based on a linear regression model is proposed as a novel method to predict financial market behavior. LASSO method is able to produce sparse solutions and performs very well when the numbers of features are less as compared to the number of observations. Experiments were performed with Goldman Sachs Group Inc. stock to determine the efficiency of the model. The results indicate that the proposed model outperforms the ridge linear regression model. © Springer International Publishing Switzerland 2015.

Advances in Intelligent Systems and Computing Afro-European Conference for Industrial Advancement

Stock Market Forecasting Using LASSO Linear Regression Model

Intrusion Detection System (IDS) is an application software which detects the presence of hostile or intrusive elements inside the system. As the nature and type of the intrusions are continuously changing, a simple IDS cannot completely tackle the security threat. In this paper, we have proposed an IDS model, which classifies different types of intrusion attacks based on Stacking classifier. Stacking is an ensemble based classifier. We have achieved good accuracy while classifying the KDD-Cup 99 dataset and that has been achieved with 10 fold cross validation. © 2014 IEEE.

Journal	Data powered by TypesetSoft Computing Applications Advances in Intelligent Systems and Computing
Publisher	Data powered by TypesetSpringer International Publishing
ISSN	2194-5357
Open Access	0