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.

Sanjiban Sekhar Roy

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Mittal D

Basu A

Abraham A.

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.

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VIT University

Stock Market Forecasting Using LASSO Linear Regression Model

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

Customer satisfaction is an important term in business as well as marketing as it surely indicates how well the customer expectations have been met with by the product or the service. Thus a good prediction model for customer satisfaction can help any organization make better decisions with respect to its services and work in a more informed matter to improvise on the same. The problem considered in this study is optimization of customer satisfaction for the customers of San Francisco International Airport. This paper adopts three classification models Naive Bayes, MultiClass Classifier, K-Star and IBK as potential classifiers for prediction of customer satisfaction. The customer satisfaction depends on various factors. The factors which we consider are the user ratings for artwork and exhibitions, restaurants, variety stores, concessions, signage, directions inside SFO, information booths near baggage claim and departure, Wi-Fi, parking facilities, walkways, air train and an overall rating for the airport services. The ratings are obtained from a detailed customer survey conducted by the mentioned airport in 2015. The original survey focused on questions including airlines, destination airport, delays of flights, conveyance to and from the airport, security/immigration etc. but our study focuses on the previously mentioned questions. Graphs are plotted for actual and predicted values and compared to find the least amount of deviation from the actual values. The model which shows least deviation from actual values is considered optimal for the above mentioned problem. © Springer International Publishing AG 2018.

Soft Computing Applications Advances in Intelligent Systems and Computing

Prediction of Customer Satisfaction Using Naive Bayes, MultiClass Classifier, K-Star and IBK

Emails are a very important part of our life today for information sharing. It is used for both personal communication as well as business purposes. But the internet also opens up the prospect of an enormous amount of junk and useless information which overwhelms and irritates us. These unnecessary and unsolicited emails are what comprise of spam. This study presents the application of a classification model to classify spam emails from using a model- Deep Support Vector Machine (Deep SVM). Moreover, other classifier models like Support Vector Machine (SVM), Artificial Neural Network models have also been implemented to compare the performance of proposed Deep SVM model. Furthermore analysis has been done to compare all the performances using available numerical statistics obtained from these models to find the best model for the purpose. Spam filtering is a very essential feature in most email services and thus effective spam classification models are pertinent to the current digital communication scenario and various work has been done in this area. © Springer International Publishing AG 2018.

Spam Email Detection Using Deep Support Vector Machine, Support Vector Machine and Artificial Neural Network

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.

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

This paper presents an image classification model using a convolutional neural network with Tensor Flow. Tensor Flow is a popular open source library for machine learning and deep neural networks. A multi-category image dataset has been considered for the classification. Conventional back propagation neural network has an input layer, hidden layer, and an output layer but convolutional neural network, has a convolutional layer, and a max pooling layer. We train this proposed classifier to calculate the decision boundary of the image dataset. The data in the real world is mostly in the form of unlabeled and unstructured format. These unstructured data may be image, sound and text data. Useful information cannot be easily derived from neural networks which are shallow i.e. the ones which have less number of hidden layers. We propose deep neural network based CNN classifier which has a large number of hidden layers and can derive meaningful information from images. © 2017 IEEE.

2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

Classifying multi-category images using deep learning : A convolutional neural network model

Security of data is considered to be one of the most important concerns in today’s world. Data is vulnerable to various types of intrusion attacks that may reduce the utility of any network or systems. Constantly changing and the complicated nature of intrusion activities on computer networks cannot be dealt with IDSs that are currently operational. Identifying and preventing such attacks is one of the most challenging tasks. Deep Learning is one of the most effective machine learning techniques which is getting popular recently. This paper checks the potential capability of Deep Neural Network as a classifier for the different types of intrusion attacks. A comparative study has also been carried out with Support Vector Machine (SVM). The experimental results show that the accuracy of intrusion detection using Deep Neural Network is satisfactory. © Springer Nature Singapore Pte Ltd. 2017.

Communications in Computer and Information Science Mathematics and Computing

A Deep Learning Based Artificial Neural Network Approach for Intrusion Detection

Expert Systems with Applications

A Bayesian regularized artificial neural network for stock market forecasting

2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing

Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction

Pattern Recognition Letters

A sparse version of the ridge logistic regression for large-scale text categorization

2010 International Conference on Advances in Recent Technologies in Communication and Computing

Stock Market Prediction Using a Hybrid Neuro-fuzzy System

Neurocomputing

Flexible neural trees ensemble for stock index modeling

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Afro-European Conference for Industrial Advancement
Publisher	Data powered by TypesetSpringer International Publishing
ISSN	2194-5357
Open Access	0