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

Most of the service providers and product based companies while launching brand new products, services or releasing new versions of existent products need to campaign to reach at the potential customers. While doing so they target their already existing customers who are the ambassadors of their company. To address the existing customers, they maintain the detailed customer data at all levels as customer maser data [9]. In this paper, we have built a prediction model to identify the customers who would most likely respond to the prospective offerings of the company basing on their past purchasing trends. Experiments have been conducted using the well known classifiers, viz., Naïve Bayes, KNN and SVM to classify a bank customer data. Subsequently, we have compared the effectiveness of these techniques and found out which one produces the maximum accuracy for the existing data set. © 2015 IEEE.

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

A customer classification prediction model based on machine learning techniques

Rough set was conceptualized to deal with indiscernibility or imperfect knowledge about elements in numerous real life scenarios. But it was noticed later that an information system may establish relation with more than one universe. So, rough set on one universal set was further extended to rough set on two universal sets. This paper presents eleven possible types of classifications on the whole and it is proved that out of those eleven types only five types which were hypothesized by are elementary and the rest six types can be reduced to the elementary five types either directly or transitively. This paper also analyzes to predict the all possible combinations of types of elements for a classification of 2 and 3 numbers of elements. It is established that, the number of classification with 2 elements is 3 whereas with 3 elements is 8 instead of 64.

Department of Computational Intelligence

School of Computer Science and Engineering

World University root node

Information Resources Management Journal

General Characterization of Classifications in Rough Set on Two Universal Sets

Background/Objectives: Voice Identification System refers to a system which comprises of hardware, software and it is used to identify voice for several applications. The aim of the research is to develop a small scale system that incorporate both speaker recognition and speech recognition and can show specific visual information to a user. Methods: To this end, we have developed a system based on the technique of Hidden Markov Model. The Hidden Markov Model is a stochastic approach which models the algorithm as a double stochastic process in which the observed data is thought to be the result of having passed a hidden process through second process. Both processes are characterized only through one that is observed. A database of voice information is created. To extract features from voice signals, Mel-Frequency Cepstral Coefficients (MFCC) technique has been applied producing a set of feature vectors. Subsequently, the system uses The Vector Quantization (VQ) for features training and classification. Findings: The designed system has been tested with multiple speakers as reference. Speech recognition based on Hidden Markov Model is achieved successfully for the conversion of speech to text. In this proposed research, speech recognition is achieved with accuracy about 90%. Applications: The system has potential to be used in music industry, crime investigation, personal assistant and in hi-tech devices.

Indian Journal of Science and Technology

A Voice Identification System using Hidden Markov Model

In this paper we have explained the detailed work done in developing a framework which can be used for the purpose of discovering business intelligence with the help of decision rules induced from the customer reviews of a product or a service posted online. We have explained the analysis for the product Samsung Galaxy S5. Our proposed framework has been designed by collecting the reviews from Samsung home page, preprocessing it and inducing rules by using rough set based LEM2 algorithm. The induced rules would be helpful for Business analyst in understanding the product dimensions, attributes and inherent association among them. © 2014 IEEE.

2014 International Conference on Contemporary Computing and Informatics (IC3I)

Business intelligence from online product review-a rough set based rule induction approach

In this paper we have explained the detailed work done in developing a system which can be used for the purpose of opinion analysis of a product or a service. The system readily processes the tweets by pulling data from tweeter posts, preprocessing it and connecting to Alchemy API by REST call method. and showing the result graphically. We have given the analysis for the product Samsung Galaxy Our proposed system access the public tweets by API and filters them for Samsung Galaxy. The analysis is being carried out as to classify the sentiment as positive, negative or neutral. © 2014 IEEE.

2014 International Conference on Computer Communication and Informatics

Opinion mining about a product by analyzing public tweets in Twitter

In modern era of computing, there is a need of development in data analysis and decision making. Most of our tools are crisp, deterministic and precise in character. But general real life situations contains uncertainties. To handle such uncertainties many theories are developed such as fuzzy set, rough set, rough set on fuzzy approximation spaces etc. But all these theories have their own limitations. To overcome the limitations, the concept of soft set is introduced. But, soft set also fails if the attributes in the information system are almost identical rather exactly identical. In this paper, we propose a decision making model that consists of two processes such as preprocess and postprocess to mine decisions. In preprocess we use rough set on fuzzy approximation spaces to get the almost equivalence classes whereas in postprocess we use soft set techniques to obtain decisions. The proposed model is tested over an institutional dataset and the results show practical viability of the proposed research. Copyright © 2014 Inderscience Enterprises Ltd.

International Journal of Intelligent Systems Technologies and Applications

A decision making model using soft set and rough set on fuzzy approximation spaces

Nowadays, most of information saved in companies are unstructured models. Retrieval and extraction of the information is essential works and importance in semantic web areas. Many of these requirements will be depend on the unstructured data analysis. More than 80% of all potentially useful business information is unstructured data, in kind of sensor readings, console logs and so on. The large number and complexity of unstructured data opens up many new possibilities for the analyst. Text mining and natural language processing are two techniques with their methods for knowledge discovery from textual context in documents. This is an approach to organize a complex unstructured data and to retrieve necessary information. The paper is to find an efficient way of storing unstructured data and appropriate approach of fetching data. Unstructured data targeted in this work to organize, is the public tweets of Twitter. Building an Big Data application that gets stream of public tweets from twitter which is latter stored in the HBase using Hadoop cluster and followed by data analysis for data retrieved from HBase by REST calls is the pragmatic approach of this project.