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Recent Papers

A Study on Customer Attitude Towards Online Shopping in India and its Impact: With Special Reference to Solapur City

The growing no. of internet user in India provides a bright prospect for online shopping. If E-marketers know the key factors affecting the behavior of customers and its relationship, then they can formulate their marketing strategies to convert potential customers into loyal ones and retaining existing online customers. This researcher paper highlights on factors which online Indian customers keep in mind while shopping. After completion of study Researchers found that cognition, sensed usefulness, the comfort of use; sensed enjoyment and security are the five components which affect consumer perceptions about online purchasing. The Internet has changed the way consumers purchase goods and services at the same time many companies have started using the Internet with the objective of cutting marketing costs, thereby reducing the price of their product and service in order to stay ahead in highly competitive markets. Companies also use the Internet to convey, communicate and disseminate information to sell the product, to take feedback and also to conduct satisfaction surveys with customers. Customers use the Internet not only to purchase the product online but also to compare prices, product features and after sale service facilities they will receive if the purchase the product from a particular store. Many experts are optimistic about the prospect of online business.

Published by: Prof. Pritam P Kothari, Prof. Shivganga S. Maindargi

Author: Prof. Pritam P Kothari

Paper ID: V2I6-1180

Paper Status: published

Published: December 3, 2016

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k-Means Clustering based Lexicon Analytical Model for Multi-Source News Classification

The supervised models have been found more efficient for the purpose of news classification. The major goal of the news classification research is to improve the accuracy while decreasing the elapsed time. It is always difficult for the people to read all of the news on their favourites portal which have listed over the given portal. In this research, an approach is KNN lexicon technique which is used to obtain the popular news list from thousands or hundreds of online news available through APIs. This approach uses extraction summarization for summarizing the keywords thereby selecting the original sentences and putting it together into a new shorter text explaining the overall overview of the news data. Then the lexicon analysis would be performed over the given text data and then final classification of the news is done using k-nearest neighbor. The results would be obtained in the form of the parameters of accuracy, elapsed time, etc.

Published by: Kamaldeep kaur, Maninder Kaur

Author: Kamaldeep kaur

Paper ID: V2I6-1179

Paper Status: published

Published: December 2, 2016

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Hall Current Effects on Unsteady MHD Convective Flow of Heat Generating/Absorbing Fluid through Porous Medium in a Rotating Parallel Plate Channel

In this paper, we discussed the heat and mass transfer on the unsteady hydromagnetic convective flow of an incompressible viscous electrically conducting heat generating/absorbing fluid through porous medium in a rotating parallel plate channel under the influence of uniform transfer magnetic field normal to the channel taking Hall current effects into account. The momentum equation for the flow is governed by the Brinkman’s model. The analytical solutions for the velocity, temperature and concentration distributions are obtained by making use of regular perturbation technique. The variations of said quantities with different flow parameters are computed by using Mathematica Software and discussed with the help of plots. The skin friction, Nusselt number, and Sherwood number are also evaluated analytically and computationally discussed with reference to pertinent parameters in detail.

Published by: Dr. P. Sulochana

Author: Dr. P. Sulochana

Paper ID: V2I6-1178

Paper Status: published

Published: December 2, 2016

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Growth in Agricultural Resource USE: An Application of Exponential Growth Curve.

Agriculture is the means of livelihood for around two thirds of the work force of India. Agriculture is the production, processing, marketing and use of foods, fibers and bye prducts from plant crops and animals. It was the key development that led to the rise of human civilization with the husbandry of domesticated animals plants creating food surpluses that enabled the development of more densely populated and stratified societies. At the time of independence, the revenue from the agriculture sector was quite low compared to what it is today. The main reason for the increase in the revenue is the in crease in agricultural production that was brought about by the Green revolution, over the years, agriculture has emerged as one of the top priorities of the Central & state governments. In 2000, the government announced the first over “National Agriculture Policy”. The resources taken were consumption of fertilizers, consumption of electricity in agricultural sector, short term and long term credit, number of tractors, area under high yielding varieties, net irrigated and gross irrigated area and total cropped area. It was computed using the exponential trend equation i.e.: Y = abt Log Y = log a + t log b taking ^ b = (1+r) r = (b-1) x 100 Where Y = study variable; area, production, yield or Resource variables a = constant ^ b = regression coefficient t = time, t = 1 ……………n r = compound growth rate in percent To test the significance of the compound growth rates, t-test applied was t* = r/S.E( r) Where t* = calculated t-ratio, distributed with (n-2) degrees of freedom r = compound growth rate S.E. (r) = standard error of the compound growth rate, S.E. was calculated by fitting the following formula. 100 x b S.E. (r) = Σ (log y)2 (Σ log y)2 (log b )2 Σ ( t – t )2 Log 10e n (n-2) Σ (t – t )2 where the limit for Σ is, i = 1,2……n

Published by: Prince Singh, Dr Manjeet Jakha

Author: Prince Singh

Paper ID: V2I6-1177

Paper Status: published

Published: December 2, 2016

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Data Mining : Text Classification System for Classifying Abstracts of Research Papers

Text classification is the process of classifying documents into predefined categories based on their content.Text classification is the primary requirement of text retrieval systems,which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data.We have proposed a Text Classification system for classifying abstract of different research papers. In this System we have extracted keywords using Porter Stemmer and Tokenizer. The word set is formed from the derived keywords using Association Rule and Apriori algorithm. The Probability of the word set is calculated using naive bayes classifier and then the new abstract inserted by the user is classified as belonging to one of the various classes. The accuracy of the system is found satisfactory. It requires less training data as compared to other classification system.

Published by: Shirdi Wazeed Baba, Reddi Sanjeev Kumar

Author: Shirdi Wazeed Baba

Paper ID: V2I6-1175

Paper Status: published

Published: December 1, 2016

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Research Paper

Multivariate Indoor Scene Recognition using the Object Level Analysis with SVM Classification

The research area of the indoor scene recognition has attracted the various scientists and engineers across the globe, which includes the neuroscientists, electronics engineers, robotic engineers, digital image experts, camera developers and manufacturers for the purpose of application designing in the fields of the computer vision, vision based communications and the access control systems. The indoor scene recognition methods require the inclusion of the various methods in the computer vision, image processing and feature recognition for the scene recognition by identifying the category of the input image by comparing it against the given training databases by the means of the feature descriptor (popularly based upon the color or low level features) and the classification algorithm. The indoor scene classification algorithms require the number of the computations and feature transformations along with the normalization and automatic categorization. In this thesis, the multi-category dataset has been incorporated with the robust feature descriptor using the scale invariant feature transform (SIFT) along with the multi-category enabled support vector machine (mSVM).

Published by: Neetu Dhingra

Author: Neetu Dhingra

Paper ID: V2I6-1174

Paper Status: published

Published: November 30, 2016

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