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Research Paper

Startup business research

Currently increasing demand for entrepreneurship leading to the best ways to start a startup. This paper provides with every aspect of research with respect to startups and its loan funding platforms along with ideologies to be considered.

Published by: Kalal Satish Goud, Vanaja Beemanapalli

Author: Kalal Satish Goud

Paper ID: V4I2-2106

Paper Status: published

Published: April 25, 2018

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Research Paper

Solar based switch mode power supply

In a switch mode power supply the input is fed from an alternating source, rectified, and filtered to obtain unregulated DC voltage which is further pass through a regulator to obtain a regulated Direct current, thus using it as a DC-DC converter. In this work, a Photo-voltaic panel is used to obtain Direct current which is fed to the hardware model, with a sole purpose of increasing dependencies on Renewable Energy sources, which is not only cost-effective but useful at places where a conventional current is not available.

Published by: Pritam Mahapatra, Biswajit Mohapatra

Author: Pritam Mahapatra

Paper ID: V4I2-2120

Paper Status: published

Published: April 25, 2018

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Review Paper

Review and study paper on wireless sensor network and few advancements

In this paper, the author aims at describing a wireless sensor network. wireless sensor network consisting of spatially distributed autonomous devices using the sensor to monitor physical or environmental conditions. The wireless sensor network can be used in wide range of applications including environmental monitoring, habitat monitoring, various military applications, smart home technologies and agriculture. Wireless sensor networks constitute one of the promising application areas of the recently developed wireless sensor networking techniques. Various clustering Schemes have been discussed and employed in both homogenous and heterogeneous wireless sensor network.

Published by: Richa, Misha Thakur

Author: Richa

Paper ID: V4I2-2127

Paper Status: published

Published: April 25, 2018

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Review Paper

Analysis and prediction of E-customers behaviour by mining clickstream data using Naive Bayes

Nowadays, online shopping has become a trend. In online shopping, it is very difficult to analyze and do prediction of the customer whether he will buy the product or not. So to predict that Naïve Bayes is used. Data mining extracts the information from a large amount of data which stores in multiple heterogeneous databases. This model extracts information and makes predictions about customers shopping behavior and helps to analyze click streams of e-customers on a digital marketplace. After collection of the dataset from the database, data mining is applied to the dataset collected and online customer behavior is predicted. Naïve Bayes is applied to the dataset which will predict the customer behavior and also predict about the customer’s interest about the item.

Published by: Namrata Pawar, Monali Gaikwad, Sarika Kalyani, Margi Savla

Author: Namrata Pawar

Paper ID: V4I2-2130

Paper Status: published

Published: April 25, 2018

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Review Paper

A hybrid model for CBIR classification using texture feature selection

Content-based image retrieval has been an active analysis space in past years. Many alternative solutions are planned to boost the performance of retrieval, however, the massive a part of these works have targeted on sub-parts of the retrieval drawback, providing targeted solutions just for individual aspects (i.e., feature extraction, similarity measures, indexing, etc.). The implementation of the CBIR model using the Tamura texture features will be implemented along with classification method features in this project. This model will produce the efficient content-based image retrieval (CBIR) based on robust Tamura texture feature descriptors for the high performance. This model will enable the CBIR query search based upon encrypted feature descriptors using the early termination based method. The CBIR model in the project would be improved by using the multivariate feature descriptors in the perfect amalgamation to enhance the performance of the implemented model.

Published by: Manpreet Singh, Sonika Jindal

Author: Manpreet Singh

Paper ID: V4I2-2136

Paper Status: published

Published: April 25, 2018

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Research Paper

Analysis of student academics performance using Hadoop

In recent years the amount of data generated in educational sector is growing rapidly. In order to gain deeper insights from the available data and extract useful knowledge to support decision making and improve the education service efficient storage management and fast processing analytics is needed. Academic data of a student helps institute to measure their progress. Students facing severe academic challenges are often recognized too late. Analytics play a critical role in performing a thorough analysis of student and learning data to make an informed decision. Big Data solution enables to analyze the wider variety of data sources and data types which improves the accuracy of predictions. Hadoop platforms provide highly scalable platforms and can store a much greater volume of data at lower cost. The purpose of the proposed Project is to help in identifying “at risk” students who are not progressing towards graduation early in order to get them back on track. The cause of lack of adequate progression can be identified and addressed. The system proposed will be helpful for educational decision-makers to reduce the failure rate among students. The implementation is done in Hadoop framework. The PAMAE algorithm is implemented for analyzing student’s academic data.

Published by: Diptimayee Baliarsingh, Samiksha Hemant Parab, Vijay N. Patil

Author: Diptimayee Baliarsingh

Paper ID: V4I2-2142

Paper Status: published

Published: April 25, 2018

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