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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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Research Paper

Partial replacement of conventional concrete by ECC

Concrete is good in compression but if any type of strain applied to it, it starts to fail. Where the steel is good tension. It can bear the deflection up to its elastic limits. Cementitious Composites abbreviated as ECC. This material is capable to exhibit considerably enhanced flexibility. An ECC has a strain capacity of more than 3 percent and thus acts more like a ductile metal rather than like a brittle glass. This project is based on the behavior of engineered cementitious composited (ECC) made by using cement OPC 53, Polypropylene fibers, silica sand, superplasticizer, and water. As for research, Polypropylene fibers are used with cementitious up to 2% to evaluate the optimum amount of fiber on which we can find the maximum compressive, tensile and flexural strength. For this research work, M20 grade concrete is using and tests will conduct for proportions of ECC concrete replacement with normal concrete of 0, 15 %, 20% and 25% (with total volume).ECC concrete can be double the cost as compared to conventional concrete but as it can amplify the duration of structure, it will be less costly than the conventional concrete.

Published by: Priya. K, Radhika. M, Aishwariya. M, R.Yuvaraj

Author: Priya. K

Paper ID: V4I2-2132

Paper Status: published

Published: April 24, 2018

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Review Paper

Sharing infrastructure resources securely in social networks

To run an application on a social network we need infrastructure which is provided by cloud service providers. Due to sharing of resources user can get access without any software installed. Because of sharing user can interact with each other. To avail this we need to allocate this resource for the betterment of user. Due to allocation, it defines how resources are allocated to social networks and cloud computing which helps to effectively utilized in the presence of user sharing.

Published by: C. M. Jadhav, S. J. Chougule

Author: C. M. Jadhav

Paper ID: V4I2-2094

Paper Status: published

Published: April 24, 2018

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Research Paper

Prosthetic hand movement using artificial neural network

Artificial neural networks (ANNs) were used to classify EMG signals from an arm. Using an amplifier card from the Smart Hand project or prosthetic hand, 16-channel EMG signals were collected from the patients arm and Filtered. After time-domain feature extraction, simple back-propagation training was used to train the networks. During the training, the patient moved his Fingers according to a predefined pattern. After the training, the patient could move an artificial hand by duplicating the movements made during training. Artificial hands are nothing new. One of the earliest mentions is of a Roman general that fought with an iron arm back around the year 50 AD and many of researchers have done on this project. Hopefully, this work will show that this approach to the problem of controlling hand prosthesis is viable and that it has benefits over other methods previously used.

Published by: Akansh Sharma, Pradeep Tripathi

Author: Akansh Sharma

Paper ID: V4I2-2003

Paper Status: published

Published: April 24, 2018

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