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Research Paper

A machine learning approach for handwritten recognition

Handwritten recognition is the ability of the system to receive and interpret the input taken from various sources like paper documents, touch screen devices, photographs, etc. In this paper, we will be designing a handwritten recognition expert system using different classification algorithms to recognize the handwritten characters like Support Vector Machine(SVM), K-Nearest Neighbour(KNN), Random Forest Classifier and Neural Networks. This application is useful for recognizing all characters and digits given as in the input image. The main objective of the project is to increase the accuracy of the recognition characters through various algorithms and choose the best algorithm for recognizing characters or digits.

Published by: Vanamoju Vandana, Yedlapalli Divya Manasa, Gedela Gayathri, Komakula Sai Lakshmi Priyanka, M. Sion Kumari

Author: Vanamoju Vandana

Paper ID: V6I3-1320

Paper Status: published

Published: May 26, 2020

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Others

Financial Inclusion in India – Need of the hour

The article studies the existing systems of financial inclusion in India, the problem at hand and few qualitative ways those can be handled. The importance of Financial Inclusion in the current situation, when we are already riddled with a pandemic. It can improve the current economy in India while putting a liquidity back in the economy.

Published by: Sayantani Panja

Author: Sayantani Panja

Paper ID: V6I3-1314

Paper Status: published

Published: May 26, 2020

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Research Paper

Handwritten digit classification using Convolutional Neural Networks

This research study throws light on one of the most common use-cases of Handwritten Digit recognition which can be seen being implemented by using a particular Deep Learning technique for pattern recognition known as Convolutional Neural Networks which works similarly to the functionality of neurons in a human brain. We have trained and tested the MNIST dataset using this technique and implemented a classifier to predict the pattern of the digits.

Published by: Shivam Srivastava, Richa Sharma, Pratyaksha Jindal, Sikandar Singh Sandhu, Pratyush, Atul Kumar

Author: Shivam Srivastava

Paper ID: V6I3-1301

Paper Status: published

Published: May 26, 2020

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Review Paper

Implementation of modified blowfish algorithm

This project focuses on implementing a modified blowfish algorithm using a hardware description language such as VHDL. Encryption algorithm plays a major role in network application and data security systems. But securing data consumes a major amount of CPU time and battery power. We also focus on improvising the performance and security provided by the blowfish encryption algorithm. With the advancement in technology, DES is found to be no longer secure. As a drop-in replacement for DES, the blowfish encryption algorithm can be used. The original blowfish algorithm function has been modified using a modified S-box and adding Key Bits Shifting (KBS) to the Function Block.

Published by: Bindu R., Dr. Deepak S. Sakkari

Author: Bindu R.

Paper ID: V6I3-1284

Paper Status: published

Published: May 26, 2020

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Review Paper

Towards many to many communications among blind, deaf and dumb users

Humans are social creatures. We learn by connecting with those and around us, through communication. While people with hearing or visual impairments alone can find a way to share their thoughts with others and understand them, deaf blind people face a much more difficult communication task. Thus project presents and implements the design, prototype and testing of a portable software and speaker device with a display for the communication between two people or also between visually impaired people

Published by: Umme Athiya, Chaithra A. S., Aishwarya R., Aswathi Rajesh

Author: Umme Athiya

Paper ID: V6I3-1305

Paper Status: published

Published: May 23, 2020

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Research Paper

Audio super resolution using Neural Networks

In this era of technological advancement, people tend to demand high-quality videos, audios, and images. So deep convolutional neural networks play an important role in learning low-resolution data and obtaining high-resolution data by performing interpolation method. This is similar to the image super-resolution. Here we introduce a signal processing technique to convert the low resolution into high-resolution data with the help of signal processing methods such as up-sampling and down-sampling using subpixel convolution through Bottleneck architecture. Our model tests the missing values within the low-resolution signals and forms high-resolution signals. This technique is applied to telephony, upscaling, text to speech conversion, and also for investigations in many departments. We test the effect of convolutions used in the signal processing and measures the compatibility and scalability for the generative model of audio.

Published by: Gorli Harshini, Gayatri Yasaswini Pappala, Gorle Manasa, Gollamandala Sam Shalini, M. Sion Kumari

Author: Gorli Harshini

Paper ID: V6I3-1299

Paper Status: published

Published: May 23, 2020

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