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Research Paper

Smart helmet to detect and prevent accident

The impact when a motorcyclist involves in a high-speed accident without wearing a helmet is very dangerous and can cause the fatality. Wearing a helmet can reduce shock from the impact and may save a life. There are many countries enforcing a regulation that requires the motorcycle's rider to wear a helmet when riding on their motorcycle, Malaysia is an example. A smart helmet is a special idea which makes motorcycle driving safer than before. This is implemented using GSM and GPS technology. The working of this smart helmet in IoT is very simple, vibration sensors are placed in different places of the helmet where the probability of hitting is more which are connected to microcontroller board. So when the rider crashes and the helmet hit the ground, these sensors sense and gives to the microcontroller board, then controller extract GPS data using the GPS module that is interfaced to it. When the data exceeds minimum stress limit then GSM module automatically sends the message to ambulance or family members. It also has an alcohol detector sensor which detects whether the person is drunk and switches off the engine if the sensor output is high.

Published by: L. Sindhu, G. Swedha, G. Nandhini, S. J. Raffel, M. Rashika

Author: L. Sindhu

Paper ID: V5I2-1201

Paper Status: published

Published: March 14, 2019

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Survey Report

Answer script evaluator: A literature survey

Every college, university, school conduct exams and most important part of exams are the results. In order to get these results, the exam papers have to be evaluated one by one manually. This process of evaluating the exam papers is time-consuming and requires more manpower. To overcome this solution, we have come up with a thought that removes the manual evaluation process. Our project focuses on developing a system that evaluates an answer script against a pre-uploaded marking scheme. Initially, the answers are taken in digital format and those digital answers are processed using algorithms such as word2vec where the word’s similarity index is extracted and the words similar to it are noted. Using this we can also get the meaning of the paragraph and we can match it with the answer key and get the match percentage. Using this percentage, we can calculate the marks to be awarded.

Published by: Sachin B. S., Shivaprasad K., Somesh T., Sumanth Hegde, Radhika A. D.

Author: Sachin B. S.

Paper ID: V5I2-1259

Paper Status: published

Published: March 14, 2019

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Research Paper

Survey on generative adversarial networks

GAN stands for Generative Adversarial Networks.GANs are the most interesting topics in Deep Learning. The concept of GAN is introduced by Ian Good Fellow and his colleagues at the University of Montreal. The main architecture of GAN contains two parts: one is a Generator and the other is Discriminator. The name Adversarial stands for conflict and here the conflict is present between Generator and Discriminator. And hence the name adversarial comes to this concept. In this paper, the author has investigated different ways GAN’s are used in real time applications and what are the different types of GAN’s present.GAN’s are mainly important for generating new data from existing ones. As a machine learning model cannot work properly if the size of the dataset is small GAN’s are here to help to increase the size by creating new fake things from original ones.GAN’s are also used in creating images from the given words that are text-to-image conversion.GANs are also applied in image resolution, image translation and in many other scenarios. From this survey on GAN author aim to know what are the different applications of GAN that are present and their scope. The author has also aimed at knowing the different types of GAN’s available at present. The author has also aimed at knowing the different applications of GAN and different proposed systems by various authors.

Published by: N. Yashwanth, P. Navya, Md. Rukhiya, K. S. V. Prasad, Dr. K. S. Deepthi

Author: N. Yashwanth

Paper ID: V5I2-1198

Paper Status: published

Published: March 14, 2019

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Others

A text reader for the visually impaired using Raspberry Pi

In this paper, an innovative, efficient and real-time beneficial technique that enables the user to hear the contents of text images instead of reading through them has been introduced. It uses the combination of Optical Character Recognition (OCR) i.e. Tesseract and Text to Speech Synthesizer (TTS) in Raspberry Pi. This kind of system helps visually impaired people to interact with computers and day-to-day text effectively through vocal and keyboard interface. Text-to-Speech is a device that scans and reads English alphabets and numbers that are in the image using the OCR technique and changing it to voices. We are including obstacle detection via ultrasonic sensor and color detection. This device consists of three modules, image processing module, voice processing module and an object detecting module. The device was developed based on Raspberry Pi 3 Model R, Clock Speed of 1.2 GHz.

Published by: C. Ebenezer Durai, A. Selvarani, S. Siva Suthan, M. Anwin Kushal

Author: C. Ebenezer Durai

Paper ID: V5I2-1261

Paper Status: published

Published: March 14, 2019

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Others

Passenger service system

Increased competition in the commercial transportation industry has made service quality of every transportation agencies as one of the key competitive measures to attract passengers against their rivals in-flight services, particularly food delivery and waste collection, have a notable impact on perception of the overall airline’s service quality because they are directly and indirectly provided to passengers during flight. This is the scenario in every industry. This project consists of two sections, passenger section, they can request their needs by pressing a button in the keypad and it sends to the control room section and it is displayed on an LCD control room section. The requested needs may be food, water and if it is “any other help”, the air hostess directly goes to the requested person.

Published by: Abhiram P. S., Mohammed Aslam, Sreebal Anil V. A., Minnuja Shelly

Author: Abhiram P. S.

Paper ID: V5I2-1169

Paper Status: published

Published: March 14, 2019

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Review Paper

Exploring the relative predictive efficiencies of spatial regression models

Spatial regression models are standard tools for analyzing data with spatial correlation. These models are broadly used in the social sciences for predicting the socio-economic factors. In this paper, we discuss about various spatial regression models and explain the concepts based on real data to demonstrate how to obtain and interpret relevant results. We describe prediction efficiencies of various predictors relative to the efficient minimum mean square error predictor in spatial models containing spatial lags in both the dependent variable and the error term. We consider Multiple Linear Regression Model (MLRM), Spatial Autoregressive Models (SAR), Spatial Autoregressive in the Error-term Model (SEM) and Spatial Durbin Models (SDMs) to estimate the literacy progress in the districts in Odisha as a result of changing socio-economic factors over time. The goodness of fit of the different models are compared along series of hypotheses about the performance of the specifications considering spatial relationships among the observations. The spatial analysis proved the existence of positive spatial autocorrelation and persistence of disparities in literacy attainment level across the regions during the analyzed period. The results of econometric analysis confirmed the expected positive impact of economic growth on literacy progress level as well as the necessity to incorporate the spatial dimension into the model.

Published by: Bhabani Shankar Das Mohapatra, Dr. E. G. Rajan

Author: Bhabani Shankar Das Mohapatra

Paper ID: V5I2-1258

Paper Status: published

Published: March 14, 2019

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