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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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Review Paper

Weather adaptive traffic prediction and analysis of accidents using machine learning algorithms

Predicting traffic flow is one of the fundamental needs for comfortable travel, but this task is challenging in vehicular cyber-physical systems because of ever-increasing uncertain traffic big data. Although live data with outstanding performance recently have become popular, most existing models for traffic flow prediction are fully deterministic and shed no light on data uncertainty. Also, they are many inventories in automobile industries to design and build safety measures for automobiles, but traffic accidents are unavoidable. There is a huge number of accidents prevailing in all urban areas. In this study, a novel dynamic approach is proposed for predicting citywide traffic flow based on weather. The proposed system utilizes the neuro-wavelet algorithm to select the required features for traffic prediction. The system also proposes the K Nearest Neighbor algorithm to predict the accuracy of accidents occurred in urban regions.

Published by: S. Bharath, Dr. S. Siamala Devi, M. Guruswamy, M. Aravind

Author: S. Bharath

Paper ID: V5I2-1275

Paper Status: published

Published: March 14, 2019

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Thesis

Analysis of object-oriented system quality model using soft computing techniques

Quality plays very important role in software industry because the objective of every software industry is to produce good quality software with in time and budget. Number of quality models have been proposed and used by various authors to build good quality software and these software quality models are also responsible for improving the performance, this improvement directly reflects the quality, increase users satisfaction and decrease the cost of quality. This report we have discussed and compared various quality models and soft computing techniques used for predicting the quality of software product and software system. It is found from the literature that there are lots of quality models and we measure quality based on their characteristics, so here, in this work we gives the characteristics definition and then comparison of quality models related to quality characteristics. In this study we exploring the information about soft computing technique for predicting the quality of a system or product and also develop a new quality model using soft computing (Fuzzy, Neural Network and Neuro-fuzzy) approach which is responsible to predict the software quality of an object-oriented system. In this report, we also identifies the most important factors of object-oriented system like Efficiency, Reliability, Reusability and Maintainability and also proposed a model based on these four factors that evaluating the quality of object-oriented system using soft computing technique i.e. Fuzzy Logic, Neural Network and Neuro-Fuzzy.

Published by: Anil Kumar

Author: Anil Kumar

Paper ID: V5I2-1269

Paper Status: published

Published: March 14, 2019

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