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Improved tweet Sentiment Classification Using Convolution Neural Network and Random Forest

With over 319 million monthly active users, Twitter has developed into a goldmine for organizations and people with a strong political, social, or economic incentive to retain or enhance their clout and reputation. Sentiment analysis enables these firms to conduct real-time surveys on numerous social media platforms. Twitter sentiment analysis technology enables the measurement of public attitudes toward certain events or products. The majority of current research is devoted to extracting sentiment traits through the analysis of lexical and syntactic variables. These characteristics are openly stated using emotional words, emoticons, and exclamation points, among others. In this research, effective feature extraction is accomplished via the use of convolution mapping and an attention layer. These features are then learned by random forest.

Published by: Pallavi Sharma, Dr. Harpreet K. Bajaj

Author: Pallavi Sharma

Paper ID: V7I3-2142

Paper Status: published

Published: June 29, 2021

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Review Paper

A study on Web conferencing system and the deployment

In the current scenario due to the spread of COVID-19, there has been an increase in the use of web conferencing systems to communicate among people. Web conferencing systems have enabled organizations, universities, and individuals to communicate over the internet from their homes during the current pandemic. Web conferencing system not only allows people to communicate over the internet, but it also has other features which enable users spread across different regions to collaborate like a whiteboard, polls, chat, and others. As the number of users is increasing for such systems, scaling and load balancing becomes vital to handle all the users. This paper presents an exploration of the web conferencing systems and explores a case study of an open-source web conferencing system and scaling of the respective system to meet the demands of the system.

Published by: Sudarshan M., Harith L. K., K. Vadhi Raja, Pranava B., Dr. G. S. Mamatha

Author: Sudarshan M.

Paper ID: V7I3-2087

Paper Status: published

Published: June 29, 2021

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Research Paper

Reusable AI-based ensemble model for detecting SQL injection in service-oriented architectures

Cybersecurity has become one of the most sought-after domains in the field of computer science. Protection of computing resources and information against disruptive cyber threats has garnered utmost attention in recent times, owing to the conventional methods used in the field that often fall short of detecting or preventing the ever-evolving collection of malware. With the advent of new technologies such as Machine learning and Artificial intelligence, it is possible to streamline the approaches in the field of Cybersecurity. These technologies can be used to detect and prevent malicious content, thereby developing successful security solutions. The right AI tech could help us process huge volumes of threat data, discover anomalies and effectively eliminate potential threats. Currently, the most common approach involves using regular expressions to sequentially compare the incoming request or its vector with a predefined set of signatures. Though this approach is widely prevalent, it falls short in terms of accuracy. This is due to the fact that the signatures are not updated often, and several logical problems or loops come up when regular expressions are used within thousands of individual rules. In this project, we aim to identify various injections among neutral input vectors using ML models and will be predicting whether the vectors are injections or not. An ensemble of a number of ML models is used to build a voting mechanism to have an accurate prediction. For the sake of demonstration, the application consists of a frontend built using react and a python flask backend server

Published by: Sudarshan M., Pranava B., Dr. G. S. Mamatha

Author: Sudarshan M.

Paper ID: V7I3-2088

Paper Status: published

Published: June 28, 2021

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Review Paper

Number Script Recognition using Neural Networks

The ability for accurate digit recognizer modelling and prediction is critical for pattern recognition and security. A variety of classification machine learning algorithms are known to be effective for digit recognition. The purpose of this experiment is rapid assessment of multiple types of classification models on digit recognition problem. The work offers an environment for comparing four types of classification models in a unified experiment: Multi-class decision forest, Multi-class decision jungle, Multi-class Neural Network and Multi-class Logistic Regression. The work presents assessment results using 6 performance metrics: Overall accuracy, Average accuracy, Micro-averaged precision, Macro-averaged precision, Micro-averaged recall and Macro-averaged recall. The experimental results showed that the highest accuracy was obtained by a Multi-class Neural Network with a value of 97.14%. The purpose of this project was to introduce neural networks through a relatively easy-to-understand application to the general public. This paper describes several techniques used for preprocessing the handwritten digits, as well as a number of ways in which neural networks were used for the recognition task.

Published by: Y. Bhanu Prasad, A. Sai Kumar, Pruthvy Charan, Dr. G. Prasad Acharya

Author: Y. Bhanu Prasad

Paper ID: V7I3-2171

Paper Status: published

Published: June 28, 2021

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Research Paper

AI Based license Plate recognition using CNN

The discovery based on the artificial intelligence of the Indian license plate is our theme. We have created a program that is able to take a photo from the surrounding area. At the end of the hardware, we need a pc (or raspberry pi) and a camera and at the end of the software, we need a library to download and process data (image). We have used OpenCV (4.1.0) and Python (3.6.7) for this project To get something (license plate) in the picture we need another tool that can see the Indian license plate to use Haar cascade, pre-trained on Indian license plates (to be updated soon) be YOLO v3). Our main objective is to establish a system that gives us the license plate number of a vehicle when given a low definition image captured by a surveillance camera at toll collection centres. Mostly our system is demanded for the purpose of traffic monitoring. Hence we designed a system that lessens the manual work of entering the license plate numbers. And also we built a system that increases the speed of processing toll collections or traffic violation punishments. Our model is built on convolutional neural networks where several mathematical computations are done within the six hidden layers and give the output characters using contour detection and character segmentation.

Published by: K V Yaswanth, Anvesh Donthi, A Venkata Ramana, Dr. M. Poornachandra Rao

Author: K V Yaswanth

Paper ID: V7I3-2167

Paper Status: published

Published: June 28, 2021

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Research Paper

Age and Gender Detection using OpenCV

In this fast emerging world Artificial Intelligence plays a very vital role in every field of science . Everything is being automated from operating a remote to driving a car using Artificial Intelligence. We show a glimpse of such automated experience with this project. In this project we show how easy it is to detect faces and identify gender along with gender with the help of CNN(Convolutional Neural Networks) and OpenCV. Using these fields of Artificial Intelligence we can reduce the use of hardware components and complexities in this project. Along with CNN and OpenCV we use Adience dataset so that the output is achieved with accurate values in training and validation. For the output to be determined even with multiple parameters we use pre-trained model that is caffee model along with OpenCV. The proposed model can be used in surveillance purposes or in medical purposes.

Published by: Mahija Kante, Dr. Esther Sunandha Bandaru, Gadili Manasa, Meghana Emandi, Vanarasi Leela Lavanya

Author: Mahija Kante

Paper ID: V7I3-2163

Paper Status: published

Published: June 28, 2021

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