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Research Paper

Automatic Alphanumeric recognition using Python and OpenCV- A new approach of ANPR

Alphanumeric recognition is a computer aspect that recognizes any digital Image automatically fed from an external source. This system initiates from obtaining digital images, localizing concerned alphanumeric areas, truncating characters and OCR from given alphanumeric characters. This system is intended to be designed and developed for helping in image processing and algorithms to identify the area from where given alphanumeric digits can be identified. These characters in a segmented way are identified with the aid of Open Source Computer Vision Library ( OpenCV) and programmed in python language. In the given source image KNN (k-Nearest Neighbor) algorithm is used to find all possible alphanumeric characters.

Published by: Syed Shafat Adil, Shiv Shankar Pal, Prashant Kumar, Mehul Anand, Sumit Gaurav

Author: Syed Shafat Adil

Paper ID: V7I4-1671

Paper Status: published

Published: August 5, 2021

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Research Paper

Analyzing tweets to classify the opinions of people living in Chennai with respect to the city

This project helps to analyze the public opinion in social media towards Chennai city by the people who live in the city. It presents a comprehensive review of local people who tweet, retweet, like on Twitter about Chennai. For completeness, it includes introductions to social media web scraping, storage, data cleaning, and some sentiment analysis tools. This project also provides a comparative study where we use three different sentiment analysis tools: Naive Bayes, Bert, and Neural Network. Analyzing social media, in particular, Twitter feeds for sentiment analysis, has become a major research activity due to the availability of web-based APIs provided by Twitter services. This project provides a review of leading software tools and how to use web scraping, cleanse using Bert and comparing the tweets with three different tools and showing them in graph and finding out which gives the best accuracy and presenting it to the CHENNAI SMART CITY LTD which comes under the GREATER CHENNAI CORPORATION. The data retrieval techniques that are presented in this paper are valid at the time of doing this project (April 2021), because they are subject to change since social media web scraping APIs are rapidly changing.

Published by: Mohammed Mafaz

Author: Mohammed Mafaz

Paper ID: V7I4-1657

Paper Status: published

Published: August 5, 2021

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Review Paper

Treatment of landfill leachate by using membrane filtration

The rapid increase in population and industrialization lead to a huge generation of solid waste throughout the country with landfilling as the most common practice for the management of solid waste. There are many methods available like physical, chemical, biological, and membrane filtration. Membrane filtration can be defined as the separation of solid immiscible particles from a liquid or a gaseous stream based primarily based on the size difference. By study it is concluded that as compared to all methods membrane filtration is very efficient, Hence, in this study membrane filtration technique is applied. In this review, the membrane is prepared with readily available materials. We have used Cellulose acetate which is a renewable, biodegradable, bio-based polymer & it is cheap material. Initial characteristics of leachate and after treatment final characters are again determined and compared. Removal efficiency is found.

Published by: Vidyashree G. Walikar, M.Y.Ganiger

Author: Vidyashree G. Walikar

Paper ID: V7I4-1652

Paper Status: published

Published: August 4, 2021

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Case Study

Identification of Jewelry Article using Transfer Learning and Image Repository

“IDENTIFICATION of jewelry ARTICLE USING TRANSFER LEARNING AND IMAGE REPOSITORY” maybe a project that aims to automate, boost and revolutionize the task of recognizing Jewellery articles for tagging before adding the article into the inventory. the aim of this project is to optimize the task of stock entry and to scale back the manual work of tagging each and each article. The project involves creating an image repository of varied Jewellery articles by manually clicking the pictures, label the pictures supported the classes, train the model, and eventually classifying the articles in real-time using live camera feed using Transfer Learning, and OpenCV. Once the article is recognized, the model will pass the worth vectors including the name of the article, the image of the article, and a number of pieces of a specific article skilled the live camera. this complete process will help in boosting up the manual process of adding Jewellery articles to inventory, will reduce manpower, be less erroneous, and can economize for the stakeholders during the end of the day.

Published by: Mohammed Mafaz

Author: Mohammed Mafaz

Paper ID: V7I4-1656

Paper Status: published

Published: August 4, 2021

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Research Paper

Detection and estimation of heavy mineral accumulation in coastal areas

To develop a conventional sensor-based system for monitoring the estimation of on-shore heavy mineral accumulation. The existing technology involves an analytical procedure using acidic solutions which is only possible under laboratory testing. Our proposed idea involves the development of an ultrasonic sensor system along with an infrared and moisture sensor module for the estimation of heavy mineral accumulation. The principle of sensor development involves the ultrasonic absorption and infrared absorption capability of specific minerals as each mineral will have a different absorption capacity. The tested samples from the laboratory are further subjected to sensor tests and the data will be evaluated. The data obtained from the sensor will be compared with laboratory test results to observe the relationship between the mineral present and the data obtained. Finally, a prototype model for real-time investigation will be developed. The main advantage of the proposed idea includes the time-efficient process accompanied by instant results which will reduce cost and procedures needed for estimation

Published by: Yaswanth Reddy Putta, Reventh M. S., Sanjay S., B. S. Sreeja

Author: Yaswanth Reddy Putta

Paper ID: V7I4-1651

Paper Status: published

Published: August 4, 2021

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Research Paper

Identifying defects during semiconductor manufacturing using Machine Learning

This Project is about identifying defects in the equipment that manufactures semiconductors by using various required parameters. Semiconductor manufacturing is a very delicate process and all the equipment that manufactures semiconductors needs to function properly, and any error in this equipment will cause major damage to the manufacturing process. In this project, we have used various machine learning algorithms and implemented the best one which has more accuracy in identifying defects during the manufacturing of semiconductors.

Published by: Nandini G., K. G. Ashwin Krishnan, Harshith R., Dileep Kumar Simhadri, Gummalla Akhil Kumar Reddy

Author: Nandini G.

Paper ID: V7I4-1638

Paper Status: published

Published: August 4, 2021

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