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Automated air pollution monitoring and forecasting system using Machine Learning

Various Air Quality Monitoring systems have been recently developed and installed in some of the cities such as Mumbai, Bangalore, Delhi, Hyderabad, etc. But the actual results do not satisfy enough. Several issues in the existing system were the number of gases detected were very few. There was a need for such a system that monitors more amounts of gases in less amount of time. Time was also an issue regarding the monitoring of the gases. Some gases consumed an enormous amount of time, so it became crucial to decrease them and make a more efficient system. The proposed system is automated air pollution monitoring and forecasting system using machine learning which uses an Arduino Mega, MQ gas sensors module for its development along with some other basic components like16x2LCD, Buzzer, Potentiometer. MQ135 gas sensor detects gases like NH3, NOx, alcohol, benzene, smoke, CO2, SO2, etc. which are the main reason for degrading air quality. Wi-Fi module is used to connect our system to the Cloud so that all the readings and the data can be transferred to the desired server for forecasting purposes. A trained model is expected to use the ARIMA algorithm for the best prediction purposes. This model is then applied to the upcoming data and forecasting is done. The various existing system was learned which are developed till date, it was found that the air quality numerical model such as WRF-Chem, community Multi-scale air quality model (CMAQ), CAMx, NAQPMS was used. The drawbacks in these models were found like, Source list was not updated in time for WRF-Chem, Detailed information about the source of the pollutants, and other variables are generally not known. After knowing all the facts from the study, this system is expected to use a Scikit machine learning tool for forecasting. The proposed model monitors the upcoming data in no time and if the value goes beyond the threshold value it activates the buzzer and the server is notified.

Published by: Vinayak Pragada, Romil Keniya, Abhijeet Chauhan, Beena Ballal

Author: Vinayak Pragada

Paper ID: V7I4-1199

Paper Status: published

Published: July 8, 2021

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Research Paper

Genotypic, phenotypic, and heritability studies in groundnut (Arachis hypogaea)

A total of 36 different genotypes maintained under Goldking Research Centre were evaluated in Kharif, 2019 for some variability parameters like GCV, PCV, Heritability, and Genetic advance as percent of the mean. The statistical analysis shows that the estimates of GCV and PCV along with heritability and genetic advance were found to be high for pod yield per plant, plant height number of aerial pegs per plant, biological yield per plant 100 pod weight, and harvest index which indicate that these quantitative characters are preponderantly governed by additive genes so there is a good scope of selection for further improvement in these traits and ultimately pod yield.

Published by: Mempal Dhanyan, S. I. Kapadia, K. S. Patel, P. K. Patel, Sunil Sapavadiya, K.N. Patel

Author: Mempal Dhanyan

Paper ID: V7I4-1242

Paper Status: published

Published: July 8, 2021

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Research Paper

Menu-based Penetration Testing and Vulnerability Assessment

The systems and networks run by an individual or an organization are prone to get exploited by hackers for personal gains or any other motive. The flaws or loopholes in the operating systems or operating procedures and practices may lead to a stage where once they are compromised; there is no going back beyond that point. This is a serious issue that must be rectified and safety measures must be inculcated in the operations. The systems and networks in use must be thoroughly checked for vulnerabilities to ensure protection. This is a required objective in every system and every organization. The project that we are working on is a menu-based vulnerability assessment and penetration testing tool that checks the selected system or network and tries to find the most common and most complex risks associated with the system or network. The tool is called EDITH, which stands for “Executable and Distinguished Interface to Hack”. This tool simplifies the process by allowing the authorized user to choose from the options provided. The simplicity of the tool is what makes it idiosyncratic in the field of penetration testing and risk analysis. The tool is built using advanced python modules that support kernel and terminal levels of access and operation rights. The tool provides a wide range of options to check vulnerabilities and perform penetration testing on the selected system or networks. The options are categorized into network-related tests and system-related tests. The tool is capable of performing penetration tests from a low-security system to a highly secured firewall built system. The tests performed by the tool are completely ethical and do not void any laws unless they are used for illegal purposes. This is a brief description of our project.

Published by: A. N. Anshuman, Abhinandan C., Suraj Upadhya G., Thanushree C., Kishore S. Verma

Author: A. N. Anshuman

Paper ID: V7I4-1227

Paper Status: published

Published: July 8, 2021

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Review Paper

Live Care: Virtual healthcare assistant

Within few days COVID-19 transformed our lives and made us go all virtual. Most of the essential services went virtual. However, there were some services that took time, one of them was Healthcare. To prevent the transmission of coronavirus Telemedicine emerged as a very efficient platform in connecting the doctors and patients. The Project aims at supporting the patients as well as the clinicians to bridge the gap and enjoy the service. Further the project will also have steps and tips to use the platform. Telemedicine is not a new concept; it was present before COVID-19 as well. It was more popular in the west and not much followed here.

Published by: Mansi Narwade, Vishakha Arote, Divyansh Swabhawane, Irfan Shaikh, Dr. Chaya Jadhav

Author: Mansi Narwade

Paper ID: V7I4-1228

Paper Status: published

Published: July 8, 2021

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Review Paper

Design of MMIC phase shifter in Ku Band for Antenna Beam steering applications

The paper presents a 4-bit MMIC phase shifter at 14 GHz using eyesight available PDK in the ADS tool. The designed phase shifter has an insertion loss of 4 dB and Return loss of 18 dB with phase angles 22.5, 45, 67.5, 90-degree phase angles.

Published by: Namrata Marigoudar, Dr. K. Sreelakshmi

Author: Namrata Marigoudar

Paper ID: V7I4-1216

Paper Status: published

Published: July 8, 2021

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Research Paper

Crop yield prediction and alternate crop prediction using ML

Agriculture is one of the most important occupation practiced in India. It is the broadest economic sector and plays an important role in overall development of the country. About 60% of the land in India is used for agriculture in order to suffice the needs of 1.2 billion people. Thus, modernization of agriculture is very important and thus will lead the farmers our culture towards profit. The proposed method aims to provide an efficient and accurate solution for prediction of yield rate of crop and also providing alternate crop based on given conditions. So, using machine learning algorithms and techniques such as Linear Regression, Random Forest Regressor to identify the pattern among data and process it as per the input condition, the method predicts yield rate of crop and alternate crop. The proposed method takes into consideration factors like location and weather conditions to process the data.

Published by: Disha Chiplonker, Arjun A., Pruthvi M., Rakshith V. R., Sunil G. L.

Author: Disha Chiplonker

Paper ID: V7I4-1245

Paper Status: published

Published: July 8, 2021

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