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Research Paper

Performance and emission characteristics of a VCR SI engine fueled with ethanol/gasoline blends

Alternative fuels have become a centre of attraction these days due to its capability of reducing the dependency on fossil fuels and harm to the environment. Alcohol such as ethanol is considered as a clean and alternative fuel for SI engines when it is used in blends with gasoline in different fractions to increase oxygen content. In this experimental investigation, ethanol obtained from sugarcane waste was used in fuels prepared by blending it in increasing ratios (5, 10, 15 and 20 vol.%) with oxygen free gasoline. These ethanol/gasoline blended fuels were used to assess the combustion and emission characteristics of a VCR SI engine. Constant speed of 1300 rpm was maintained during the whole experimentation and the compression ratios were varied as 6, 7 and 9 respectively. Results showed that the ethanol - gasoline blends with 20 vol.% presented the highest volumetric efficiency, torque and brake power, whereas, the ethanol - gasoline blends of 5 vol.% presented the lowest volumetric efficiency, torque and brake power among other blends. Gasoline on the other hand showed the lowest volumetric efficiency, torque and brake power among all the test fuels. Also, the CO and UHC emissions were significantly reduced with the increase of ethanol content in the fuel blends, which indicates an efficient combustion. The impacts of compression ratios on the engine were also observed. The lowest values of CO and UHC emissions were observed at the compression ratio of 6 and with E20 blends. Ethanol – gasoline blends comprising 20 vol.% ethanol and 80 vol.% gasoline provided a better performance among all the blends used for the experimentation.

Published by: Ridon Bagra, Dr. P. Tamilselvam, G. Tharanitharan, Rolin Sorum

Author: Ridon Bagra

Paper ID: V6I3-1606

Paper Status: published

Published: June 26, 2020

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Research Paper

End to end CI/CD pipeline for Machine Learning

In every industry machine learning applications are becoming popular, however, compared to traditional software applications the process fo developing, deploying, and continuously improving for machine learning applications is more complex. In industry practice continuous integration, delivery, and deployment enable organizations to release new features in their products frequently. For engineering processes of developing and designing secure pipelines to support continuous practices, how machine learning systems should be architected to gain a deep understanding in the process, and how to capture, improve and report data into different aspects of continuous integration, delivery, and deployment. Without proper pipeline for machine learning it is hard to predict, test, explain, and improve data workflow behavior. Pipelining in machine learning bringing different principles and practices to machine learning applications to work in a proper manner. In the industrial sector consequences of an irregular pipeline can cause financial, resource, and time will get wasted and some times it can indirectly influence companies' personal reputation in the market. This paper discusses the problems experience while building a machine learning pipeline and ultimately describe the framework to implement the problems in the workflow. Methodically reviewing the state of the art of continuous execution to organize approaches and tools, recognize challenges and practices. As a result, the machine learning pipeline reduces the gaps and increases the speed of experimentation in the workflow.

Published by: Ram Mohan Vadavalasa

Author: Ram Mohan Vadavalasa

Paper ID: V6I3-1581

Paper Status: published

Published: June 26, 2020

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Technical Notes

Machine Cloning

Middleware systems which integrate with front end and back-end systems in any enterprise solutions play an important role in a monolithic setup of an enterprise. Most of the middleware systems are file or XML based and not RPM packages as Linux or other Operating Systems define. These file and XML based softwares gives us opportunity to Clone a system and build a new one which saves time and money. Many existing tools and solutions in the market right now talk about Operating System cloning but rarely talk about software cloning. We are going to address few such methods and procedures to achieve this.

Published by: Mallikarjuna Akkinapalli

Author: Mallikarjuna Akkinapalli

Paper ID: V6I3-1568

Paper Status: published

Published: June 26, 2020

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Research Paper

Alternate Medicine Recommendation

On the Internet, where the number of choices is overwhelming, there is a need to filter, prioritize and efficiently deliver relevant information to alleviate the problem of information overload, which has created a potential problem for many Internet users. Alternate Medicine System solves this problem by searching through a large volume of medical information to provide users with filters and services. This project explores the different characteristics and potentials of different recommendation techniques in recommendation systems to serve as a compass for research and practice in the field of medical recommendation systems. In this project, we have used a dataset over 100+ medicines from different companies and brands to do recommendation based on the content of medicine and then filter it based on rating and cost-based analysis. Through experimental results, we have found that more than 95% of the medicines have a lower cost based alternative available with a higher rating.

Published by: Roshani R. Zamare, Shital P. Dhok, Sampada V. Babhulkar, Richa S. Singh, Jayshri G. Marbade, Nayan D. Bawane, Abhishek M. Shukla

Author: Roshani R. Zamare

Paper ID: V6I3-1621

Paper Status: published

Published: June 26, 2020

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Research Paper

Optimized social media customer support using machine learning with AWS

The rising popularity of social media communication can be witnessed everywhere. Not only individuals but companies too have noticed the importance of communication via social media and persist. Associations between customers and companies can be seen via social media. Customers might choose to publicize their opinions about a service or product via social media. Twitter is exceptionally used for is such cases. In this paper, we will exhibit how everyday customers are publicizing their items or taking the criticism for their items utilizing Web-based social networking systems like Twitter. In this procedure, the framework will consumer messages (tweets) and apply machine learning techniques (SVM, Porter stemming) to analyze the nature of the tweet via sentiment analysis. The results will be shown as tweets regarding positive, negative. We likewise will provide an automated response to the tweet (if found negative) and forward the message contents to the customer care email client for quick analysis and explicit response.

Published by: Supreeth Basabattini, N. Nikitha, Risita Kumar, Mathiyalagan R.

Author: Supreeth Basabattini

Paper ID: V6I3-1611

Paper Status: published

Published: June 26, 2020

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Review Paper

Review paper on the behavior of RC structure subjected blast load

As we know that nowadays the increase in the number of terrorist attacks especially in the last few years has shown the effect of blast load on buildings is a serious matter that should be taken into consideration in the design process. Although these kinds of attacks are exceptional cases, man-made disasters; blast loads are in fact dynamic loads that need to be carefully calculated just like earthquakes and wind loads. In that I have calculated the blast load manually by using IS code 4991-1968 and the analysis is carried out in multi-storeyed building by using STADD-Pro Software.

Published by: Diksha Menghare, Raju Bondre

Author: Diksha Menghare

Paper ID: V6I3-1608

Paper Status: published

Published: June 26, 2020

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