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Research Paper

Prediction of chronic kidney disease and diet recommendation

Chronic renal disorder is that the sort of disease within which there's a decrease in kidney function over a period of months or years. Early prediction of CKD is one in all the main problem in medical fields. So automated. tools which use. machine learning techniques determine the patient’s kidney condition which will be helpful to the doctors in prediction of disease.. Our system retrieves the features which are significantly affects the human with CKD, and so the ML technique which automates the classification of the disease into different stages. Our main goal is to predict the disease stage and suggest suitable diet for CKD patients using classification algorithms on medical test records. Diet recommendations for patients are going to be given per the potassium zone which is calculated using blood potassium level to weigh down the progression of CKD.

Published by: Annapoorna B. A., Nisarga Y. N., Rachana R. Shastry, Sreelatha P. K.

Author: Annapoorna B. A.

Paper ID: V7I4-1351

Paper Status: published

Published: July 14, 2021

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Research Paper

Stock Sentiment Analysis using News Headlines

Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relation between news and stock trend. To show this, we created three different classification models which depict polarity of news articles being positive or negative.

Published by: Tavva Sai Prathyusha, Thummapala Mounika, Vemaraju Siri chandana, Vysyaraju priyanka, Seepana Priyanka

Author: Tavva Sai Prathyusha

Paper ID: V7I4-1349

Paper Status: published

Published: July 14, 2021

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Research Paper

Antimicrobial textiles based on nanosilver

In recent years, Antimicrobial textiles have been an area of great research due to many of their desired properties like reducing infection transmission in the medical environment, enhanced performance, and improving odor control. Their basic feature is to protect the fabric from rotting caused by microbes. Antimicrobial textiles fulfill functions such as preventing cross-infection, arresting microbial metabolism, and protecting the textile from staining, discoloration, and quality deterioration.

Published by: Meenal Singh

Author: Meenal Singh

Paper ID: V7I4-1328

Paper Status: published

Published: July 14, 2021

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Review Paper

Glycerol – An alternate energy source for livestock feeding

Glycerol, is being an attractive feed ingredient for cattle, is a by-product of a wide range of industrial applications. Glycerol has potential value in livestock feeding since it improves feed efficiency, metabolism, and can avoid ketosis. Research indicates that glycerol can be a suitable partial grain replacement in the diet of cows during the transition period and at early lactation. The impact on milk yield is not significant, but glycerol mostly decreases milk fat content. The inclusion of glycerol in the dairy ration has an effect on ruminal fermentation patterns. Glycerol is rapidly fermented in the rumen into propionate, and it is metabolized to glucose in the liver through the process of glycogenolysis, additionally, glycerol administration to ruminants can reduce greenhouse gas emissions. The purpose of this review is to highlight the potential benefits and drawbacks related to the use of glycerol in cattle. Glycerol, from biodiesel Industries, must be purified in order to make it a useful product for livestock feeding. The use of glycerol in ruminant nutrition can be justified for several reasons: (i) as a source of energy, (ii) as a glycogenic precursor, and (iii) it may have an effect on milk composition. The high energy value of glycerol provides the opportunity to use it as a partial substitute for grain in dairy rations. Supplementation of glycerol in dairy animals' diets is associated with increased propionate, butyrate, valerate, and isovalerate concentrations in the rumen. Glycerol can be used as 10%–15% of the dietary dry matter (DM) and is a well-established tool for treatment for ketosis in cows. Glycerol increases plasma glucose and may reduce non-esterified fatty acids and hydroxybutyrate levels. The dietary supplementation of glycerol does not have a direct effect on DM intake, milk yield, or milk composition. However, some researchers reported an increase in milk yield after glycerol supplementation associated with decreased milk fat concentration. It is also possible that the concentration in the milk of odd-chain fatty acids and cis-9, trans-11 conjugated linoleic acid may increase after glycerol application.

Published by: Mukesh Sharma, Dr. Manoj Gendley, Dr. Kranti Sharma

Author: Mukesh Sharma

Paper ID: V7I4-1356

Paper Status: published

Published: July 14, 2021

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Research Paper

Effect of COVID-19 on consumer behavior in the corporate sector

The coronavirus pandemic has made the longest lockdown ever in the world in history. This pandemic has affected all the human health, food chains, international businesses, companies, global economy as well as it impacts on the behaviour of the consumers. As all the offline stores were closed during the lockdown, a large number of consumers are using online stores to fulfil their needs. In that pandemic situation, purchasing of most products from online stores increased about 6-10% in all over the world. Therefore, the pandemic has increased the use of digital platforms and consumer behaviour has a large impact of covid19. Consumer behaviour helps the marketers to gain knowledge about the expectations of the customers and it makes successful business as the organization provides products according to the customers’ needs. Therefore, consumer behaviour led to an impact of making successful business.

Published by: Priya Nair, Anirrban Ghosh, Sanjay Rao

Author: Priya Nair

Paper ID: V7I4-1350

Paper Status: published

Published: July 14, 2021

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Review Paper

Phishing website detection using Machine Learning

Today internet or websites plays a major role in every person's life. Almost Every sector using the website services like the banking sector using some apps for payment, the passport sector is using for registration and renewal of passport, the government sector for application of PAN cards, adhar cards, etc. This makes human life simpler. But this provides an opportunity to make phishing websites. Phishing websites are the same as legitimate or real websites. The purpose of the phishers is to steal their personal information, account ID, Passwords from individuals and organizations. They even add some tricks by asking security questions like a pet name, city name to gain users' trust. Although legitimate and phishing looks like same there are some features that make difference between that two things. That features such as IP address, URL length, having @ symbol, double slash redirection, Prefix, and suffix, having subdomain, domain registration link, HTTPS tokens, Request URL, URL of anchor, disabling right-click, using a pop-up window, and some more. Already many approaches are proposed for detecting phishing websites machine learning is the most appropriate one. This is because there are some common features that can be identified by machine learning. In this paper, we used a random forest algorithm to detect phishing websites based on features that make difference between both of the websites.

Published by: R. Dhatri, N. Shafiyabi, U. Nithish, P. Vamsi, K. Subrahmanya Kousik

Author: R. Dhatri

Paper ID: V7I4-1324

Paper Status: published

Published: July 14, 2021

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