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Research Paper

Biochemical analysis of Siddha mono herbal drug Amukkara Podi( Amukkara Karpam)

Siddhar Yogam or Siddhar Yoga Muraigal are the ancient methods that were advocated by the Siddhars in order to prevent the ailments occurring due to human aging. Also, these methods consisted of physical, mental, and even dietary practices, that must be practiced for a period of a mandala (48 days). The dietary practices, that were advocated by them are the ‘Kaayakarpam’ practices. These usually include the intake of a certain poly or monoherbal drug, for a stipulated span of time. They may either be the drug of choice for general body wellness, or for a specific disease. Considering Amukkara Podi (the powdered root of Withania Somnifera), it has several references as a Kaayakarpam[1] drug for general body wellness. As a special drug, it acts wonderfully towards weight loss for obese patients, when taken in with honey. Obesity[2] or being obese is a health disorder that is characterized by the presence of a BMI of more than 25. These patients tend to be overweight, have a lethargic attitude or a condition of tiredness towards work, and at later stages may suffer from knee, hip pains, etc. Obesity has also been considered to be an uprising health disorder of concern by WHO. The treatment generally includes rigorous exercise in order to lose bodyweight. In cases where obese individuals cannot lose weight through exercise, other options could be taken into consideration. One among them is the intake of Amukkara Podi with Honey. This paper reports the biochemical analysis of the drug, ‘Amukkara Podi’, which could be used as a forerunner for the usage of the drug against obesity.

Published by: Dr. Binthuja G. Dharan, Dr. G. Thirunarayanan, Dr. A. S. Poongodi Kanthimathi

Author: Dr. Binthuja G. Dharan

Paper ID: V7I2-1554

Paper Status: published

Published: April 30, 2021

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Research Paper

Machine Learning Algorithms to Predict Next Day Rain in Australia

This paper predicts whether it will rain the next day or not in Australia. This paper compares 4 machine learning algorithms namely Random Forest Classifier, XGBoost, Light GBM and Logistic Regression models by training and testing them with the data set. The XGBoost Model performed the best when compared to Random Forest Classifier, Logistic Regression and LightGBM. XGBoost produced an accuracy of 94.03%. The models could have performed better if the date was treated as a cyclic continuous feature because the weather itself is cyclic which shows a similar trend during the same seasons.

Published by: P. Sai Dinesh Reddy

Author: P. Sai Dinesh Reddy

Paper ID: V7I2-1543

Paper Status: published

Published: April 30, 2021

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Case Study

Internet of Things Based Smart Society

Internet of Things (IoT) is a fledgling and popular technology, which has given the provision to people for doing daily work like, at just a click or on command. The Internet of Things has helped develop many amazing Things like the smart street where streetlight gets automatically switched on or off with the help of sensor data. Well-sensor is a crucial factor in the field of Internet of Things applications. The great developers and people from the industry are working continuously to develop a system that will help sectors like healthcare, agricultural, industrial, construction, etc. A lot of researchers do research on the Internet of Things as a part of the interest and as a result, they end up developing amazing and unique systems. This paper produces a summary of the Internet of Things system named “Smart Society” which consists of 5 unique modules which are Smart Street Lighting, Disaster Management System, House Safety, Smart Gardening and Healthy Environment. In the further part of the paper, the components and the software systems are discussed which have been utilized to develop this model, and at last, the challenges and future scope of the “Smart Society System”.

Published by: Nitin Nandkumar Sakhare, Dhaval Tanna, Rohit Bhokarikar, Aboli Rode, Aachal Rathod

Author: Nitin Nandkumar Sakhare

Paper ID: V7I2-1531

Paper Status: published

Published: April 30, 2021

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Review Paper

A review on the impact of mobility patterns and prediction on Covid-19 rates

After the outbreak of COVID-19 several prediction models are being used by authorities and officials all over the world to implement appropriate control measures and to make well-informed decisions. Due to an enormous level of ambiguity and shortage of crucial data, conventional models have shown low efficiency for long-term prediction. A coronavirus is a contagious disease that is resulting in the massive growth of COVID-19 cases, therefore, we have used human mobility patterns as the effective factor to take preventive measures to thereby stop the outbreak. In this model, we have used methods such as exponential growth, and prophet models. We here demonstrate the impact of changes in mobility patterns by binding the data in Data Science to efficiently trace the disease, plan strategies, methods and foretell the future growth of the pandemic.

Published by: Priyanka Dongre, Rachna Pazare, Mansi Lanje, Anushka Burewar, Ekta Gajbhiye, Abhishek Kumar

Author: Priyanka Dongre

Paper ID: V7I2-1389

Paper Status: published

Published: April 28, 2021

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Research Paper

Stock price prediction using machine learning models in python

The project aims to provide retail investors with Machine Learning models to navigate through the stock market. This is achieved through the use of machine learning and Python. Several stock price prediction approaches and models are developed including Recurrent neural networks i.e. LSTM, simple linear regressions, and Decision Tree Models. By taking the past stock prices the models were trained and tested on that data to predict the stock prices.

Published by: Kathika Sai Krishna, Mettupalli Hari Naveen Reddy, Appidi Koushik Reddy, Mukkamalla Naveen Reddy

Author: Kathika Sai Krishna

Paper ID: V7I2-1525

Paper Status: published

Published: April 28, 2021

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Research Paper

Performance evaluation of artificial neural network in parameter estimation of infiltration models on a clay soil

In the present study, attempts have been made to predict the soil infiltration rate by using the Kostiakov infiltration model. Subsequently, the feedforward backpropagation Artificial Neural Network (ANN) was employed to evaluate the constants of the Kostiakov infiltration model. The double ring infiltrometer approach was employed for collecting the infiltration test data. Infiltration tests were carried out for winter and summer seasons on 106 observation points, over the study area. The parameters of the infiltration model stated above are determined along with this data for different soil properties like bulk density; moisture content; % sand; % silt; % clay; electrical conductivity; field capacity; and wilting point which were determined by experimentation. These data serve as input to the ANN and the parameters of the Kostiakov model were determined. The performances of ANN models with the different input combinations are evaluated for the prediction of the Kostiakov model parameters on clay soil. The performance of the ANN was assessed based on various evaluation criteria. The Nash-Sutcliffe efficiency was observed to be 93.79% and 97.71% for model parameters ‘a’ and 'b' respectively. Thus, the study demonstrates the application of ANN for the evaluation of infiltration characteristics of clay soil obviating the need for carrying out tedious field experimentation.

Published by: Dr. Chandrakant L. Jejurkar, Dr. Milind L. Waikar

Author: Dr. Chandrakant L. Jejurkar

Paper ID: V7I2-1456

Paper Status: published

Published: April 28, 2021

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