Manuscripts

Recent Papers

Research Paper

Simulated and observed DO (Dissolved Oxygen) values for selected major stations of spring-fed Kosi River stretch through model WASP (Water Quality Analysis Simulation Program)

The river Kosi is a spring fed river of Indian Himalayan region which quenches the thirst of major population of Kumaon region. The model WASP (Water quality analysis simulation program)is a widely used model suggested by EPA that can help simulate the water quality processes in diverse water bodies as reservoirs, ponds, lakes river like surface water bodies. The analysis in the 4 major selected stations of the stretch for Dissolved Oxygen was found to be 4-11mg/l as measured value whereas simulated values ranged from 1 mg/l to 10mg/l at the origin of the river, whereas at the end point of the river the simulated and measured value ranged from 4mg/l-8mg/l , 6mg/l. The Dissolved oxygen in the water is very essential to estimate the water quality as it directly affects the aquatic life and also signifies the presence of organic matter as well as microorganisms in the water. The continuous variations in the DO levels are observed in the entire stretch. The probable reason is uneven rainfall, scattered population and terrain complexities of the region. The continuous and frequent merging of various springs into the main course river results in frequent fluctuations in the physicochemical parameters of the river. This study is an attempt to comparatively analyse the water quality of the only river of the region with the simulated values from model WASP and observed water quality from daily sampling from the water body.

Published by: Dr. Pooja Rani Sinha, Kireet Kumar, Dr. V. P. Uniyal

Author: Dr. Pooja Rani Sinha

Paper ID: V7I6-1144

Paper Status: published

Published: November 8, 2021

Full Details
Research Paper

Stock Market prediction made easy with Machine Learning algorithms

The main objective of this paper is to find the best model for predicting the stock market movement. We have tested various models based on machine learning that were previously implemented and during the process, we found out that the Random Forest and Support Vector Machine algorithms were not exploited well. In this paper, we are going to find out a more feasible method to predict the stock market with higher accuracy. We have taken a dataset of stock market prices from previous years and pre-processed the data for real analysis. So, our paper will also be focusing on pre-processing of the raw dataset. After pre-processing, we will be reviewing the use of random forest and support vector machine on the datasets and the outcome it generates. The paper also examines the feasibility of the prediction system in real-world settings and issues associated with the accuracy of predicting the market. If this model achieves higher accuracy than previously implemented machine learning algorithms then it can prove to be a great asset for stockbrokers, institutions, and individual investors.

Published by: Vaibhav Kumar, Rishabh Raj

Author: Vaibhav Kumar

Paper ID: V7I6-1138

Paper Status: published

Published: November 8, 2021

Full Details
Research Paper

House Price Prediction using Machine Learning

In today's society, everyone wants a home that fits their lifestyle and budget while still providing the facilities they require. House values fluctuate a great deal, indicating that they are typically overstated. Many criteria must be considered when projecting house prices, including the location, number of rooms, carpet area, age of the property, and other fundamental local features. This study seeks to forecast house prices based on all of the main factors that go into deciding the price.

Published by: Aldrin Fernandes, Abdullah Qureshi, Aamir Shaikh, Amit Narote

Author: Aldrin Fernandes

Paper ID: V7I6-1155

Paper Status: published

Published: November 6, 2021

Full Details
Thesis

Validated RP–HPLC method for determination of Magaldrate and Simethicone in tablet dosage form

A RP-HPLC method was developed for the estimation of Magaldrate and Simethicone in tablet dosage form which is simple, less time consuming using an economical column. The essay of the sample has been carried out with assay percentage of 99.56% and 101.97% Magaldrate and Simethicone respectively. The developed method has been validated for different parameters like, precision, ID precision, Linearity, Accuracy, Robustness, LOD and LOQ and it is simple, specific accurate and economical.

Published by: K. Chaithanya, Omar Mohammed, P. Satish Kumar

Author: K. Chaithanya

Paper ID: V7I6-1145

Paper Status: published

Published: November 6, 2021

Full Details
Research Paper

Five Level Inverter for Renewable Energy Power Generation Systems

A five level inverter is developed and applied for to reduce the switching loss, harmonic distortion and electromagnetic interference caused by the switching operation of power semiconductor devices.

Published by: M. Divya, Golagani Rama Harini, Bowribilli Mounika, Shaik Karishma, Vesapogu Jyothsna, Bhimavarapu Yuva Madhuri

Author: M. Divya

Paper ID: V7I6-1143

Paper Status: published

Published: November 6, 2021

Full Details
Thesis

Method Development and Validation for the estimation of Azilsartan in Bulk and Pharmaceutical Dosage Form by using HPLC

A simple, sensitive and rapid stability indicating HPLC method was developed and validated for the determination of Azilsartan in bulk and pharmaceutical dosage form. The method was developed by HPLC using a Inertsil C18 (250x4.5mm ID) 5µm column in a isocratic mode with mobile phase constituted by buffer: Methanol and water, pH 3.5 flow rate was 1.0ml/min, column temperature at 20-25°C, UV detection wavelength 240nm and 20µL of injection volume. The retention time of Azilsartan was 3.084min. The validation parameters were in accordance with ICH specifications, assay exhibited a linear range of 50-250µg/ml with regression coefficient 0.998. The limit of detection and quantification were 0.46 µg/ml and 1.42 µg/ml. Accuracy was between 98-102%. The drug was subjected to various stress conditions like peroxide, photolytic, acidic, alkaline, thermal degradations. Stress study of Azilsartan was found susceptible to degrade under hydrolytic (acid and base) conditions. The proposed method has stability indicating the resolution of the main peak from their degradation peak

Published by: P. Satish Kumar, Ravi Harsha, Prathiba

Author: P. Satish Kumar

Paper ID: V7I6-1137

Paper Status: published

Published: October 30, 2021

Full Details