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Research Paper

Comparative studies of Eri Silkworm (Philosamia Cynthia Ricini) reared on artificial diet and natural castor leaves (Ricinus communis Linn)

Eri-silkworms (Philosamia cynthia ricini) were reared on castor leaves (its natural host plant) from 1st to 5th instars and artificial diet upto second instar then shifted to castor leaves. The larval duration was one day more in diet batches than leaves batches. The cocoon yield increased 13% cocoons in diet feed batches than leaf fed batches. Flimsy cocoons % was more in leaves chawki reared Eri silkworm. The Stable cocoons crops in artificial diet Eri chawki rearing than leaves rearing.

Published by: Mousumi Mondal, Dhattatreya Hosagrahar, Srinivas Bandlamuri Venkataramana

Author: Mousumi Mondal

Paper ID: V6I2-1471

Paper Status: published

Published: April 28, 2020

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Research Paper

IoT based smart car parking system

This paper presents an IoT Based Smart Car Parking System which displays the parking slot number of vacant slots on the LCD display when the car arrives at the gate. It initially checks the presence of the vehicle in each of the parking slots and then displays the slot number of a vacant slot on an LCD display placed near the gate. This guides the new car to park at the appropriate slot. The status of each parking slot is also uploaded onto thingspeak cloud platform. Raspberry pi is used to control the whole mechanism. This prototype also gives an option of controlling the opening/closing of the gate by a website. The gate can be controlled by a website created using HTML and flask package in Raspbian Operating System. This feature is added for security purposes. The objective of this paper is to develop an intelligent, user-friendly automated car parking system that reduces the manpower and traffic congestion and to offer safe and secure parking slots within a limited area.

Published by: Shravya K. Holla, Keerthi S., Vidhya Dhari L.

Author: Shravya K. Holla

Paper ID: V6I2-1466

Paper Status: published

Published: April 28, 2020

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Research Paper

Identification of potent COVID-19 Main Protease (Mpro) inhibitors from Curcumin analogues by Molecular Docking Analysis

These days, COVID-19, a new strain of coronavirus COVID-19 is rapidly spreading, has affected more than 210 countries and territories received global attention. The lack of efficacious medicines or vaccines in opposition to SARS-CoV-2 has also worsened the situation. Hence, there is a pressing want to increase up research for the improvement of potential therapeutics and low priced diagnostic in opposition to COVID-19. The crystallized form of COVID-19 main protease (Mpro) was illustrated by a Chinese researcher Liu et al. (2020) which is a novel therapeutic drug target. The goal of the study is to identify COVID-19 Mpro potential from mono-carbonyl analogues of curcumin through binding free energy analysis into COVID-19 by utilizing molecular docking. We conducted docking simulation to mono-carbonyl analogues of curcumin as ligands into the main protease of COVID-19 as a protein. The 3D structure of the COVID-19 Mpro was downloaded from PDB (Code ID: 6LU7). The structure of ligands was prepared using Chem Bio Draw Ultra 12.0.02. Docking process, the interaction, and binding of ligands – protein was done using the software Molegro Virtual Docking (MVD) and visualized using the software Molegro Molecular Viewer (MMV). The results showed hydrogen bonding and Steric interaction between compound A2 ( curcumin analogues) with, COVID-19. Moldock scores of compound A2 is -202.476 kcal/mol. It is predicted that compound A2 has potency as a lead compound to find new antiviral candidates against COVID-19 for possible therapeutic agents.

Published by: Jaydip Bhaliya, Vraj Shah

Author: Jaydip Bhaliya

Paper ID: V6I2-1462

Paper Status: published

Published: April 28, 2020

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Others

Alternatives to mitigate Corona

Coronavirus is most pandemic virus and it is challenging too for the scholars and scientists to discover its vaccine. Like every organism in nature struggling for survival, they evolve or mutate. Similarly, the coronavirus has changed its genome sequence to adapt to the new species. In fact, research has shown COVID-19 has mutated repeatedly in ways to boost its survival which results in more difficulties to discover its vaccine. We can’t hit the coronavirus directly and neither our immune system is capable enough to fight with it, but by killing the infected cells, we can kill the corona virus too.

Published by: Manjeet Singh, Lavina Pratap Bhambhani

Author: Manjeet Singh

Paper ID: V6I2-1463

Paper Status: published

Published: April 25, 2020

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Research Paper

ASIC implementation of smart home using VLSI design

In the upcoming new era, the need for improvement in technology and upgrades of existing devices has more priority. With such idea of upgrading the home automation system we have designed this model. In this model we used six sensors that is, temperature sensor (LM35), PIR motion sensor, smoke detection, water level sensor, garage sensor etc. For different purposes which ultimately leads to two major visions that is, (a) security (b) Comfort. In this model we have developed has highly improvements in both visions, like PIR motion sensor detects unusual intrusion in the home which helps to provide security. Here we used software tools Xilinx 14.4 to develop simulation results for each sensor. RTL schematic diagram is used to represent the pin level diagram. Therefore, to implant more features in home automation system and with high performance at low cost is our ultimate goal.

Published by: Syed Muqtar Nawaz, Sureddy Sravya, Mohammed Imran, C. Ramesh Kumar Reddy

Author: Syed Muqtar Nawaz

Paper ID: V6I2-1455

Paper Status: published

Published: April 25, 2020

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Research Paper

Prediction of heart diseases using Machine Learning algorithms

Heart-related diseases or cardiovascular diseases are the main reason for huge number of deaths in the world over last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more wisdom, time and expertise. Machine learning techniques help health care professionals in the diagnosis of heart disease. This aims a better understanding and application of machine learning in the medical domain, an automated system in medical diagnosis would enhance medical efficiency and reduce costs. In order to decrease the number of deaths by heart diseases there have to be a quick and efficient detection technique, the use of multiple machine learning algorithms for heart disease, models based on supervised learning algorithms such as: Decision tree, Naïve Bayes, K-Nearest Neighbors, Random Forest, Logistic Regression, and then implement them to predict heart disease based on patients’ medical records. Find the accuracy of the models, Choose the best output with the highest accuracy. These machine learning algorithms and techniques have been applied to various medical data sets. The implementation of work is done on heart disease data set from the University of California Irvine (UCI) machine learning repository, it contains several instances and attributes. By using the data set we test on different machine learning techniques and predict the best model which is computationally efficient as well as accurate for the prediction of heart disease.

Published by: N. Vineeth, V.N.V.L.S. Swathi, C. Vishal Sai, V. Vishal

Author: N. Vineeth

Paper ID: V6I2-1453

Paper Status: published

Published: April 25, 2020

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