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Multidrug-resistant Acinetobacter baumanii infections – A dreadful superbug to humans

Acinetobacter baumanii is considered to be one of the significant pathogens causing nosocomial infections and outbreaks in hospital settings, especially in ICU. The dreadful concern with Acinetobacter infections is their ability to gain resistance against all commonly used antibiotics like Penicillin, cephalosporins, Fluoroquinolones, carboxypenicillin, carbapenems as a result this Multi-Drug Resistant (MDR) strains increases patient hospital stay, cost of treatment, mortality, and morbidity among affected patients. A total of 146 Acinetobacter isolates were received from various clinical samples. Samples received were routinely subjected to Culture. Gram staining was done. The smear showed Gram-Negative Coccobacilli taken and subjected for further biochemical reactions. Antibiotic susceptibility testing was done by Kirby Bauer disc diffusion method in Muller -Hinton agar plate. The majority of Acinetobacter baumanii isolates were received from the 19-40 age group (45.8%), followed by the 41- 60 (26% ) age group. Most of the Acinetobacter baumanii strains were isolated from wound swab 33.5%, followed by urine 29.4%, sputum 17.1%, Blood 10.2%.Antibiotic sensitivity of 146 Acinetobacter baumanii isolates showed Highest resistance to Ampicillin (100%), Gentamycin (70.5%),Cotrimoxazole (68.4%) , Piperacillin Tazobactam (66.4%) , Ciprofloxacin (63%), Nitrofurantoin (61.6%) , Ceftriaxone (61.6%). Levofloxacin, Amikacin, Tigecycline, Colistin were found to be the most effective antibiotics with a sensitivity of 73.2%, 87.6%, 91.7%, 100% respectively. Of 146 isolates 63 (43%) were Multi-Drug Resistant, i.e isolates showing resistance to more than 3 classes of antibiotics. Hence strict infection control and antimicrobial stewardship policies should be implemented in hospitals to reduce mortality and morbidity associated with Acinetobacter baumanii infections.

Published by: Dr. N. A. Fairoz Banu, Dr. Chitralekha Saikumar

Author: Dr. N. A. Fairoz Banu

Paper ID: V7I4-1398

Paper Status: published

Published: July 18, 2021

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Research Paper

Face Mask Detection

Manual surveillance of whether people are wearing a mask or not is not only costly but also a risky process. In this present world, which is being hit by the Coronavirus pandemic, the risk of spread of virus is exponentially high when people are not wearing mask. Thus, the automation of the process of detection of people not wearing mask had become a necessity. Our project uses deep learning techniques to determine whether a person is wearing a mask or not. Our proposed system identifies the mask violators automatically which reduces the risk of transmission of virus and also makes it easier for the authority to monitor the mask violators and take action against them without being put at risk of transmitting the virus. We use MobileNetV2 architecture to efficiently achieve best accuracy which is the key to our system. We were able to achieve accuracy of 99.22% after training the model. Detecting and tracking the face mask is the main aim of the project.

Published by: Bhaskar N. Patel, Siddhant Sipoliya, Shashwat Mishra, Shreyansh, Sreelatha P. K.

Author: Bhaskar N. Patel

Paper ID: V7I4-1379

Paper Status: published

Published: July 18, 2021

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Research Paper

The Pink City litter survey: Litter investigation of Kardhani Market Malviya Nagar, Jaipur

Nowadays, Litter is a worldwide problem. Every country expends a large amount of money to clean up litter. The USA expended almost 11 billion dollars annually. Litter is an environmental problem which not only responsible for air, water, and soil pollution but also produces harmful effects on humans, animals, plants, and the risk of fire hazards, endangering. Litter is big a problem also in India. Annual waste generation is almost 62 million tons in India, only 43 million tons collected by the different Govt. & private bodies. It is clear that almost 19-million-ton waste remains in the form of litter so our study focused on identifying the nature and source of litter and on solving this problem. We did a survey to identify nature, source, and public attitudes towards litter. We did 2 types of litter surveys in the Satkaar market, Jaipur (Rajasthan). In the first survey, we collected litter material from three streets of the Satkaar market then took it to the lab for analysis. Firstly we categorized litter into seven categories plastic, paper and cardboard, composite material, glass item, metallic items, food and vegetable waste, and other category waste (foam, rubber, cigarette butts, flower and garland, cloths, bird feather). we found that most littering items ids plastic (almost 40% of total % weight of litter ), average recyclable litter is 34.66 % and biodegradable litter is about 43%.in the second survey I made a set of 23 questions to know the attitude and behavior of public towards litter. We found in this survey that most people think that litter is a crime, both administration, and the public are responsible for litter when the educational level increased then the habit of littering is reduced.

Published by: Ishfaq Ul Abass, Sanjay Kumar Meena, Sumit Kumar, Satynarayan Chaudhary, Shan Ahmad Mir, Raghvendra Sharma

Author: Ishfaq Ul Abass

Paper ID: V7I4-1382

Paper Status: published

Published: July 18, 2021

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Survey Report

Analysis of business process automation: RPA

This research is a study to explore the features available in the leading RPA software for business. Robotic process automation (RPA) is a leading software technology that helps in building, deploying, and managing software robots that surpass human actions interacting with digital system. The software bot can perform task as of human do like identifying what’s there on the screen, extracting & identifying data, navigating system etc. But RPA robots are more consistent than human. The main highlights of this study discussed the Pros and Cons of RPA.

Published by: Namrata Manohar Dhuri, Dr. Abhijit N. Banubakode

Author: Namrata Manohar Dhuri

Paper ID: V7I4-1396

Paper Status: published

Published: July 16, 2021

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Research Paper

Association of social networking addiction and anorexia nervosa (desire for thinness)

The present research was conducted to study the association between Social Networking addiction and Anorexia Nervosa( desire for thinness) among adolescents. A sample of 913 junior college students were selected as a sample for the present study.The findings suggest that there is no significant association between Social networking addiction and Anorexia nervosa.

Published by: Dr. Khan Zeenat Muzaffar

Author: Dr. Khan Zeenat Muzaffar

Paper ID: V7I4-1287

Paper Status: published

Published: July 16, 2021

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Research Paper

Recognition of Handwritten digits using Machine Learning and Deep Learning algorithms

Digitalization has become very prominent in today’s world. The need for storing information in computers is rapidly increasing. Converting handwritten documents into digital form by humans is often difficult and time-consuming. With the rapid development of technology, human’s reliance on machines to do time-consuming and monotonic tasks also greatly increased. Machine learning and deep learning are the major fields in Computer Science that have developed intelligent algorithms to train machines to do a set of repetitive tasks. Handwritten digit recognition is one of the significant areas of research and development with an increasingly large number of possibilities that could be attained. Handwritten Digit Recognition is the ability of a computer to receive and interpret handwritten input from various sources such as paper documents, photographs, touch screens, and other devices. This paper illustrates handwritten digit recognition with the help of MNIST datasets using Support Vector Machines (SVM) and Convolution Neural Network (CNN) models. The main objective of this paper is to compare the accuracy of the models stated above and develop a Graphical User Interface (GUI) application with the most accurate model.

Published by: Sahithi Akundi, B. Prajna, Bathina Lakshmi Rishitha, Balaka Supraja, Arugula Gayathri

Author: Sahithi Akundi

Paper ID: V7I4-1381

Paper Status: published

Published: July 15, 2021

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