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Development and evaluation of solar photovoltaic driven condensing unit for the solar distillation system

Solar distillation is a promising technology for clean water production in areas with abundant solar energy. The main constraint of this technology is low production capacity. In various studies, it has been found that improving water evaporation and vapor condensation inside distillation units can help in increasing the productivity of the solar distillation unit. In the proposed research work single basin double slope solar distillation unit was modified using external reflectors and a solar photovoltaic driven condenser. Experimentation was carried out for comparative performance evaluation of simple conventional distillation unit and modified distillation unit with external reflector and condenser. It was found that the external reflector on the glass cover increases the incident solar energy and thus increases water temperature inside the distillation unit. The rate of evaporation also increased due to condensing fan and condenser coupled with the distillation unit. Results obtained for comparative performance simple distillation unit and modified distillation unit shows that the productivity of single basin double slope solar distillation unit with reflector and SPV drove condenser was 35%. Also, it was found that the modified distillation unit was economically feasible than the conventional distillation unit.

Published by: Supriya Balasaheb Ghule, Dr. A. G. Mohod, Dr. Y. P. Khandetod, Dr. K. G. Dhande, M. B. Patil

Author: Supriya Balasaheb Ghule

Paper ID: V7I4-1357

Paper Status: published

Published: July 18, 2021

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Research Paper

Melanoma Classification using Convolutional Neural Network Model Integrated with Tabular Model

Melanoma is a major skin cancer type that has a very high death rate. The various sorts of skin abrasions cause an imprecise analysis because of their high resemblance. Precise categorization of the skin abrasions in the premature phase will allow dermatologists to cure the affected individuals in well time and hence saving their lives. This is backed by a research that shows that 90% of the cases are curable, if identified in the initial phase. With the advancements in the computing power and image classification, automatic detection of the melanoma using computer algorithms has become far reliable. With many methods used, neural networks prove to be the best solution devised to attain the highest accuracy in classifying melanoma through early symptoms. We did our survey to find the drawbacks of recent models that serve this purpose with the goal to overcome them and provide a better solution. With the findings based on this survey, we proposed a model that stives to overcome the drawbacks concluded from the previous models. With an accuracy of ~96%, the proposed model provides better solution in prediciting whether the skin lesions are malignant or not.

Published by: Harshkumar Modi, Bhavya Chhabra, Sukkrit

Author: Harshkumar Modi

Paper ID: V7I4-1427

Paper Status: published

Published: July 18, 2021

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Research Paper

Facial Recognition with Expression

In this paper, motions are a strong tool in communication and a technique that humans show their emotions is through their facial expressions. one among the difficult and powerful tasks in social communications is countenance recognition, as in non-verbal communication, facial expressions are key. In the field of computing, face expression Recognition (FER) may be a spirited analysis space, with many recent studies using Convolutional Neural Networks (CNNs). during this paper, we demonstrate the classification of FER supported static images, using CNNs, while not requiring any pre-processing or feature extraction tasks. The paper conjointly illustrates techniques to improve future accuracy throughout this space by victimisation preprocessing, which includes face detection and illumination correction. Feature extraction is utilized to extract the foremost prominent components of the face, together with the jaw, mouth, eyes, nose, and eyebrows. what is more, we tend to conjointly discuss the literature review and gift our CNN design, and the challenges of victimisation max-pooling and dropout, that eventually aided in higher performance. we tend to obtained a check accuracy of 61.7% on FER2013 throughout a seven-classes classification task compared to seventy five.2% in progressive classification.

Published by: Manoj P., Mahesh N., Shivakumar Vishwakarma R., Alpha Vijayan

Author: Manoj P.

Paper ID: V7I4-1410

Paper Status: published

Published: July 18, 2021

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Research Paper

Digital health care system for medical report/ certificate sharing

The health care services industry is always showing signs of change and supporting new advancements and advances. This paper describes our blockchain architecture as a new system solution to supply a reliable mechanism for secure and efficient medical record exchanges. It is going to revolutionize the e-Health industry with greater efficiency by eliminating many of the intermediates as we know them today. Digital Health Care (DHC) reform suggests a new consumer paradigm in data and information processing. Here users can be distributed or access networked collaborative care to a centralized repository of personal health records. The new approach in this paper is to explore the Advanced Block-Chain paradigm for e-Health record-keeping and while addressing the special needs of patient privacy. As society is moving towards peer networking and online practices, we are going to combine the best parts of the two worlds in both the healthcare regulation and the technology revolution while formulating advanced solutions.

Published by: Komal Suresh Raut

Author: Komal Suresh Raut

Paper ID: V7I4-1393

Paper Status: published

Published: July 18, 2021

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Research Paper

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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