Institution
National Institute of Technology, Karnataka
Education•Mangalore, Karnataka, India•
About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Corrosion & Cloud computing. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.
Topics: Corrosion, Cloud computing, Microstructure, Alloy, Heat transfer
Papers published on a yearly basis
Papers
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TL;DR: In this paper, 4-(N,N-diethylamino)benzaldehyde thiosemicarbazone (DEABT) was studied for its corrosion inhibition property on the corrosion of aged 18 Ni 250 grade maraging steel in 0.67 M phosphoric acid at 30-50°C by potentiodynamic polarization, EIS and weight loss techniques.
64 citations
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TL;DR: Experimental analysis proves that the proposed approach reduces energy consumption and the rate of migrations, and hence offers much scope for future research.
64 citations
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TL;DR: In this article, the performance of reactive powder concrete (RPC) at elevated temperatures from 200°C to 800°C was evaluated, by obtaining residual mechanical properties after exposure.
64 citations
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TL;DR: In this article, three new Donor-π-Acceptor type dyes D1-3 carrying 3-(1-hexyl-1H-indol-3-yl)-2-(thiophen-2-yl)acrylonitrile as backbone with three different acceptor units were designed and synthesized as promising sensitizers for solar cell application.
64 citations
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TL;DR: Wang et al. as discussed by the authors used deep learning architectures to develop a Coronavirus diagnostic system using X-ray and CT images, and achieved the accuracy of 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively.
Abstract: In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible.
64 citations
Authors
Showing all 5100 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ajay Kumar | 53 | 809 | 12181 |
Bhiksha Raj | 51 | 359 | 13064 |
Alexander P. Lyubartsev | 49 | 184 | 9200 |
Vijay Nair | 47 | 425 | 10411 |
Sukumar Mishra | 44 | 405 | 7905 |
Arun M. Isloor | 38 | 261 | 6272 |
Vinay Kumaran | 36 | 262 | 4473 |
M. C. Ray | 30 | 115 | 2662 |
Airody Vasudeva Adhikari | 30 | 119 | 2832 |
Ian R. Lane | 27 | 129 | 2947 |
D. Krishna Bhat | 26 | 95 | 1715 |
Anurag Kumar | 26 | 126 | 2276 |
Soma Biswas | 25 | 127 | 2195 |
Chandan Kumar | 25 | 66 | 1806 |
H.S. Nagaraja | 23 | 90 | 1609 |