Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

2021 - 20223

Settings

Subject: search.filters.subject.Covid-19

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    M-CAD: Towards Multi-Categorical Auto Diagnosis of Varied Diseases using Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2021) Praveen, K.; Patil, N.; Srikanth, C.S.; Nayaka, J.
    The economic burden and the number of lives lost due to diagnostic errors are higher than ever due to the onset of pandemics and new viruses, Specially in medium and low-economic status nations (including India) are affected heavily in terms of capital and human resources. Due to limited expertise in diagnostic technologies in remote parts of India and many low-economic nations of Africa, autonomous diagnostics can save millions of lives and lower the costs. To accomplish this goal we propose a method that uses modern developments in Deep Learning in semantic segmentation and classification to predict multiple diseases from multiple medical images. To conduct the study we test the model with Dermoscopy images and CT-Scans to predict 8 classes relating to Melanoma cancer, Covid-19 virus and different types of Carcinoma. The setup is tested on largest publicly available ISIC Dermoscopy dataset, 1061 CT-scan images combined for the classification and Segmentation(only for Melanoma). Classification model(M-CAD) is progressively tested by increasing the number of classes and data that it trains on. This pilot study is conducted on a small subset of the complete data, segmentation of Melanoma images obtained an accuracy of 96.6% compared to human expert agreement which is 90.9%. we were able to produce average accuracy of 81.5% and AUC of 0.94 for 6 classes using CT-Scans whereas accuracy and AUC for all the 8 classes is 80.2% and 0.97 respectively. These results were quite promising for a model that classifies different images with no apparent relation at all. © 2021 IEEE.
  • No Thumbnail Available
    Item
    Covid-19 Fake News Detector using Hybrid Convolutional and Bi-LSTM Model
    (Institute of Electrical and Electronics Engineers Inc., 2021) Surendran, P.; Balamuralidhar, B.; Kambham, H.; Anand Kumar, M.
    Fake news is essentially incorrect and deceiving information presented to the public as news with the motive of tarnishing the reputations of individuals and organizations. In today's world, where we are so closely connected due to the internet, we see a boom in the development of social networking platforms and, thus, the amount of news circulated over the internet. We must keep in mind that fake news circulated on social media and other platforms can cause problems and false alarms in society. In some cases, false information can cause panic and have a dangerous effect on society and the people who believe it to be true. Along with the virus, the Covid-19 pandemic has also brought on distribution and spreading of misinformation. Claims of fake cures, wrong interpretations of government policies, false statistics, etc., bring about a need for a fact-checking system that keeps the circulating news in control. This work examines multiple models and builds an Artificial Intelligence system to detect Covid-19 fake news using a deep neural network. © 2021 IEEE.
  • No Thumbnail Available
    Item
    Social Media Enabled Rehabilitation Services: Influence of Covid-19 Metaphors
    (Springer Science and Business Media Deutschland GmbH, 2022) Alathur, S.
    This study analyses the social media discussions on disability during the Covid-19 in the Southern part of India. The purpose is to assess the e-participants’ attitude toward disability problems during their social media participation. Participant observation and focus group discussions with citizens often post in social media disability groups. Existing studies less reported the social media disability discussions in regional language, even though it is vital to explore justice at the grassroots level of disability inclusion. The current findings show that citizens’ ill-conceived understanding of disability challenges during the covid-19 results in an unsupportive social media environment. The extreme and exploitation expressions in regional language groups show the lack of local support from the common public for the disabled during the pandemic. The lack of non-institutional voluntary metaphors emphasizes the need to improve the disability support structures at the regional level. Recommendations to enhance e-participation competency are provided. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.