Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
18 results
Search Results
Item COVIDDX: AI-based clinical decision support system for learning COVID-19 disease representations from multimodal patient data(SciTePress, 2021) Mayya, V.; Karthik, K.; Kamath S․, S.; Karadka, K.; Jeganathan, J.The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work, we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis reports were collected from reputed online sources. Convolutional neural network models were trained on this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision support system powered by ensemble deep learning models (CADNN) is designed and deployed on the web. The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19 representations for supporting clinical decisions. © © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reservedItem NITK_NLP at CheckThat! 2021: Ensemble transformer model for fake news classification(CEUR-WS, 2021) LekshmiAmmal, R.L.; Anand Kumar, M.Social media has become an inevitable part of our life as we are primarily dependent on them to get most of the news around us. However, the amount of false information propagated through it is much higher than the genuine ones, thus becoming a peril to society. In this paper, we have proposed a model for Fake News Classification as a part of CLEF2021 Checkthat! Lab1 shared task, which had Multi-class Fake News Detection and Topical Domain Classification of News Articles. We have used an ensemble model consisting of pre-trained transformer-based models that helped us achieve 4tℎ and 1st positions on the leaderboard of the two tasks. We achieved an F1-score of 0.4483 against a top score of 0.8376 in one task and a score of 0.8813 in another. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).Item COVID-19 detection from spectral features on the DiCOVA dataset(International Speech Communication Association, 2021) Ritwik, K.V.S.; Kalluri, S.B.; Vijayasenan, D.In this paper we investigate the cues of COVID-19 on sustained phonation of Vowel-/i/, deep breathing and number counting data of the DiCOVA dataset. We use an ensemble of classifiers trained on different features, namely, super-vectors, formants, harmonics and MFCC features. We fit a two-class Weighted SVM classifier to separate the COVID-19 audio from Non-COVID-19 audio. Weighted penalties help mitigate the challenge of class imbalance in the dataset. The results are reported on the stationary (breathing, Vowel-/i/) and nonstationary( counting data) data using individual and combination of features on each type of utterance. We find that the Formant information plays a crucial role in classification. The proposed system resulted in an AUC score of 0.734 for cross validation, and 0.717 for evaluation dataset. © © 2021 ISCA.Item COVID-19 Prediction Using Chest X-rays Images(Institute of Electrical and Electronics Engineers Inc., 2021) Kumar, A.; Sharma, N.; Naik, D.Understanding covid-19 became very important since large scale vaccination of this was not possible. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Till now in various fields, great success has been achieved using convolutional neural networks(CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. The proposed research work has performed transfer learning using deep learning models like Resnet50 and VGG16 and compare their performance with a newly developed CNN based model. Resnet50 and VGG16 are state of the art models and have been used extensively. A comparative analysis with them will give us an idea of how good our model is. Also, this research work develops a CNN model as it is expected to perform really good on image classification related problems. The proposed research work has used kaggle radiography dataset for training, validating and testing. Moreover, this research work has used another x-ray images dataset which have been created from two different sources. The result shows that the CNN model developed by us outperforms VGG16 and Resnet50 model. © 2021 IEEE.Item Reducing False Prediction on COVID-19 Detection Using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.; Varna, S.A.; Kumar, A.; Kumar, R.This paper proposes a custom deep neural network-based scheme for coronavirus disease 2019 (COVID-19) detection. The proposed method takes X-ray images that use transfer learning techniques on pre-trained models. One objective of this work is to quickening the detection of the virus. Another goal is to reduce the number of falsely detected cases by a significant margin. The experimental setup demonstrates promising results on the selected dataset, which achieve up to 99.74%, 99.69%, 98.80% as classification, precision, and recall accuracy. © 2021 IEEE.Item Machine Learning based COVID-19 Mortality Prediction using Common Patient Data(Institute of Electrical and Electronics Engineers Inc., 2022) Agrawal, S.; Patil, N.COVID-19 was declared a pandemic in 2020, and it caused havoc worldwide. The fact that it is unpredictable adds to its lethality. The world has already seen various COVID-19 infection waves, subsequent waves being even more deadly. Many patients are asymptomatic initially but suddenly develop breathing problems. More than four million people have died due to COVID-19. It is necessary to forecast a patient's likelihood of dying so that appropriate precautions can be implemented. In this study, a COVID-19 mortality prediction model which uses machine learning is proposed. Most of the current research work requires several patient features and lab test results to predict mortality. However, we suggest a simpler and more efficient technique that relies solely on X-rays and basic patient information such as age and gender. Several ensemble-based models were evaluated and compared using a variety of metrics, and the best method was able to achieve a classification accuracy of 92.6% and AUPRC of 0.95. © 2022 IEEE.Item Pattern Analysis of COVID-19 Based On Geotagged Social Media Data with Sociodemographic Factors(Institute of Electrical and Electronics Engineers Inc., 2022) Sabareesha, S.S.S.; Bhattacharjee, S.; Shetty, R.D.The world has faced a catastrophic global crisis of COVID-19 caused by coronavirus and called for analyzing the affected areas in any country. The study helps to understand how the second wave affected different states in India concerning sociodemographic factors, such as population density, economy, and unemployment rate. During the lockdown, the sudden impact of staying at home has led to increased social media usage, where people expressed their opinions on multiple topics. Twitter provides timestamp and sometimes spatial information of the tweets generated. Using the geotagged Twitter dataset, a study in India is performed in this work considering the second wave of COVID-19, which occurred approximately from April to June 2021. It analyses the temporal and spatial patterns of the geotagged tweets generated from all the states during the period mentioned above. Also, topic modeling and sentiment analysis are performed to understand the concerns discussed by the people. We use different states' sociodemographic factors and machine learning algorithms to divide the population into high and low categories to understand the topic prevalence in different socioeconomic groups. This study reveals that the low socioeconomic groups have shared more concerns, urging the government to help fight the COVID-19 pandemic. © 2022 IEEE.Item An Automated Approach for Screening COVID-19 from Thermal Images Using Convolutional Neural Network(Springer Science and Business Media Deutschland GmbH, 2022) Srivastava, D.K.; Pawan, S.J.; Rajan, J.The world has seen the disastrous effect caused by COVID-19 on humankind. The rapidity of COVID-19 transmission, re-infections, post-COVID-19 symptoms, and the emergence of new COVID-19 strands have disrupted the global healthcare systems. Consequently, screening for COVID-19 cases has become of the utmost importance. As temperature and mask checks help significantly to prevent the rapid spread of COVID-19, automating this process in public places has become indispensable. In this work, we propose an end-to-end approach for mask detection followed by temperature for efficient screening. The proposed model achieved 93.5%, 96.7%, and 97.7% precision, recall, and mAP when trained on the thermal surveillance dataset and tested on a lightning dataset consisting of images with varying intensities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Seasonal and Lockdown Effects on Air Quality in Metro Cities in India(Springer Science and Business Media Deutschland GmbH, 2023) Krishna Raj, K.; Shrihari, S.Air pollution is one of the worst avoidable threats in developing nations across the world. India has undergone a substantial number of infrastructure changes during recent years due to the ever-increasing population. This and the consequent industrialization, the air quality of Indian cities became worsened. The changes in climatic conditions across various cities in India also contribute to air pollution. To control the air pollution within the acceptable limit several control measures have been imposed in India, despite these efforts the air pollution level has not decreased considerably. In India, the first COVID-19 case has reported on 30th January 2020 in the state of Kerala. To control the quick spread of COVID-19 in India, the central government executed a three-week nationwide lockdown from 24th March 2020, and further, it has extended into several phases. It was the first time in India a long-term shutting down of all the sectors happening and which resulted in positively on the environment. This study is dealing with the lockdown effect on air quality in metro cities in India and is compared with the pre-existing conditions. Also, the seasonal variations in air quality in the course of the past two years are compared. The data of pollutants PM10, PM2.5, SO2, NO2, O3, CO, and NH3 from metro cities were collected and by adopting the National Air Quality Index to depict the variations in overall air quality. During the lockdown period, most of the cities experience a considerable improvement in overall air quality and PM10, NO2, PM2.5, and CO concentrations. Whereas, the Ozone shows some increasing trend in a few cities might be due to the increment in the temperature caused by the exposure of sun during the summer season. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Trustworthiness of COVID-19 News and Guidelines(Springer, 2023) Singh, S.; Nagar, L.; Lal, A.; Chandavarkar, B.R.COVID-19 pandemic is a serious health concern issue over the past couple of years. It spreads mostly due to bio-contacts, which leads people to follow social distancing and stay away from social gatherings. It leads the people to bound themselves to stay with their family members at their home only, being at home, staying idle, or following work from home schedule by working online through the Internet over the electronic gadgets such as mobiles, laptops, desktops, etc. It leads the people to attach to online activities more for spending their time at their home, which enormously increases people interest in social media platforms such as Twitter, Facebook, etc. As it was a major pandemic period, it created panic and a fearful situation in society. It makes the people believe any news and guidelines spreading through social media platforms irrespective of checking their trustworthiness and truthiness of it. This pandemic period created a seriously bad impact on society’s emotional, physical, and mental health that is a great loss to a country even all over the world. Under this, many unwanted messages are spreading for one’s interest or a group to polarize their interest. In a panic situation, it is highly required of a solution that prevents the spread of these negative vibes to maintain the overall health of society. This chapter tries to implement an optimal solution using various kinds of layers and different optimization functions. It particularly gives better performance in the case of sequential data using machine learning (ML) and deep learning (DL) frameworks trained with the dataset for identifying the fake news and guidelines spread over on COVID-19. To train the model, a dataset was taken from the Twitter Application Programming Interface (API). Finally, the truthiness detection technique with social interaction is completed using Twitter dataset. The efficacy of the suggested method is demonstrated by the obtained results on a Twitter dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.