Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Classifying behavioural traits of small-scale farmers: Use of a novel artificial neural network (ANN) classifier(Institute of Electrical and Electronics Engineers Inc., 2016) Jena, P.R.; Majhi, R.This paper develops and employs a novel artificial neural network (ANN) model to study farmers' behaviour towards decision making on maize production in Kenya. The paper has compared the accuracy level of ANN based model and the statistical model and found out that the ANN model has achieved higher accuracy and efficiency. The findings from the study reveal that the farmers are mostly influenced by their demographic and food security for decision making. Further to examine the relative importance of different demographic and food security characteristics, an ANOVA test is undertaken. The results found that education and food security indices are instrumental in influencing farmers' decision making. © 2016 IEEE.Item QSAR Classification Models for Predicting 3CLPro-protease Inhibitor Activity(Institute of Electrical and Electronics Engineers Inc., 2021) Mondal, K.; Kamath S․, S.The ongoing COVID-19 pandemic achieved a worldwide scale rapidly and has caused devastating casualties in terms of both human lives and in damage to the world economy. Several efforts for designing drugs and vaccines are underway across the globe. One of the potential early breakthroughs resulted due to the potential for repurposing existing drugs for COVID-19, specifically by drug modeling using computing power availability. Prediction of inhibition activity is a major step in such computation based drug discovery process. It is one of the virtual screening processes that throws light on particular molecules that may potential drug candidates. The subsequent stages in drug discovery are highly resource-intensive, during which a streamlined analysis of potential candidates can help in optimal design. Thus, the problem of predicting inhibition activity of compounds on proteins has attracted significant research interest. In this paper, an approach that employs quantitative structure-activity relationship (QSAR) modelling of SARS-CoV-3CLpro enzyme inhibitors for the development of activity classification model is proposed. The classification models predict SAR-CoV-3CLpro inhibitory activity for query compounds in the screening process using labels. Moreover, molecular docking analysis is performed using 3 FDA approved drugs that are being used as repurposed drugs for COVID-19 treatment. The best performing model with docking data (RMSD and Binding energy) of these 3 drugs were validated and the results obtained were promising. © 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 Citation Intent Classification Using Transformers(Institute of Electrical and Electronics Engineers Inc., 2024) Rakshith Gowda, H.C.; Raj, K.S.; Anand Kumar, M.As the world of scholarly research continues to grow, the intricate network of citations serves as the foundation of academic discussion, symbolizing the interweaving of concepts and the dissemination of information. The study of citations in scientific literature is important for discovering new knowledge, retrieving information, and analyzing discourse. However, manually categorizing citation functions is a slow and biased process. To address this, we conducted research on automated citation function classification in astrophysics literature by creating and evaluating deep learning models. We also introduce the FOCAL dataset, which stands for Functions of Citations in Astrophysics Literature, includes astrophysics articles with manually labelled citation functions. Our approach uses language features, citation contexts, and domain knowledge to classify citation functions. Results show that our method accurately identifies citation functions, indicating its potential for improving citation analysis. © 2024 IEEE.