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Author

R. Bhargavi

Bio: R. Bhargavi is an academic researcher from Thiagarajar College of Engineering. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Proceedings ArticleDOI
28 Sep 2020
TL;DR: For the spatial hazard modeling of PM10 productive machine learning models such as Mixture Discriminant Analysis (MDA), Bagged Classification and Regression Trees (Bagged CART), Random Forest (RF), with accuracy 0.87, 0.92 and 0.93 respectively are used, however, these models cannot give accurate results with large samples and to overcome this eXtreme Gradient Boosting (XGBoost) method is used.
Abstract: Air pollution is one of the major threats to human health and environment. When some substances in the atmosphere exceed a certain concentration it becomes harmful to the ecological system and the normal conditions of human existence. Particulate matter (PM) refers to small solid or liquid particles floating in the air. These small particles can move deeper into the respiratory tract, including the lungs this can lead to cough, asthma attacks, high blood pressure, heart attack, stroke and so on. Particulate matter is considered as the air pollutant of greatest concern to health. So as a first step to understand the seriousness of the issue is to monitor PM concentration. For the spatial hazard modeling of PM10 productive machine learning models such as Mixture Discriminant Analysis (MDA), Bagged Classification and Regression Trees (Bagged CART), Random Forest (RF), with accuracy 0.87, 0.92 and 0.93 respectively are used. However, these models cannot give accurate results with large samples and to overcome this eXtreme Gradient Boosting (XGBoost) method is used.

2 citations


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Journal ArticleDOI
TL;DR: In this paper, the authors used remote sensing data such as elevation, slope, road density, Soil Adjusted Vegetation Index, Normalized difference Vegetation index, built-up index, land surface temperature, and wind speed.

16 citations

Proceedings ArticleDOI
19 Aug 2022
TL;DR: In this paper , the authors applied the XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms.
Abstract: Discovering the source of a patient's fever without clinically localised signs can be a daunting task for doctors. In particular for leukaemia patients with fever of unknown origin, fast discovering the source of the fever is a formidable challenge, as this population has the potential to lead to fever in many different situations. In this paper, we applied XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms. Our results illustrates that those machine learning algorithms achieves good performance. In particular, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.8376 and F1-score of 0.7034. Compared with existing literature, our experiment provides new insights for doctors to determine the cause of fever in leukemia patients.

1 citations