A Deep Learning-Based Prediction and Simulator of Harmful Air Pollutants: A Case from the Philippines

Macatangay, Lloyd H.; Hernandez, Rowell M.

doi:10.1109/icsgrc49013.2020.9232528

Cited by 14 publications

(3 citation statements)

References 30 publications

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“…Machine learning methods assist data in improving the performance of variable classification. It was also demonstrated that in comparison to traditional parametric classifiers, machine learning has a higher accuracy result when dealing with complex data with a high dimensional feature [8] [9]. However, machine learning methods are not frequently employed these days due to several ambiguities about how to integrate and apply machine learning techniques efficiently.…”

Section: Introductionmentioning

confidence: 99%

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Lawas

2023

ijarcs

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People's health is important. It must be preserved. There are a variety of issues that might contribute to a person's health, including their lifestyle. One of the most prominent concerns during this pandemic is a person's body weight, which is particularly significant these days. Obesity is one of those body diseases about which it is important to be aware of the reasons. This study intends to create different machine learning models to define what causes obesity, select the most appropriate model that did the best, and discuss how accurate it performed. It can also be used in determining the how the obesity impact in our daily lives. Through the use of different machine learning models such as KNN, Random Forest, Gradient Boosting and Ada Boost, the study be able to obtain the appropriate model. Despite being trained on unbalanced data, the classifiers utilized were able to predict the properties of the presented datasets that Random Forest has an accuracy of 83.6%.

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Section: Introductionmentioning

confidence: 99%

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Lawas

2023

ijarcs

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“…In Manila, the average human exposure to PM2.5 is 17 x 10 -9 kg•m -3 , exceeding the 10 x 10 -9 kg•m -3 standard limit for PM2.5-exposure by the World Health Organization (WHO) [4]. The Philippines is among the top ten countries globally with the highest death burden due to air pollution which was estimated to account for 64,000 deaths in 2019 [5][6].…”

Section: Introductionmentioning

confidence: 99%

Genotoxicity of PM2.5 and PM1.0 Particulates on Human Peripheral Blood Lymphocytes in Manila, Philippines

Mkg¹,

Ja²,

Cj³

et al. 2021

Preprint

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Urban air quality is increasingly being studied as a fraction of the world's population is now living in megacities. In this study, particulate matter (PM) along Taft Avenue, Manila, Philippines, is investigated in terms of its ability to induce genetic damage on human peripheral blood lymphocytes (PBL). Size-segregated roadside air samples were obtained from 2015-2016 near the university gate and analyzed using in vitro micronucleus and cytokinesis-block proliferation tests. While cellular proliferation was unaffected by 0 – 0.1 kg·m-3 of PM1.0 and PM2.5, PBL cells treated with PM2.5 displayed significantly higher micronucleus count (p = 0.03) compared to the cells treated with PM1.0. Atomic Absorption Spectroscopy revealed greater amounts of Cd, Ca, Pb, K, Na, and Zn in PM2.5 compared to PM1.0. The results indicate the differences in composition of the two size fractions of air particulates are associated with their genotoxicities.

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“…In Manila, the average human exposure to PM 2.5 is 17 × 10 −9 kg/m 3 , exceeding the 5 × 10 −9 kg/m 3 standard limit for PM 2.5 exposure by the World Health Organization (WHO) [4]. The Philippines is among the top ten countries globally with the highest death burden due to air pollution which was estimated to account for 64,000 deaths in 2019 [5,6].…”

Section: Introductionmentioning

confidence: 99%

Genotoxicity of PM2.5 and PM1.0 Particulates on Human Peripheral Blood Lymphocytes in Manila, Philippines

et al. 2021

View full text Add to dashboard Cite

Urban air quality is increasingly being studied as a fraction of the world’s population is living in megacities. In this study, particulate matter (PM) along Taft Avenue, Manila, the Philippines, is investigated in terms of its ability to induce genetic damage in human peripheral blood lymphocytes (PBLs). Size-segregated roadside air samples were obtained from 2015–2017 near a university gate and analyzed using in vitro micronucleus (MN) and cytokinesis-block proliferation tests. While cellular proliferation was unaffected by 0–0.1 kg/m3 of PM1.0 and PM2.5, PBL cells treated with PM2.5 displayed a significantly higher micronucleus count (p = 0.03) compared to the cells treated with PM1.0. Atomic absorption spectroscopy revealed greater amounts of Cd, Ca, Pb, K, Na, and Zn in PM2.5 compared to PM1.0. The results indicate that the differences in composition of the two size fractions of air particulates are associated with their genotoxicities.

show abstract

A Deep Learning-Based Prediction and Simulator of Harmful Air Pollutants: A Case from the Philippines

Cited by 14 publications

References 30 publications

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Application of Multiple Machine Learning Techniques in Classifying Obesity Level Using Multivariate Dataset

Genotoxicity of PM2.5 and PM1.0 Particulates on Human Peripheral Blood Lymphocytes in Manila, Philippines

Genotoxicity of PM2.5 and PM1.0 Particulates on Human Peripheral Blood Lymphocytes in Manila, Philippines

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