Selected Malaysia air quality pollutants assessment using chemometrics techniques

Rani, Nipunika; Azid, Azman; Khalit, Saiful Iskandar; Gasim, Muhammad Barzani; Juahir, Hafizan

doi:10.4314/jfas.v9i2s.23

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…The ANN capabilities are by perceiving the example of the intricate, fundamental and multi-dimensionality information with does not depend on any assumption before preparing the relationship among factors, including the non-parametric information (Amran et al 2015). The ANN works as three basic types of layers; the input layer, hidden layer(s), and output layer (Mutalib et al 2013;Yusof et al 2018;Rani et al 2018). As mimicking the human brain system, the results from output layer are transmitted by hidden nodes from input layer (Bandyopadhyay & Chattopadhyay, 2007).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Yusof

Azid

Sani³

et al. 2019

Mal. J. Fund. Appl. Sci.

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The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Yusof

Azid

Sani³

et al. 2019

Mal. J. Fund. Appl. Sci.

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“…Debt swap funding has been used by constructing an index using the Keyser Meyer Olkin method. A measure of accuracy in a statistical method of Principal Component Analysis(Rani et al, 2018;Ul Hadia et al, 2016).…”

mentioning

confidence: 99%

Debt Swap Funding Nexus Education Attainment in the Presence of Per Capita Income in Debt Burdened Economies: An Empirical Analysis Using Two Step SYS-GMM

Ahmad,

Safdar,

Azam

2023

J. educ. soc. stud.

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Debt swap funding is one of the important catalysts for education attainment in debt-burdened economies. It enhances the education status by providing extra fiscal space for the government to invest resources for quality education, increasing capital inflow, and reducing the debt burden in the recipient countries. The main purpose of the study is to investigate the impact of debt swap funding on education attainment in debt-burdened economies for the period 2003-2021. The relationship between debt swap funding and education attainment is analyzed in the presence of per capita income of the respective countries by using the advanced methodology Two Step System GMM. Other variables, which are the main determinants of debt swap funding, like IMF structural adjustment program and debt service, are used as control variables. Education attainment is also dependent on per capita income included in the model, along with growth parameters of new growth theory (technological growth, population growth, and depreciation rate), which are part of the control variables. The results of the study show a positive and statistically significant relation between debt swap funding and education attainment in debt-burdened economies in the presence of per capita income. Policy recommendations are proposed in light of the results obtained regarding the debt swap funding nexus education attainment.

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Identifying Missing Data Mechanisms Among Incomplete Air Pollution Datasets in Malaysia

Libasin,

Ul-Saufie,

Ahmat

et al. 2024

Advances in Science, Technology &Amp; Innovation

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Selected Malaysia air quality pollutants assessment using chemometrics techniques

Cited by 3 publications

References 27 publications

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Debt Swap Funding Nexus Education Attainment in the Presence of Per Capita Income in Debt Burdened Economies: An Empirical Analysis Using Two Step SYS-GMM

Identifying Missing Data Mechanisms Among Incomplete Air Pollution Datasets in Malaysia

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