Kemal Maulana Alhasa

National Research and Innovation Agency

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Wayan Suparta

Universitas Teknologi Yogyakarta

Mohd Shahrul Mohd Nadzir

National University of Malaysia

Mohd Talib Latif

National University of Malaysia

Haris Hafizal Abd Hamid

National University of Malaysia

Mandeep Singh Jit Singh

National University of Malaysia

Murnira Othman

National University of Malaysia

Noratiqah Mohd Ariff

National University of Malaysia

Zetti Zainol Rashid

University Kebangsaan Malaysia Medical Centre

Md Firoz Khan

North South University

Norlaila Mustafa

National University of Malaysia

Cooperative Institutions

National University of Malaysia

University of Malaya

University Kebangsaan Malaysia Medical Centre

National Space Agency

National Institutes of Biotechnology Malaysia

University of Cambridge

Universiti Malaysia Terengganu

University of Kuala Lumpur

University of Technology Malaysia

Universiti Sains Malaysia

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IoT-Based Indoor Air Quality Monitoring System Using SAMD21 ARM Cortex Processor

Advanced structured materials (2022)

Zaki Khaslan Noor Hidayah Mohd Yunus Mohd Shahrul Mohd Nadzir Jahariah Sampe Noorazlina Mohamad Salih

ARM architecture

Laptop

Stove

10.1007/978-3-030-92964-0_24

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Citations (4)

Prototype Design of Smart Greenhouse Gases Systems for Environmental Monitoring in Peatland Areas

Journal of Engineering Science and Technology Review (2022)

Wayan Suparta Kemal Maulana Alhasa

Monitoring atmospheric greenhouse gases emissions in peatlands are increasingly important to anticipate the causes of fires and their impact on the environment.This study has designed a prototype GHG monitoring system for tropical peatlands using Netduino 3 Wi-Fi and is connected to meteorological sensors.The application systems developed with C# can access measurements remotely through the Internet of Things platform.The smart monitoring system has the advantage to measure new parameters such as precipitable water vapor and water vapor concentration rather than originally only measuring surface pressure, temperature, and relative humidity.It also can send measurement results and store data to micro secure digital and two web servers (Google Form and Dropbox).In the first stage, the prototype was tested in two conditions: inside and outside the laboratory before being installed on peatlands.The result of the measurement system has also been compared with CO2 levels and CH4 levels measured from NDIR gas sensors.

10.25103/jestr.151.05

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Citations (1)

Modeling of zenith path delay over Antarctica using an adaptive neuro fuzzy inference system technique

Expert Systems with Applications (2014)

Wayan Suparta Kemal Maulana Alhasa

10.1016/j.eswa.2014.09.029

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Citations (29)

Prediction of ZTD Based on ANFIS Model

SpringerBriefs in meteorology (2015)

Wayan Suparta Kemal Maulana Alhasa

Fuzzy inference system

10.1007/978-3-319-28437-8_5

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Citations (0)

Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System

Sensors (2018)

Kemal Maulana Alhasa Mohd Shahrul Mohd Nadzir Olalekan Popoola Mohd Talib Latif Yusri Yusup

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

Multilayer perceptron

10.3390/s18124380

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Citations (36)

Introduction

SpringerBriefs in meteorology (2015)

Wayan Suparta Kemal Maulana Alhasa

Zenith

10.1007/978-3-319-28437-8_1

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Citations (1)

Modeling of Precipitable Water Vapor Using an Adaptive Neuro-Fuzzy Inference System in the Absence of the GPS Network

Journal of Applied Meteorology and Climatology (2016)

Wayan Suparta Kemal Maulana Alhasa

Abstract This paper constructs an adaptive neuro-fuzzy inference system (ANFIS) model to estimate precipitable water vapor (PWV) in Southeast Asia, particularly in the Peninsular Malaysia, Sabah, and Singapore region. The input to the model is developed using the surface pressure, temperature, and relative humidity. The models are trained and tested using PWV values derived from the global positioning system (GPS). The data used are for May 2012 taken at the Nanyang Technology University of Singapore (NTUS) and Universiti Malaysia Sabah, Kinabalu (UMSK); and for February 2009 taken at the Universiti Kebangsaan Malaysia Bangi (UKMB). The validation process is conducted using June 2012 data for NTUS and UMSK and March 2009 data for UKMB. The performance the ANFIS model is compared with a multilayer perceptron (MLP), Elman neural networks, and multiple linear regression (MLR) models. Results from validations at the three stations showed that the ANFIS model performed well as compared with MLP, Elman neural networks, and MLR, with a mean absolute error of 0.015 mm, a percent error of 0.028%, and root-mean-square error of 0.019 mm. These results suggest that the ANFIS model is a promising approach for estimating PWV values that is cost effective, continuous, and potentially useful for meteorological applications.

Multilayer perceptron

Precipitable water

10.1175/jamc-d-15-0161.1

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Citations (14)

Cloud-to-Ground lightning observations over the Western Antarctic region

Polar Science (2019)

Norbayah Yusop Mohd Riduan Ahmad Mardina Abdullah Siti Khalijah Zainudin Wan Nur Arina Wan Mohd Nor

Lightning detection

Peninsula

10.1016/j.polar.2019.05.002

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Citations (8)

Particulate matter (PM2.5) as a potential SARS-CoV-2 carrier

Scientific Reports (2021)

Norefrina Shafinaz Md Nor Chee Wai Yip Nazlina Ibrahim Mohd Hasni Jaáfar Zetti Zainol Rashid

Abstract The rapid spread of the SARS-CoV-2 in the COVID-19 pandemic had raised questions on the route of transmission of this disease. Initial understanding was that transmission originated from respiratory droplets from an infected host to a susceptible host. However, indirect contact transmission of viable virus by fomites and through aerosols has also been suggested. Herein, we report the involvement of fine indoor air particulates with a diameter of ≤ 2.5 µm (PM 2.5 ) as the virus’s transport agent. PM 2.5 was collected over four weeks during 48-h measurement intervals in four separate hospital wards containing different infected clusters in a teaching hospital in Kuala Lumpur, Malaysia. Our results indicated the highest SARS-CoV-2 RNA on PM 2.5 in the ward with number of occupants. We suggest a link between the virus-laden PM 2.5 and the ward’s design. Patients’ symptoms and numbers influence the number of airborne SARS-CoV-2 RNA with PM 2.5 in an enclosed environment.

Airborne Transmission

Pandemic

2019-20 coronavirus outbreak

Sars virus

10.1038/s41598-021-81935-9

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Citations (120)

The development of PWV index for air pollution concentration detection in Banting, Malaysia

Wayan Suparta Kemal Maulana Alhasa Mandeep Singh Jit Singh Mohd Talib Latif

This study is aimed to clarify the ability of precipitable water vapor (PWV) index variability in detecting the air pollutants concentration in Banting, Selangor. For that purpose, we have developed the PWV index using the fuzzy clustering (Fuzzy C-Means (FCM)). There are five categories of PWV index, namely "Very Low", "Low"," Moderate", "High" and "Very High". Correlation analysis was performed on a daily averaged for five periods which were yearly, April to December, northeast monsoon (November-December), southwest monsoon (June to September) and two transition period (April-May) and October. From the preliminary results, we found that the concentrations of air pollutions have a small impact towards PWV index during the northeast monsoon and were not correlated during southwest monsoon. For the yearly period, the SO 2 and O 3 concentrations have a small impact towards PWV index and are not affected by the other concentrations. During the haze episode, the concentrations of air pollution found increased while the PWV index was decreased. Overall, the concentrations of air pollutants give a small impact to the variability of PWV index.

Air Pollution Index

10.1109/iconspace.2015.7283810

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Citations (3)