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Ammar Zakaria
Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia

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Journal article
Published: 21 July 2021 in Sensors
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This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

ACS Style

Chew Goh; Latifah Kamarudin; Ammar Zakaria; Hiromitsu Nishizaki; Nuraminah Ramli; Xiaoyang Mao; Syed Syed Zakaria; Ericson Kanagaraj; Abdul Abdull Sukor; Fauzan Elham. Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm. Sensors 2021, 21, 4956 .

AMA Style

Chew Goh, Latifah Kamarudin, Ammar Zakaria, Hiromitsu Nishizaki, Nuraminah Ramli, Xiaoyang Mao, Syed Syed Zakaria, Ericson Kanagaraj, Abdul Abdull Sukor, Fauzan Elham. Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm. Sensors. 2021; 21 (15):4956.

Chicago/Turabian Style

Chew Goh; Latifah Kamarudin; Ammar Zakaria; Hiromitsu Nishizaki; Nuraminah Ramli; Xiaoyang Mao; Syed Syed Zakaria; Ericson Kanagaraj; Abdul Abdull Sukor; Fauzan Elham. 2021. "Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm." Sensors 21, no. 15: 4956.

Journal article
Published: 26 May 2021 in Sensors
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This manuscript presents a new method to monitor and localize the moisture distribution in a rice silo based on tomography images. Because the rice grain is naturally hygroscopic, the stored grains’ quality depends on their level of moisture content. Higher moisture content leads to fibre degradation, making the grains too frail and possibly milled. If the moisture is too low, the grains become brittle and are susceptible to higher breakage. At present, the single-point measurement method is unreliable because the moisture build-up inside the silo might be distributed unevenly. In addition, this method mostly applies gravimetric analysis, which is destructive. Thus, we proposed a radio tomographic imaging (RTI) system to address these problems. Four simulated phantom profiles at different percentages of moisture content were reconstructed using Newton’s One-Step Error Reconstruction and Tikhonov Regularization algorithms. This simulation study utilized the relationship between the maximum voxel weighting of the reconstructed RTI image and the percentage of moisture content. The outcomes demonstrated promising results, in which the weighting voxel linearly increased with the percentage of moisture content, with a correlation coefficient higher than 0.95 was obtained. Therefore, the results support the possibility of using the RTI approach for monitoring and localizing the moisture distribution inside the rice silo.

ACS Style

Nurul Mohd Ramli; Mohd Fazalul Rahiman; Latifah Kamarudin; Latifah Mohamed; Ammar Zakaria; Anita Ahmad; Ruzairi Rahim. A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images. Sensors 2021, 21, 3686 .

AMA Style

Nurul Mohd Ramli, Mohd Fazalul Rahiman, Latifah Kamarudin, Latifah Mohamed, Ammar Zakaria, Anita Ahmad, Ruzairi Rahim. A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images. Sensors. 2021; 21 (11):3686.

Chicago/Turabian Style

Nurul Mohd Ramli; Mohd Fazalul Rahiman; Latifah Kamarudin; Latifah Mohamed; Ammar Zakaria; Anita Ahmad; Ruzairi Rahim. 2021. "A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images." Sensors 21, no. 11: 3686.

Journal article
Published: 08 March 2021 in Sensors
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Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

ACS Style

Noraini Azmi; Latifah Kamarudin; Ammar Zakaria; David Ndzi; Mohd Rahiman; Syed Zakaria; Latifah Mohamed. RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques. Sensors 2021, 21, 1875 .

AMA Style

Noraini Azmi, Latifah Kamarudin, Ammar Zakaria, David Ndzi, Mohd Rahiman, Syed Zakaria, Latifah Mohamed. RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques. Sensors. 2021; 21 (5):1875.

Chicago/Turabian Style

Noraini Azmi; Latifah Kamarudin; Ammar Zakaria; David Ndzi; Mohd Rahiman; Syed Zakaria; Latifah Mohamed. 2021. "RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques." Sensors 21, no. 5: 1875.

Journal article
Published: 01 June 2020 in Smart Cities
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Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

ACS Style

Abdul Syafiq Abdull Sukor; Latifah Munirah Kamarudin; Ammar Zakaria; Norasmadi Abdul Rahim; Sukhairi Sudin; Hiromitsu Nishizaki. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities 2020, 3, 444 -455.

AMA Style

Abdul Syafiq Abdull Sukor, Latifah Munirah Kamarudin, Ammar Zakaria, Norasmadi Abdul Rahim, Sukhairi Sudin, Hiromitsu Nishizaki. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities. 2020; 3 (2):444-455.

Chicago/Turabian Style

Abdul Syafiq Abdull Sukor; Latifah Munirah Kamarudin; Ammar Zakaria; Norasmadi Abdul Rahim; Sukhairi Sudin; Hiromitsu Nishizaki. 2020. "RSSI-Based for Device-Free Localization Using Deep Learning Technique." Smart Cities 3, no. 2: 444-455.

Full paper
Published: 07 April 2020 in Advanced Robotics
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Mobile robot carrying gas sensors have been widely used in mobile olfaction applications. One of the challenging tasks in this research field is Gas Distribution Mapping (GDM). GDM is a representation of how volatile organic compound is spatially dispersed within an environment. This paper addresses the effect of obstacles towards GDM for indoor environment. This work proposes a solution by improvising the Kernel DM + V technique using propagated distance transform (DT) as the weighing function. Since DT computations are CPU heavy, parallel computing, using Compute Unified Device Architecture (CUDA) available in Graphics Processing Unit (GPU), is used to accelerate the DT computation. The proposed solution is compared with the Kernel DM + V algorithm, presenting that the proposed method drastically improves the quality of GDM under various kernel sizes. The study is also further extended towards the effect of obstacles on gas source localization task. The outcome of this work proves that the proposed method shows better accuracy for GDM estimation and gas source localization if obstacle information is considered.

ACS Style

Retnam Visvanathan; Kamarulzaman Kamarudin; Syed Muhammad Mamduh; Masahiro Toyoura; Ahmad Shakaff Ali Yeon; Ammar Zakaria; Latifah Munirah Kamarudin; Xiaoyang Mao; Shazmin Aniza Abdul Shukor. Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment. Advanced Robotics 2020, 34, 637 -647.

AMA Style

Retnam Visvanathan, Kamarulzaman Kamarudin, Syed Muhammad Mamduh, Masahiro Toyoura, Ahmad Shakaff Ali Yeon, Ammar Zakaria, Latifah Munirah Kamarudin, Xiaoyang Mao, Shazmin Aniza Abdul Shukor. Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment. Advanced Robotics. 2020; 34 (10):637-647.

Chicago/Turabian Style

Retnam Visvanathan; Kamarulzaman Kamarudin; Syed Muhammad Mamduh; Masahiro Toyoura; Ahmad Shakaff Ali Yeon; Ammar Zakaria; Latifah Munirah Kamarudin; Xiaoyang Mao; Shazmin Aniza Abdul Shukor. 2020. "Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment." Advanced Robotics 34, no. 10: 637-647.

Conference paper
Published: 02 December 2019 in IOP Conference Series: Materials Science and Engineering
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The implementation of intelligent transportation systems, or what is otherwise referred to as IoT and big data analytics in transportation system are affected by several factors. Considering the overall impact of a definite and comprehensive plan that involves business, infrastructure, and organizational administration, improved customers satisfaction can better be achieved when all these antecedent factors are investigated, and their elements are determined. There is no recorded study on multidimensional implementation model where more than one of them is considered. This paper, therefore, stems from the general background of implementation success factors but aims to discover the generality of the definite plan and procedure that must be executed for a successful implementation of big data analytics and IoT-oriented transportation system. This paper employs a systematic literature review and an in-depth interview to address the obvious limitation. This paper also discussed the qualitative approach that was used to gather data on the successful implementation factors in the transportation system. The deliverable from this research will be the implementation success factors for the big data analytics and IoT-oriented transportation system.

ACS Style

W N Hussein; L M Kamarudin; H N. Hussain; N A Ishak; A. Zakaria; Khalid Jadaa. Discovering the Implementation Success Factors for IoT and Big Data Analytics in Transportation System. IOP Conference Series: Materials Science and Engineering 2019, 705, 012049 .

AMA Style

W N Hussein, L M Kamarudin, H N. Hussain, N A Ishak, A. Zakaria, Khalid Jadaa. Discovering the Implementation Success Factors for IoT and Big Data Analytics in Transportation System. IOP Conference Series: Materials Science and Engineering. 2019; 705 (1):012049.

Chicago/Turabian Style

W N Hussein; L M Kamarudin; H N. Hussain; N A Ishak; A. Zakaria; Khalid Jadaa. 2019. "Discovering the Implementation Success Factors for IoT and Big Data Analytics in Transportation System." IOP Conference Series: Materials Science and Engineering 705, no. 1: 012049.

Journal article
Published: 14 May 2019 in Journal of Intelligent & Fuzzy Systems
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ACS Style

Abdul Syafiq Abdull Sukor; Ammar Zakaria; Norasmadi Abdul Rahim; Latifah Munirah Kamarudin; Rossi Setchi; Hiromitsu Nishizaki. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes. Journal of Intelligent & Fuzzy Systems 2019, 36, 4177 -4188.

AMA Style

Abdul Syafiq Abdull Sukor, Ammar Zakaria, Norasmadi Abdul Rahim, Latifah Munirah Kamarudin, Rossi Setchi, Hiromitsu Nishizaki. A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes. Journal of Intelligent & Fuzzy Systems. 2019; 36 (5):4177-4188.

Chicago/Turabian Style

Abdul Syafiq Abdull Sukor; Ammar Zakaria; Norasmadi Abdul Rahim; Latifah Munirah Kamarudin; Rossi Setchi; Hiromitsu Nishizaki. 2019. "A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes." Journal of Intelligent & Fuzzy Systems 36, no. 5: 4177-4188.

Journal article
Published: 10 April 2019 in Journal of Intelligent & Fuzzy Systems
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ACS Style

Boon Pin Ooi; Norasmadi Abdul Rahim; Ammar Zakaria; Maz Jamilah Masnan; Shazmin Aniza Abdul Shukor. Random subspace oracle (RSO) ensemble to solve small sample-sized classification problems. Journal of Intelligent & Fuzzy Systems 2019, 36, 3225 -3234.

AMA Style

Boon Pin Ooi, Norasmadi Abdul Rahim, Ammar Zakaria, Maz Jamilah Masnan, Shazmin Aniza Abdul Shukor. Random subspace oracle (RSO) ensemble to solve small sample-sized classification problems. Journal of Intelligent & Fuzzy Systems. 2019; 36 (4):3225-3234.

Chicago/Turabian Style

Boon Pin Ooi; Norasmadi Abdul Rahim; Ammar Zakaria; Maz Jamilah Masnan; Shazmin Aniza Abdul Shukor. 2019. "Random subspace oracle (RSO) ensemble to solve small sample-sized classification problems." Journal of Intelligent & Fuzzy Systems 36, no. 4: 3225-3234.

Journal article
Published: 03 September 2018 in International Journal of Performance Analysis in Sport
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ACS Style

Sukhairi Sudin; Ali Yeon Md Shakaff; Ammar Zakaria; Ahmad Faizal Salleh; Latifah Munirah Kamarudin; Noraini Azmi; Fathinul Syahir Ahmad Saad. Real-time track cycling performance prediction using ANFIS system. International Journal of Performance Analysis in Sport 2018, 18, 1 -17.

AMA Style

Sukhairi Sudin, Ali Yeon Md Shakaff, Ammar Zakaria, Ahmad Faizal Salleh, Latifah Munirah Kamarudin, Noraini Azmi, Fathinul Syahir Ahmad Saad. Real-time track cycling performance prediction using ANFIS system. International Journal of Performance Analysis in Sport. 2018; 18 (5):1-17.

Chicago/Turabian Style

Sukhairi Sudin; Ali Yeon Md Shakaff; Ammar Zakaria; Ahmad Faizal Salleh; Latifah Munirah Kamarudin; Noraini Azmi; Fathinul Syahir Ahmad Saad. 2018. "Real-time track cycling performance prediction using ANFIS system." International Journal of Performance Analysis in Sport 18, no. 5: 1-17.

Journal article
Published: 02 September 2018 in Advanced Robotics
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ACS Style

Kamarulzaman Kamarudin; Ali Yeon Md Shakaff; Victor Hernandez Bennetts; Syed Muhammad Mamduh; Ammar Zakaria; Retnam Visvanathan; Ahmad Shakaff Ali Yeon; Latifah Munirah Kamarudin. Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization. Advanced Robotics 2018, 32, 903 -917.

AMA Style

Kamarulzaman Kamarudin, Ali Yeon Md Shakaff, Victor Hernandez Bennetts, Syed Muhammad Mamduh, Ammar Zakaria, Retnam Visvanathan, Ahmad Shakaff Ali Yeon, Latifah Munirah Kamarudin. Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization. Advanced Robotics. 2018; 32 (17):903-917.

Chicago/Turabian Style

Kamarulzaman Kamarudin; Ali Yeon Md Shakaff; Victor Hernandez Bennetts; Syed Muhammad Mamduh; Ammar Zakaria; Retnam Visvanathan; Ahmad Shakaff Ali Yeon; Latifah Munirah Kamarudin. 2018. "Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization." Advanced Robotics 32, no. 17: 903-917.

Conference paper
Published: 01 August 2018 in 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA)
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3D model of human body has been widely used in many applications including medical, health and security, since it is able to provide information on human body shape. This paper proposes a method to identify human based on the 3D model of the body and the depth data form the Kinect. The system firstly utilizes the coordinate points from the 3D model to calculate the selected anthropometry features of human body. Then, the features are compared with real time Kinect's depth acquisition to perform pose recognition and human identification. Eight candidates were involved in the reliability test of the system with each of them performed 6 trials, making a total of 48 trials. The overall reliability of the system in identifying the correct candidate was found to be 79.167%.

ACS Style

Chai Joon Lip; Ahmad Shakaff Ali Yeon; Latifah Munirah Kamarudin; Kamarulzaman Kamarudin; Retnam Visvanathan; Ahmad Firdaus Ahmad Zaidi; Syed Muhammad Mamduh; Ammar Zakaria; Wan Mohd Nooriman. Human 3D Reconstruction and Identification Using Kinect Sensor. 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) 2018, 1 -7.

AMA Style

Chai Joon Lip, Ahmad Shakaff Ali Yeon, Latifah Munirah Kamarudin, Kamarulzaman Kamarudin, Retnam Visvanathan, Ahmad Firdaus Ahmad Zaidi, Syed Muhammad Mamduh, Ammar Zakaria, Wan Mohd Nooriman. Human 3D Reconstruction and Identification Using Kinect Sensor. 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA). 2018; ():1-7.

Chicago/Turabian Style

Chai Joon Lip; Ahmad Shakaff Ali Yeon; Latifah Munirah Kamarudin; Kamarulzaman Kamarudin; Retnam Visvanathan; Ahmad Firdaus Ahmad Zaidi; Syed Muhammad Mamduh; Ammar Zakaria; Wan Mohd Nooriman. 2018. "Human 3D Reconstruction and Identification Using Kinect Sensor." 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) , no. : 1-7.

Conference paper
Published: 01 August 2018 in 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA)
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Mapping is one of the most important aspect in robot navigation. This project emphasizes on the mapping using Simultaneous Localization and Mapping (SLAM) using GMapping approach. Turtlebot equipped with Hokuyo Laser Range Finder (LRF) URG-04LX-UG01 is used for the mapping. The SLAM operations were done in there different locations in UniMAP, which are either indoor or mixed of indoor-outdoor environment. The results show that the indoor maps obtained are more accurate than the outdoor map. This is due to the laser scanner unable to produce accurate scan measurement in outdoor environment and therefore result in scan matching problems.

ACS Style

Wan Abdul Syaqur; Ahmad Shakaff Ali Yeon; Abu Hassan Abdullah; Kamarulzaman Kamarudin; Retnam Visvanathan; Abdul Halim Ismail; Syed Muhammad Mamduh; Ammar Zakaria. Mobile Robot Based Simultaneous Localization and Mapping in UniMAP's Unknown Environment. 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) 2018, 1 -5.

AMA Style

Wan Abdul Syaqur, Ahmad Shakaff Ali Yeon, Abu Hassan Abdullah, Kamarulzaman Kamarudin, Retnam Visvanathan, Abdul Halim Ismail, Syed Muhammad Mamduh, Ammar Zakaria. Mobile Robot Based Simultaneous Localization and Mapping in UniMAP's Unknown Environment. 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA). 2018; ():1-5.

Chicago/Turabian Style

Wan Abdul Syaqur; Ahmad Shakaff Ali Yeon; Abu Hassan Abdullah; Kamarulzaman Kamarudin; Retnam Visvanathan; Abdul Halim Ismail; Syed Muhammad Mamduh; Ammar Zakaria. 2018. "Mobile Robot Based Simultaneous Localization and Mapping in UniMAP's Unknown Environment." 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) , no. : 1-5.

Journal article
Published: 11 June 2018 in Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering)
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ACS Style

Reena Thriumani; Ammar Zakaria; Ye Htut @ Mohammad Iqbal Omar; Nur Farhanah Ab Halim; Amanina Iymia Jeffree; Ali Yeon Md Shakaff; Latifah Munirah Kamarudin; Abdul Hamid Adom. An Initial Study on Oxidized Graphene-Coated QCM Based Gas Sensor for Cancer Related Volatile Sensing Application. Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering) 2018, 11, 29 -39.

AMA Style

Reena Thriumani, Ammar Zakaria, Ye Htut @ Mohammad Iqbal Omar, Nur Farhanah Ab Halim, Amanina Iymia Jeffree, Ali Yeon Md Shakaff, Latifah Munirah Kamarudin, Abdul Hamid Adom. An Initial Study on Oxidized Graphene-Coated QCM Based Gas Sensor for Cancer Related Volatile Sensing Application. Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering). 2018; 11 (1):29-39.

Chicago/Turabian Style

Reena Thriumani; Ammar Zakaria; Ye Htut @ Mohammad Iqbal Omar; Nur Farhanah Ab Halim; Amanina Iymia Jeffree; Ali Yeon Md Shakaff; Latifah Munirah Kamarudin; Abdul Hamid Adom. 2018. "An Initial Study on Oxidized Graphene-Coated QCM Based Gas Sensor for Cancer Related Volatile Sensing Application." Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering) 11, no. 1: 29-39.

Conference paper
Published: 01 May 2018 in Journal of Physics: Conference Series
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ACS Style

Waleed Noori Hussein; L.M. Kamarudin; Haider N. Hussain; Ammar Zakaria; R Badlishah Ahmed; N.A.H. Zahri. The Prospect of Internet of Things and Big Data Analytics in Transportation System. Journal of Physics: Conference Series 2018, 1018, 1 .

AMA Style

Waleed Noori Hussein, L.M. Kamarudin, Haider N. Hussain, Ammar Zakaria, R Badlishah Ahmed, N.A.H. Zahri. The Prospect of Internet of Things and Big Data Analytics in Transportation System. Journal of Physics: Conference Series. 2018; 1018 ():1.

Chicago/Turabian Style

Waleed Noori Hussein; L.M. Kamarudin; Haider N. Hussain; Ammar Zakaria; R Badlishah Ahmed; N.A.H. Zahri. 2018. "The Prospect of Internet of Things and Big Data Analytics in Transportation System." Journal of Physics: Conference Series 1018, no. : 1.

Journal article
Published: 02 April 2018 in BMC Cancer
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Volatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells. The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium. This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells. The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.

ACS Style

Reena Thriumani; Ammar Zakaria; Yumi Zuhanis Has-Yun Hashim; Amanina Iymia Jeffree; Khaled Mohamed Helmy; Latifah Munirah Kamarudin; Mohammad Iqbal Omar; Ali Yeon Md Shakaff; Abdul Hamid Adom; Krishna C. Persaud. A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS. BMC Cancer 2018, 18, 362 .

AMA Style

Reena Thriumani, Ammar Zakaria, Yumi Zuhanis Has-Yun Hashim, Amanina Iymia Jeffree, Khaled Mohamed Helmy, Latifah Munirah Kamarudin, Mohammad Iqbal Omar, Ali Yeon Md Shakaff, Abdul Hamid Adom, Krishna C. Persaud. A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS. BMC Cancer. 2018; 18 (1):362.

Chicago/Turabian Style

Reena Thriumani; Ammar Zakaria; Yumi Zuhanis Has-Yun Hashim; Amanina Iymia Jeffree; Khaled Mohamed Helmy; Latifah Munirah Kamarudin; Mohammad Iqbal Omar; Ali Yeon Md Shakaff; Abdul Hamid Adom; Krishna C. Persaud. 2018. "A study on volatile organic compounds emitted by in-vitro lung cancer cultured cells using gas sensor array and SPME-GCMS." BMC Cancer 18, no. 1: 362.

Conference paper
Published: 19 March 2018 in IOP Conference Series: Materials Science and Engineering
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This work is concerned with the localization of gas source in dynamic indoor environment using a single mobile robot system. Algorithms such as Braitenberg, Zig-Zag and the combination of the two were implemented on the mobile robot as gas plume searching and tracing behaviours. To calculate the gas source location, a weighted arithmetic mean strategy was used. All experiments were done on an experimental testbed consisting of a large gas sensor array (LGSA) to monitor real-time gas concentration within the testbed. Ethanol gas was released within the testbed and the source location was marked using a pattern that can be tracked by a pattern tracking system. A pattern template was also mounted on the mobile robot to track the trajectory of the mobile robot. Measurements taken by the mobile robot and the LGSA were then compared to verify the experiments. A combined total of 36.5 hours of real time experimental runs were done and the typical results from such experiments were presented in this paper. From the results, we obtained gas source localization errors between 0.4m to 1.2m from the real source location.

ACS Style

Ahmad Shakaff Ali Yeon; Kamarulzaman Kamarudin; Retnam Visvanathan; Syed Muhammad Mamduh Syed Zakaria; Ammar Zakaria; Latifah Munirah Kamarudin. Gas Source Localization via Behaviour Based Mobile Robot and Weighted Arithmetic Mean. IOP Conference Series: Materials Science and Engineering 2018, 318, 012049 .

AMA Style

Ahmad Shakaff Ali Yeon, Kamarulzaman Kamarudin, Retnam Visvanathan, Syed Muhammad Mamduh Syed Zakaria, Ammar Zakaria, Latifah Munirah Kamarudin. Gas Source Localization via Behaviour Based Mobile Robot and Weighted Arithmetic Mean. IOP Conference Series: Materials Science and Engineering. 2018; 318 (1):012049.

Chicago/Turabian Style

Ahmad Shakaff Ali Yeon; Kamarulzaman Kamarudin; Retnam Visvanathan; Syed Muhammad Mamduh Syed Zakaria; Ammar Zakaria; Latifah Munirah Kamarudin. 2018. "Gas Source Localization via Behaviour Based Mobile Robot and Weighted Arithmetic Mean." IOP Conference Series: Materials Science and Engineering 318, no. 1: 012049.

Conference paper
Published: 19 March 2018 in IOP Conference Series: Materials Science and Engineering
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The advancement of Micro-Electro-Mechanical-Systems (MEMS), microcontroller technologies and the idea of Internet of Things (IoT) motivates the development of wireless modules (e.g. WiFi, Bluetooth, Zigbee, and LoRa) that are small and affordable. This paper aims to provide detailed information on the development of the LoRaFi board. The LoRaFi 1.0 is a multi-protocol communication board developed by Centre of Excellence for Advanced Sensor Technology (CEASTech). The board was developed for but not limited to monitor the indoor air quality. The board comprises two different wireless communication modules namely, Long-range technology (LoRa) and WiFi (using ESP8266). The board can be configured to communicate either using LoRa or WiFi or both. The board has been tested and the wireless communication operates successfully. Apart from LoRa, WiFi enables data to be forwarded to the cloud/server where the data can be stored for further data analysis. This helps provide users with real-time information on their smartphones or other applications. In the future, researchers will conduct tests to investigate the communication link quality. Newer version with reduced board size and additional wireless communication module will be developed in the future as to increase board flexibility and widen the range of applications that can use the board.

ACS Style

Noraini Azmi; Sukhairi Sudin; Latifah Munirah Kamarudin; Ammar Zakaria; Retnam Visvanathan; Goh Chew Cheik; Syed Muhammad Mamduh Syed Zakaria; Khudhur Abdullah Alfarhan; R Badlishah Ahmad. Design and Development of Multi-Transceiver Lorafi Board consisting LoRa and ESP8266-Wifi Communication Module. IOP Conference Series: Materials Science and Engineering 2018, 318, 012051 .

AMA Style

Noraini Azmi, Sukhairi Sudin, Latifah Munirah Kamarudin, Ammar Zakaria, Retnam Visvanathan, Goh Chew Cheik, Syed Muhammad Mamduh Syed Zakaria, Khudhur Abdullah Alfarhan, R Badlishah Ahmad. Design and Development of Multi-Transceiver Lorafi Board consisting LoRa and ESP8266-Wifi Communication Module. IOP Conference Series: Materials Science and Engineering. 2018; 318 (1):012051.

Chicago/Turabian Style

Noraini Azmi; Sukhairi Sudin; Latifah Munirah Kamarudin; Ammar Zakaria; Retnam Visvanathan; Goh Chew Cheik; Syed Muhammad Mamduh Syed Zakaria; Khudhur Abdullah Alfarhan; R Badlishah Ahmad. 2018. "Design and Development of Multi-Transceiver Lorafi Board consisting LoRa and ESP8266-Wifi Communication Module." IOP Conference Series: Materials Science and Engineering 318, no. 1: 012051.

Conference paper
Published: 19 March 2018 in E3S Web of Conferences
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In Asia, Climbing perch (Anabas testudineus) is commonly found in paddy fields and irrigation systems. Due to its habitat, Climbing perch is exposed to toxic pesticides used in paddy fields such as metaldehyde which is one of the most widely used molluscicide. This study aims to determine the acute toxicity Lethal Concentration50 (LC50) of metaldehyde and its effect on the behaviour and physical changes of the Climbing perch. The fish mortality responses to six different metaldehyde concentrations ranging from 180 to 330 mg/L were investigated. The 96-h LC50 values were determined and analysed using three different analysis methods which is arithmetic, logarithmic and probit graphic. The LC50 values obtained in this study were 239, 234 and 232 mg/L, respectively. After 96-h of exposure to metaldehyde, the fish showed a series of abnormal behavioural response in all cases: imbalance position, and restlessness of movement. The LC50 values show that metaldehyde is moderately toxic to the Climbing perch indicating that metaldehyde is not destructive to Climbing perch. However, long term exposure of aquatic organisms to the metaldehyde means a continuous health risk for the fish population as they are more vulnerable and it is on high risk for human to consume this toxicated fishes.

ACS Style

Syamimi Wahida Mohamad Ismail; Farrah Aini Dahalan; Ammar Zakaria; Ali Yeon Mad Shakaff; Siti Aqlima Ahmad; Mohd Yunus Abd Shukor; Mohd Khalizan Sabullah; Khalilah Abdul Khalil; Mohd Faizal Ab Jalil. The acute toxicity of the metaldehyde on the climbing perch. E3S Web of Conferences 2018, 34, 02031 .

AMA Style

Syamimi Wahida Mohamad Ismail, Farrah Aini Dahalan, Ammar Zakaria, Ali Yeon Mad Shakaff, Siti Aqlima Ahmad, Mohd Yunus Abd Shukor, Mohd Khalizan Sabullah, Khalilah Abdul Khalil, Mohd Faizal Ab Jalil. The acute toxicity of the metaldehyde on the climbing perch. E3S Web of Conferences. 2018; 34 ():02031.

Chicago/Turabian Style

Syamimi Wahida Mohamad Ismail; Farrah Aini Dahalan; Ammar Zakaria; Ali Yeon Mad Shakaff; Siti Aqlima Ahmad; Mohd Yunus Abd Shukor; Mohd Khalizan Sabullah; Khalilah Abdul Khalil; Mohd Faizal Ab Jalil. 2018. "The acute toxicity of the metaldehyde on the climbing perch." E3S Web of Conferences 34, no. : 02031.

Conference paper
Published: 01 March 2018 in 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA)
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Activity recognition is considered as an important task in many applications, particularly in healthcare services. Among these applications include medical diagnostic, monitoring of users' daily routine and detection of abnormal cases. This paper presents an approach for the activity recognition using an accelerometer sensor embedded in a smartphone. This approach uses a publicly available accelerometer dataset as the raw input signal. The features of the signal are selected based on the time and frequency domain. Then, Principal Component Analysis (PCA) is used to reduce the dimensionality of the features and extract the most significant ones that can classify human activities. A comparison process is performed between the original raw data and PCA-based features and additionally, time and frequency-domain features are also compared using several machine learning classifiers. The obtained results show that the PCA-based features obtain higher recognition rate while frequency-domain features have higher accuracy, with the rate of 96.11% and 92.10% respectively.

ACS Style

Abdul Syafiq Abdull Sukor; Ammar Zakaria; N. Abdul Rahim. Activity recognition using accelerometer sensor and machine learning classifiers. 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) 2018, 233 -238.

AMA Style

Abdul Syafiq Abdull Sukor, Ammar Zakaria, N. Abdul Rahim. Activity recognition using accelerometer sensor and machine learning classifiers. 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA). 2018; ():233-238.

Chicago/Turabian Style

Abdul Syafiq Abdull Sukor; Ammar Zakaria; N. Abdul Rahim. 2018. "Activity recognition using accelerometer sensor and machine learning classifiers." 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) , no. : 233-238.

Conference paper
Published: 23 February 2018 in MATEC Web of Conferences
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Mangifera indica var Harumanis is one of the exotic mango cultivated in Perlis, Malaysia. It economic importance increased in recent years due to the popularity attributed to it excellent taste, sweet and aromatic fragrance. Supply of Harumanis cannot meet market demand, mainly due to the difficulty of raising the crops and the short and uncertain harvesting seasons. The cultivation of Harumanis in greenhouse was found to be feasible to mitigate the short coming. However, the cultural practices of high density planting in greenhouse calls for regular pruning to meet the desired canopy. Regular strategic pruning produced pruning waste with potential economic value. This research was done to quantify the by-products produced by pruning from three 1/2 acre greenhouses, under the ecological and climatic of Perlis. The fresh biomass of waste collected during primary, secondary and tertiary pruning were determined. The pruning waste produced was 0.676kg per plant equivalent to 146.4kg per greenhouse during primary and secondary pruning. Estimation of fresh biomass waste produced during floral induction determine by regression was 816 kg per greenhouse with the expected total of 40.8 ton from 50 greenhouses. In view of the fact that the mango plant management practice was from the clean agricultural, the waste can be potentially utilized for green manufacturing. This was the first attempted to quantify pruning waste in mango cultivation before further utilization of pruning waste.

ACS Style

Rosfatihah Roslim; Ahmad Azudin Nordin; Mohd Hishamudin Che Mat; Ammar Zakaria; Mahmad Nor Jaafar. Evaluation of pruning waste of Mangifera indica var Harumanis cultivated in greenhouse. MATEC Web of Conferences 2018, 150, 06022 .

AMA Style

Rosfatihah Roslim, Ahmad Azudin Nordin, Mohd Hishamudin Che Mat, Ammar Zakaria, Mahmad Nor Jaafar. Evaluation of pruning waste of Mangifera indica var Harumanis cultivated in greenhouse. MATEC Web of Conferences. 2018; 150 ():06022.

Chicago/Turabian Style

Rosfatihah Roslim; Ahmad Azudin Nordin; Mohd Hishamudin Che Mat; Ammar Zakaria; Mahmad Nor Jaafar. 2018. "Evaluation of pruning waste of Mangifera indica var Harumanis cultivated in greenhouse." MATEC Web of Conferences 150, no. : 06022.