Ammar Zakaria
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Preferred name
Ammar Zakaria
Official Name
Ammar, Zakaria
Alternative Name
Zakaria, Ammar
Zakaria, A.
Main Affiliation
Scopus Author ID
36560557700
Researcher ID
D-2902-2015

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  • Publication
    Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map
    ( 2022-01-01)
    Zaffry Hadi Mohd Juffry
    ;
    Kamarulzaman Kamarudin
    ;
    Abdul Hamid Adom
    ;
    Mao X.
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Abdulnasser Nabil Abdullah
    The dynamic characteristic of gas dispersal in uncontrolled environment always leads to inaccurate gas source localization prediction from gas distribution map. Gas distribution map is a representation of the gas distribution over an environment which helps human to observe the concentration of harmful gases at a contaminated area. This paper proposes the utilization of Deep Neural Network (DNN) to predict the gas source location in a gas distribution map. DNN learns from the previous gas distribution map data and patterns to generate a model that is able predict location of gas source. The results indicate that DNN is able to accurately predict the location within the range of 0.8 to 2 m from the actual gas source. This finding shows that DNN has a high potential for utilization in gas source localization application.
      1  28
  • Publication
    Deep Neural Network for Localizing Gas Source Based on Gas Distribution Map
    ( 2022-01-01)
    Zaffry Hadi Mohd Juffry
    ;
    Kamarulzaman Kamarudin
    ;
    Abdul Hamid Adom
    ;
    Mao X.
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Abdullah A.N.
    The dynamic characteristic of gas dispersal in uncontrolled environment always leads to inaccurate gas source localization prediction from gas distribution map. Gas distribution map is a representation of the gas distribution over an environment which helps human to observe the concentration of harmful gases at a contaminated area. This paper proposes the utilization of Deep Neural Network (DNN) to predict the gas source location in a gas distribution map. DNN learns from the previous gas distribution map data and patterns to generate a model that is able predict location of gas source. The results indicate that DNN is able to accurately predict the location within the range of 0.8 to 2 m from the actual gas source. This finding shows that DNN has a high potential for utilization in gas source localization application.
      1  29
  • Publication
    Multi-Target Detection and Tracking (MTDT) Algorithm Based on Probabilistic Model for Smart Cities
    ( 2021-03-01)
    Jadaa K.J.
    ;
    Latifah Munirah Kamarudin
    ;
    Hussein W.N.
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    Wireless Sensor Network (WSNs) provides promising solutions for monitoring in several domains including industrial monitoring and control, home automation and smart cities, etc. There are numerous restrictions on the current development of target detection and tracking algorithms which makes them unsuitable or effective for indoor use. Such constraints include changes in the direction and changing target speeds, missing a target, and target dynamics. These issues come with difficulty in detection and tracking multiple targets. Moreover, the majority of the target tracking algorithms were presented on the conditions that the target is typically smooth with no unexpected changes that are difficult absolutely. Moreover, sensing coverage considers the crucial issue in a wireless sensor network. This paper implies an algorithm for detection and tracking of moving targets (intruders) for an indoor environment based on the probabilistic model utilizing WSN for safety and security. A mathematical model is presented to determine the optimum number of sensor nodes needed. The findings of the simulation showed that the MTDT algorithm provides a low missing target rate of less than 0.7 % for worst-case and can be utilized for different kinds of environment scenarios.
      1  8
  • Publication
    Predictive analysis of in-vehicle air quality monitoring system using Deep Learning technique
    ( 2022)
    Abdul Syafiq Abdull Sukor
    ;
    Goh Chew Cheik
    ;
    Latifah Munirah Kamarudin
    ;
    Xiaoyang Mao
    ;
    Hiromitsu Nishizaki
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.
      1  20
  • Publication
    Novel Approach Using Passive UHF RFID for Grain Moisture Detection
    ( 2022-01-01)
    Ainaa Syamim Mohd Radzi
    ;
    Latifah Munirah Kamarudin
    ;
    Latifah Mohamed
    ;
    Ammar Zakaria
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Ndzi D.L.
    This paper proposes a novel method to detect moisture hotspots and irregularities in rice grain storage using low-cost passive UHF RFID technology. Experiments were designed with a UHF RFID handheld reader to test rice moisture levels of 12%, 16%, 20% and 24%. Two containers were used in the study where Container A was filled with grains with a 12% moisture level and container B is inserted into Container A and contained 2 kg rice samples of varying moisture contents. The RFID reader was positioned outside the container to measure the received signal strength indicator (RSSI) from the RFID tags placed within the containers. The attenuation of the signal is analyzed to obtain a correlation between moisture content (MC) and RSSI values. Results show a positive correlation between the RSSI and MC of rice which can be used to identify inconsistencies in moisture distribution in stored grain. An empirical model has been proposed which can be used to estimate RSSI values given the moisture content or vice versa for RFID operating at 915 MHz.
      4  30
  • Publication
    Investigation on magnetometer as potential sensors for infusion pump utilisation status
    ( 2021-12-01)
    Engku Ahmad Rafiqi Engku Ariff
    ;
    Ammar Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Sakinah Zakaria
    ;
    Visvanathan R.
    The utilisation of medical device e.g. an infusion pump is commonly treated as nontrivial process in service and maintenance section albeit in actuality, it is a crucial process for determining its correct maintenance cycle. Therefore, a discussion on the capability of magnetometer sensor as one of potential sensors to be used to keep track the utilisation status of infusion pump. The variation of magnetic flux density and its frequency produced by the stepper motor inside the infusion pump is measured and analysed through magnetometer sensor, embedded inside a Bluetooth Low Energy (BLE) device. Obtained result shows that magnetometer sensor has the potential to track the utilisation status of the tested infusion pumps.
      1  26
  • Publication
    Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment
    ( 2020-05-18)
    Visvanathan R.
    ;
    Kamarulzaman Kamarudin
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Toyoura M.
    ;
    Ali Yeon A.S.
    ;
    Ammar Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Mao X.
    ;
    Shazmin Aniza Abdul Shukor
    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.
      1  21
  • Publication
    Application of Deep Neural Network for Gas Source Localization in an Indoor Environment
    ( 2023-01-01)
    Juffry Z.H.M.
    ;
    Kamarulzaman Kamarudin
    ;
    Abdul Hamid Adom
    ;
    Miskon M.F.
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Abdullah A.N.
    Nowadays, the quality of air in the environment has been impacted by the industry. It is important to make sure our ambient air especially in an indoor environment is clean from contaminating particles or harmful gases. Therefore, the air quality inside the indoor environment should be monitored regularly. One of the major problems, when a particular environment has been contaminated by harmful gases, is finding the source of the emission. If the indoor environment has been contaminated by a harmful source it should be instantly localized and eliminated to prevent severe casualties. In this paper, we propose the utilization of synthetic data generated by the Computational Fluid Dynamic (CFD) approach to train the Deep Neural Network (DNN) model called CFD-DNN to perform gas source localization in an indoor environment. The model is capable to locate the contaminated source within a small area of an indoor environment. A total of 361 datasets with different locations of contaminated source release have been obtained using the CFD approach. The obtained dataset was divided into training and testing datasets. The training dataset was used for the model training process while the testing dataset is fed into the model to test model reliability to predict the gas source location. The Euclidian distance equation was used to measure the distance error between the actual and predicted location of the source. The result shows that the model is capable to locate the gas source within a minimum and maximum error of 0.03m to 0.46m respectively.
      1  30
  • Publication
    Recent Advancements in Radio Frequency based Indoor Localization Techniques
    ( 2021-03-01)
    Kenny Fong Peng Wye
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    Norhawati Ahmad
    Recent developments in location-based services have heightened the needs for decent accuracy localization system for different applications. The Global Positioning System (GPS) provides adequate accuracy in the outdoor environment but suffers from poor localization accuracy in the complex and dynamic indoor environment. Therefore, the number of indoor localization researches to apply in different applications with different technologies have increased in recent years. The purpose of this paper is to summarize recent advancements in Radio Frequency (RF) based indoor localization techniques and provide insights in the indoor localization field especially in the range based localization. The range based localization is then categorized into few common algorithms such as AOA, TOA, TDOA and RSS. The discussion between algorithms in term of their advantages, disadvantages and improvement strategies to improve their accuracy are also present in this paper.
      1  17
  • Publication
    Predictive analysis of In-Vehicle air quality monitoring system using deep learning technique
    ( 2022)
    Abdul Syafiq Abdull Sukor
    ;
    Goh Chew Cheik
    ;
    Latifah Munirah Kamarudin
    ;
    Xiaoyang Mao
    ;
    Hiromitsu Nishizaki
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.
      1  19