Syed Muhammad Mamduh Syed Zakaria
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Preferred name
Syed Muhammad Mamduh Syed Zakaria
Official Name
Syed Muhammad Mamduh, Syed Zakaria
Alternative Name
Syed Zakaria, Syed Muhammad Mamduh
Syed Zakaria, S. M.M.
Zakaria, S. M.M.S.
Zakaria, Syed Muhammad Mamduh Bin Syed
Zakaria, Syed Muhammad Mamduh Syed
Main Affiliation
Scopus Author ID
55193486100
Researcher ID
AAY-1214-2020

Research Output

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  • Publication
    Performance analysis of the microsoft kinect sensor for 2D Simultaneous Localization and Mapping (SLAM) techniques
    ( 2014)
    Kamarulzaman Kamarudin
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Ali Yeon Md Shakaff
    ;
    Ammar Zakaria
    This paper presents a performance analysis of two open-source, laser scanner-based Simultaneous Localization and Mapping (SLAM) techniques (i.e., Gmapping and Hector SLAM) using a Microsoft Kinect to replace the laser sensor. Furthermore, the paper proposes a new system integration approach whereby a Linux virtual machine is used to run the open source SLAM algorithms. The experiments were conducted in two different environments; a small room with no features and a typical office corridor with desks and chairs. Using the data logged from real-time experiments, each SLAM technique was simulated and tested with different parameter settings. The results show that the system is able to achieve real time SLAM operation. The system implementation offers a simple and reliable way to compare the performance of Windows-based SLAM algorithm with the algorithms typically implemented in a Robot Operating System (ROS). The results also indicate that certain modifications to the default laser scanner-based parameters are able to improve the map accuracy. However, the limited field of view and range of Kinect's depth sensor often causes the map to be inaccurate, especially in featureless areas, therefore the Kinect sensor is not a direct replacement for a laser scanner, but rather offers a feasible alternative for 2D SLAM tasks.
  • 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.
  • Publication
    Rice Grain Moisture Sensing Based on UHF RFID Tag
    ( 2022-06-24)
    Ainaa Syamim Mohd Radzi
    ;
    Latifah Munirah Kamarudin
    ;
    Latifah Mohamed
    ;
    Ammar Zakaria
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Ndzi D.L.
    One of the critical steps in the post-production of paddy rice is to be stored in conditions that need to be controlled, especially the moisture content (MC) of the grains. The ability to determine and control moisture is a very important aspect of maintaining grain quality. This study aims to detect the MC of rice grain using UHF RFID technology. In this paper, three experiments have been carried out to detect the MC of rice in full rice grain-filled containers involving two conditions: with metal and without metal containers. The samples used consist of four 2 kg bags with MC levels of 11.875%, 16%, 20%, and 24%. The Received Signal Strength Indicator (RSSI) values were measured using a UHF handheld reader with two RFID tags to predict the MC. The results show an increasing RSSI pattern as the MC increases.
      3
  • Publication
    Gas Source Localization via Mobile Robot with Gas Distribution Mapping and Deep Neural Network
    ( 2022-01-01)
    Ahmad Shakaff Ali Yeon
    ;
    Ammar Zakaria
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Visvanathan R.
    ;
    Kamarulzaman Kamarudin
    ;
    Latifah Munirah Kamarudin
    With the growth of artificial intelligence compute technology, the gas source localization problem would be solved by mobile robots equipped with gas sensing system and artificial intelligence compute units. This work presented a feasibility study of deep learning approach towards gas source localization by mobile robots. A deep neural network strategy was developed and incorporated with the Kernel DM+V gas distribution mapping method. The gas source localization work in this paper was performed on a controlled indoor testbed. From this work, it is shown that by incorporating the developed deep neural network model, it may help improved the gas source location prediction accuracy. A comparison of accuracy between Kernel DM+V and the neural network model is also presented to better visualize the improvement.
      3
  • Publication
    RF-Based moisture content determination in rice using machine learning techniques
    ( 2021)
    Noraini Azmi
    ;
    Latifah Munirah Kamarudin
    ;
    Ammar Zakaria
    ;
    David Lorater Ndzi
    ;
    Mohd Hafiz Fazalul Rahiman
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Latifah Mohamed
    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.
      10  6
  • 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
  • Publication
    Development of fruits artificial intelligence segregation
    ( 2021-12)
    Norhidayah Mohd Rozi
    ;
    Razaidi Hussin
    ;
    Muammar Mohamad Isa
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Afzan Kamarudin
    ;
    Rizalafande Che Ismail
    ;
    Mohd Nazrin Md Isa
    ;
    Siti Zarina Md Naziri
    Higher output was needed as technology advance to meet human needs and industry demands. Fruits Artificial Intelligence Segregation (FAIS) is a project that uses image processing to detect and differentiate between various types of fruits. This paper proposes an OpenCV python, and the Convolution Neural Network (CNN) is used to complete the segregation of multiple fruits. The code extracts the fruit's characteristics and separates them based on their color and shape once placed in front of the camera to implement liveness detection. This paper shows the accuracy and reliability of the Fruits Artificial Intelligence Segregation (FAIS) system based on the number of datasets.
      2  10
  • Publication
    Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique
    ( 2022-10-01)
    Abdul Syafiq Abdull Sukor
    ;
    Cheik Goh Chew
    ;
    Latifah Munirah Kamarudin
    ;
    Mao Xiaoyang
    ;
    Nishizaki H.
    ;
    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  3
  • 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.
      4