Amiza Amir
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Amiza Amir
Official Name
Amiza, Amir
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Scopus Author ID
36170326400
Researcher ID
EKV-8568-2022

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  • Publication
    Design of Passive RFID Tag Using Frequency Selective Surface with Polarization Insensitive
    ( 2023-10-06)
    Saidatul Norlyana Azemi
    ;
    Ibrahim N.A.
    ;
    Amiza Amir
    ;
    Mohd Rashidi Che Beson
    ;
    Abdul Aziz M.E.
    RFID is not a new technology. It has been applied in various industries such as for wearable applications. Common RFID tags especially for those that have been designed and are available are not independent of the incident receiver angle. Numerous wearable antennas on the market are only designed for a certain received angle. For example, a wearable RFID antenna is used in medical as a pulse reading detector. If the patient makes any movement, the patient's pulse reading is no longer accurate or there may be no pulse reading. Hence, the purpose of this project is to design and RFID antennas using Frequency Selective Surface, FSS for wearable applications that are independent towards the incident angle and small in size. In this project, several antennas design with Frequency Selective Surface (FSS) is proposed. The design for this antenna is round, square, and hexagonal. This antenna has an operating frequency from 2.4 GHz to 5.8GHz, bandwidth efficiency> 50%, dielectric constant 1.30, independent incident angle up to 60 degrees, and has a high gain of around 2 to 3dB.
  • Publication
    Performance Analysis of Congestion Control Mechanism in Software Defined Network (SDN)
    ( 2017-12-11)
    Rahman M.Z.A.
    ;
    Naimah Yaakob
    ;
    Amiza Amir
    ;
    R Badlishah Ahmad
    ;
    Yoon See Ki
    ;
    Abd Halim A.H.
    In the near future, the traditional networks architecture will be difficult to be managed. Hence, Software Defined Network (SDN) will be an alternative in the future of programmable networks to replace the conventional network architecture. The main idea of SDN architecture is to separate the forwarding plane and control plane of network system, where network operators can program packet forwarding behaviour to improve the network performance. Congestion control is important mechanism for network traffic to improve network capability and achieve high end Quality of Service (QoS). In this paper, extensive simulation is conducted to analyse the performance of SDN by implementing Link Layer Discovery Protocol (LLDP) under congested network. The simulation was conducted on Mininet by creating four different fanout and the result was analysed based on differences of matrix performance. As a result, the packet loss and throughput reduction were observed when number of fanout in the topology was increased. By using LLDP protocol, huge reduction in packet loss rate has been achieved while maximizing percentage packet delivery ratio.
  • Publication
    The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal
    ( 2017-12-11)
    Nurul E’zzati Md Isa
    ;
    Amiza Amir
    ;
    Mohd Zaizu Ilyas
    ;
    Mohammad Shahrazel Razalli
    Most EEG-based motor imagery classification research focuses on the feature extraction phase of machine learning, neglecting the crucial part for accurate classification which is the classification. In contrast, this paper concentrates on the classifier development where it thoroughly studies the performance analysis of k-Nearest Neighbour (k-NN) classifier on EEG data. In the literature, the Euclidean distance metric is routinely applied for EEG data classification. However, no thorough study has been conducted to evaluate the effect of other distance metrics to the classification accuracy. Therefore, this paper studies the effectiveness of five distance metrics of k-NN: Manhattan, Euclidean, Minkowski, Chebychev and Hamming. The experiment shows that the distance computations that provides the highest classification accuracy is the Minkowski distance with 70.08%. Hence, this demonstrates the significant effect of distance metrics to the k-NN accuracy where the Minknowski distance gives higher accuracy compared to the Euclidean. Our result also shows that the accuracy of k-NN is comparable to Support Vector Machine (SVM) with lower complexity for EEG classification.
  • Publication
    Image classification for snake species using machine learning techniques
    ( 2017-01-01)
    Amiza Amir
    ;
    Nik Adilah Hanin Zahri
    ;
    Naimah Yaakob
    ;
    R Badlishah Ahmad
    This paper investigates the accuracy of five state-of-the-art machine learning techniques — decision tree J48, nearest neighbors, knearest neighbors (k-NN), backpropagation neural network, and naive Bayes — for image-based snake species identification problem. Conventionally, snake species identification is conducted manually based on the observation of the characteristics such head shape, body pattern, body color, and eyes shape. Images of 22 species of snakes that can be found in Malaysia were collected into a database, namely the Snakes of Perlis Corpus. Then, an intelligent approach is proposed to automatically identify a snake species based on an image which is useful for content retrieval purpose where a snake species can be predicted whenever a snake image is given as input. Our experiment shows that backpropagation neural network and nearest neighbour are highly accurate with greater than 87% accuracy on CEDD descriptor in this problem.
  • Publication
    Large neighbourhood search based on mixed integer programming and ant colony optimisation for car sequencing
    (Taylor & Francis, 2019)
    Dhananjay Thiruvady
    ;
    Kerri Morgan
    ;
    Amiza Amir
    ;
    Andreas T. Ernst
    We investigate the problem of scheduling a sequence of cars to be placed on an assembly line. Stations, along the assembly line install options (e.g. air conditioning), but have limited capacities, and hence cars requiring the same options need to be distributed far enough apart. The desired separation is not always feasible, leading to an optimisation problem that minimises the violation of the ideal separation requirements. In order to solve the problem, we use a large neighbourhood search (LNS) based on mixed integer programming (MIP). The search is implemented as a sliding window, by selecting overlapping subsequences of manageable sizes, which can be solved efficiently. Our experiments show that, with LNS, substantial improvements in solution quality can be found.
  • Publication
    Real-time drowsiness detection system for driver monitoring
    (IOP Publishing, 2020)
    M Arunasalam
    ;
    Naimah Yaakob
    ;
    Amiza Amir
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    N F Azahar
    Nowadays, the rate of road accidents due to microsleep has been alarming. During microsleep, people might doze off without realizing it. For many decades, drowsiness detection system for vehicles was not among the major concerns though it turns out as one of imperative features that could have avoid microsleep and thus should be implemented in all vehicles in order to ensure safety of drivers and other vehicles on the road. To the best of our knowledge, enforcements on driving restriction during drowsiness state is yet to be implemented. The absence of such system in the current transportation systems expose drivers to great danger especially at night because accidents are highly likely to happen at night due to drowsy and fatigue drivers. Therefore, this project proposes a real-time drowsiness detection system for vehicles, featuring ignition lock to reduce accidents. An eye blink sensor is embedded in a wearable glasses and heart beat sensor is used to detect drowsiness level of drivers. The system also includes SMS notification system to relatives or friends with location details of the drowsy driver. This project is able to detect and react based on 3 levels of drowsiness by alerting the driver through buzzer. Ignition lock will be applied when high level of drowsiness is detected. Consequently, the vehicle will be slowed down and eventually stopped when dangerous level of drowsiness is detected as a safety precaution.
  • Publication
    Personal shopper – mobile phone applications
    (IOP Publishing, 2020)
    F N Zamani
    ;
    Saidatul Norlyana Azemi
    ;
    Amiza Amir
    ;
    Naimah Yaakob
    This project is focused on the development of personal shopper mobile phone application. The purpose of this system is to help part-timer and full-timer personal shoppers gather in one platform and also for the customer to hire a personal shopper to buys their items. This project will implement a rating system, give reward to the customer, accept customer requests, view customer requested details and contact the customer through Whatsapp application. This application is very flexible for the customer and personal shopper where the customer can become a personal shopper and vice versa. This entire project is developed according to software engineering methodology with the waterfall model. The tool used to create this project is Android Studio with Java, PHP, XAMPP Server, and MySQL database.
      1  8
  • Publication
    Tomato Diseases Classification Using Extreme Learning Machine
    ( 2023-10-06)
    Xian T.S.
    ;
    Ruzelita Ngadiran
    ;
    Iszaidy Ismail
    ;
    Amiza Amir
    ;
    Siti Zuraidah Ibrahim
    ;
    Taha T.B.
    Plant disease classification is essential to the agriculture industry. In this work, a tomato disease classification using Extreme Learning Machine (ELM) with image-based features. Extreme Learning Machine (ELM), a classification algorithm with a single layer feed-forward neural network where it can be combined with few activation functions. The image features as the input where the image is pre-processed via HSV colour space and extracted using Haralick textures, colour moments and HSV histogram. The features are then loaded in the ELM classifier to perform the ELM training and testing. The accuracy result of ELM classification is then analysed after the validation. The dataset used for disease detection is tomato plant leaves which is a subset of the Plant-Village dataset. The results produced from the ELM demonstrate an accuracy of around 84.94% which is comparable to classifiers such as the Support Vector Machine and Decision Tree.
      2  57
  • Publication
    Gain Enhancement of CPW Antenna for IoT Applications using FSS with Miniaturize Unit Cell
    ( 2021-07-26)
    Saidatul Norlyana Azemi
    ;
    Mohd Rashidi Che Beson
    ;
    Amiza Amir
    ;
    Azhari M.S.B.A.
    ;
    Jiunn N.K.
    Wireless connectivity is a critical enabler for many IoT applications. Antennas are often required to be installed inside the device cover, which usually occurs in small sizes with optimal performance. On the other hand, a suitable antenna should also have high efficiency, gain and adequate bandwidth covering the desired frequency range. Here, we proposed new type of Frequency Selective Surface (FSS) with miniaturized resonator element to enhance the gain of an CPW antenna. Furthermore, the miniaturization of the Frequency Selective Surface unit cell is attained by coupling the two meandered wire resonators. The wire resonator is separated by thin and single substrate layer. The structure of the FSS is shown to have a FSS unit cell dimension that is miniaturized to 0.057λ. The CPW antenna size is only 28.8mm × 46.5mm operating at 2.45 GHz frequency. With the additional of the FSS, the antenna's gain reaches up from 1.8 dBi to 2.6 dBi with omnidirectional radiation pattern.
      1  30
  • Publication
    Machine learning algorithms for optic pathway disease diagnostics: a review
    (IOP Publishing, 2020)
    Abu, Masyitah
    ;
    Nik Adilah Hanin Zahri
    ;
    Amiza Amir
    ;
    Iszaidy Ismail
    Most of people are unaware that some of the indicators of optic pathway diseases such as stroke or tumor can be detected from the loss part of human vision, or referred as visual field defect. Ophthalmologist will manually examine the site, size and margin of the lesion from patient’s visual field points mapped by Humphrey Field Analyzer. Different site, size and margin of lesion indicates different type of defects and disease that associated with it. Therefore, an effective automated detection mechanism of multi class visual field defect is in demand to help decision making by ophthalmologist. In this paper, we review multiple techniques of supervised and unsupervised learning method for detection of optic pathway disease.
      1  7