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
    On the effectiveness of congestion control mechanisms for remote healthcare monitoring system in IoT environment - A review
    ( 2017-01-03)
    Wan Aida Nadia Wan Abdullah
    ;
    Naimah Yaakob
    ;
    R Badlishah Ahmad
    ;
    Amiza Amir
    ;
    Siti Asilah Yah
    A progressive advancement in biosensors and wireless technology are the major contributors to the realization of continuous remote health monitoring system (RHMS). Wireless Body Area Network (WBAN) is part of this technology due to the deployment of multiple sensors such as Electrocardiogram (ECG) to collect vital body signals for processing and diagnosis. Among the benefits offered by this technology include remote monitoring of patient's health status and early detection of abnormalities in the collected signals. Once detected, several preventive measurements can be taken. However, this system needs to encounter some challenges in the wireless network such as delay, packet loss and throughput due to network congestion when transmitting and receiving a bulk of multiple data. Generally, the presence of these problems in transmitting vital body signals may result in incorrect medical diagnosing which can increase mortality rate and cause severe impact to the overall system's performance. Thus, a suitable design of congestion control mechanism is urgently needed in designing a reliable and efficient remote health monitoring system.
  • Publication
    Controller Placement Algorithms in Software Defined Network - A Review of Trends and Challenges
    ( 2017-12-11)
    Si-Kee Yoon
    ;
    Zahereel Ishwar Abdul Khalib
    ;
    Naimah Yaakob
    ;
    Amiza Amir
    Traditional network architectures are complex to manage, comparatively static, rigid and difficult to make changes for new innovation. The proprietary devices in such architectures are based on manual configuration which are unwieldy and error-prone. Software Defined Network (SDN) which is described as a new network paradigm that decouple the control plane from data plane are capable to solve today's network issues and improve the network performance. Nevertheless, among so many challenges and research opportunity in SDN, Controller Placement Problem (CPP) is said to be the most important issues which can directly affect the overall network performance. Thus far, the issue regarding the CPP and its challenge has not been completely reviewed and discussed properly in any other papers. In this paper, we provide a comprehensive review on several optimized controller placement problem algorithms in SDN. This paper also highlights some limitations of the reviewed methods and also emphasizes on suitable approach to address the aforementioned problems.
  • 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
    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.