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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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.

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Large neighbourhood search based on mixed integer programming and ant colony optimisation for car sequencing

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.

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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.

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Evaluating Tree-based Ensemble Strategies for Imbalanced Network Attack Classification

2024-01-01 , Soon H.F. , Amiza Amir , Nishizaki H. , Nik Adilah Hanin Zahri , Latifah Munirah Kamarudin , Saidatul Norlyana Azemi

With the continual evolution of cybersecurity threats, the development of effective intrusion detection systems is increasingly crucial and challenging. This study tackles these challenges by exploring imbalanced multiclass classification, a common situation in network intrusion datasets mirroring realworld scenarios. The paper aims to empirically assess the performance of diverse classification algorithms in managing imbalanced class distributions. Experiments were conducted using the UNSW-NB15 network intrusion detection benchmark dataset, comprising ten highly imbalanced classes. The evaluation includes basic, traditional algorithms like the Decision Tree, KNearest Neighbor, and Gaussian Naive Bayes, as well as advanced ensemble methods such as Gradient Boosted Decision Trees (GraBoost) and AdaBoost. Our findings reveal that the Decision Tree surpassed the Multi-Layer Perceptron, K-Nearest Neighbor, and Naive Bayes in terms of overall F1-score. Furthermore, thorough evaluations of nine tree-based ensemble algorithms were performed, showcasing their varying efficacy. Bagging, Random Forest, ExtraTrees, and XGBoost achieved the highest F1-scores. However, in individual class analysis, XGBoost demonstrated exceptional performance relative to the other algorithms. This is confirmed by achieving the highest F1-scores in eight out of the ten classes within the dataset. These results establish XGBoost as a predominant method for handling multiclass imbalance classification with Bagging being the closest feasible alternative, as Bagging gains an almost similar accuracy and F1-score as XGBoost.

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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.

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Real-time drowsiness detection system for driver monitoring

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.

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UHF RFID tag antennas for wearable devices

2023 , Saidatul Norlyana Azemi , Wan Nur Azreen Azemin , Nornikman Hassan , Amiza Amir , Mohd Rashidi Che Beson

RFID technology is evolving as one of the most popular technologies in this era of technology, fast gaining attention due to high demand from users and rapidly garnering interest in scientific and commercial areas. The frequency used will be determined by the RFID application, and the power rate will change as the frequency increases. Without a straight line of sight, the RFID tag antenna can identify distinct objects. For wearable applications, several RFID tag antennas are too big to match the chip impedance. In order to overcome that problem, a small tag RFID tag antenna for UHF is designed which aimed to be operated from 865 to 867 MHz for assembling production. Impedance matching is used to transforms the impedance of the radiating antenna, to match the chip impedance. To design and simulate the designed antenna, CST Microwave Studio software has been used in this project to get the desired result which is the return loss and gain. The design for this antenna is very simple to ease the fabrication process. Overall, the construction comprises a spiral-shaped loop RFID tag antenna printed on Roger substrate RO4350 with a height of 0.8 mm and a dielectric constant of 3.3. The simulation result of the reflection coefficient of the antenna is 866 MHz at the operating frequency.

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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.

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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.

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Personal shopper – mobile phone applications

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.

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