Naimah Yaakob
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Preferred name
Naimah Yaakob
Official Name
Naimah, Yaakob
Alternative Name
Yaakob, N.
Yaakob, Naimah Bt
Yaakob, Naimah
Naimah, Y.
Main Affiliation
Scopus Author ID
24826032200
Researcher ID
AAY-3556-2021

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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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Performance comparison of energy efficient dynamic transmission and static transmission power in static mobility node wireless ad-hoc network

2017-12-11 , Siti Asilah Yah , Naimah Yaakob , Ong Bi Lynn , Mohammad Elshaikh Elobaid , Wan Aida Nadia Wan Abdullah

Transmission power optimization in Wireless Ad-Hoc Network is an important thing in order to minimize the energy consumption for effective utilization of the applications like Vehicle Ad-Hoc Network (VANET) applications. If one or more nodes in the wireless Ad-hoc network have little or no energy, then data transmission will be temporarily or permanently interrupted which might create a serious havoc in the Ad-hoc network especially during vital information transferred. This will, in turn, affect the performance of the entire network. Therefore transmission power control is one of the important research topics that needs to be focused in the wireless ad-hoc network in order to ensure effective energy consumption. Recently, we proposed a Dynamic Transmission Power algorithm to maintain network connectivity by adapting node's transmission power based on the distance between the vehicles in VANET. Our research aims to design a dynamic transmission power that can minimize the rate of energy consumption. Hence, in order to develop the proposed method, prerequisite experiment need to be done. This paper investigates the energy saving efficiency of dynamic and static transmission range in static mobility node wireless ad-hoc network which is prerequisite experiments before further experiment on VANET can be carried on. The simulation results prove that dynamic transmission range gives better energy consumption compared to static transmission range, so it is worth it to carry out the subsequent experiments on VANET.

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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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Velocity Based Performance Analysis of GreedLea Routing Protocol in Internet of Vehicle (IoV)

2024-12-01 , Normaliza Omar , Naimah Yaakob , Mohamed Elshaikh Elobaid Said Ahmed , Zulkifli Husin , Iszaidy Ismail , Alaa KY Dafhalla

Intelligent routing protocols for IoV have also been made possible by the convergence of IoT and machine learning algorithm. In order to make informed routing decisions, these intelligent routing protocols examine data gathered from IoT devices like vehicle sensors and traffic monitoring systems using machine learning algorithms. Moreover, as the number of vehicles increases and road networks become more complex, traditional routing protocols for ad hoc networks are being replaced by more advanced and efficient protocols. The purpose of this study is to concentrate on these unique qualities of IoVs network scenario. A combined routing method has been developed to construct periodic connectivity and find routes on-demand in order to save route data as graphs. The simulation's findings show that GreedLea routing protocol outperforms GPSR and AODV routing protocols in terms of delay and packet delivery ratio (PDR). The results demonstrate that the average AODV latency is significantly higher when there are fewer vehicles on the network. This is due to the fact that connections are frequently lost at higher speeds and lower densities, and re-establishing new channels takes a lot of time. As the number of vehicles rises, efficiency improves and the wait gets shorter. The average latency, yet, keeps increasing as vehicle density increases due to the additional overheads related with routing.

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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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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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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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Double layer controller for distributed software defined network in mitigating cyber attacks

2024 , M. Y. Wong , Naimah Yaakob , Mohd Rashidi Che Beson , Nur Idawati Md Enzai , R Badlishah Ahmad

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The effect of total noise on two-dimension OCDMA codes

2017-11-22 , Layth A. Khalil Al Dulaimi , R Badlishah Ahmad , Naimah Yaakob , Syed Alwee Aljunid Syed Junid , Rima Matem

In this research, we evaluate the performance of total noise effect on two dimension (2-D) optical code-division multiple access (OCDMA) performance systems using 2-D Modified Double Weight MDW under various link parameters. The impact of the multi-access interference (MAI) and other noise effect on the system performance. The 2-D MDW is compared mathematically with other codes which use similar techniques. We analyzed and optimized the data rate and effective receive power. The performance and optimization of MDW code in OCDMA system are reported, the bit error rate (BER) can be significantly improved when the 2-D MDW code desired parameters are selected especially the cross correlation properties. It reduces the MAI in the system compensate BER and phase-induced intensity noise (PIIN) in incoherent OCDMA The analysis permits a thorough understanding of PIIN, shot and thermal noises impact on 2-D MDW OCDMA system performance. PIIN is the main noise factor in the OCDMA network.

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Automated detection of Printed Circuit Boards (PCB) defects by using machine learning in electronic manufacturing: current approaches

2020 , S. S. Zakaria , Amiza Amir , Naimah Yaakob , S Nazemi

The manufacturing of a printed circuit board in the SMT assembly line goes through multiple phases of automatic handling. To ensure the quality of the board and reduce the number of defects, inspection tasks such as solder paste inspection and automatic optical inspection are conducted. The inspection tasks are carried out at various phases of the assembly line. The paper aims to answer the questions of how machine learning technology can contribute for better PCB fault detection in the assembly line and at which parts of the assembly line this technology has been applied. The paper discusses the PCB defect detection by using machine learning and other approaches. The current research shows that PCB defect detection using machine learning are miniscule. Early detection is still unexplored and experimented in the industry.

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