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

Research Output

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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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Use of natural language processing for the detection of hate speech on social media

2025-09 , Mehedi Hasan Shohan , Kazi Rifat Ahmed , Nur Farhan Kahar , Nusrat Jahan , Md. Maruf Hassan , Nadira Islam , R Badlishah Ahmad , Ong Bi Lynn , Naimah Yaakob

Our society’s communication patterns have fundamentally changed as a consequence of the emergence of social media platforms. One effect of these changes is a rise in unpleasant behaviours like making rude and derogatory comments online. Speaking harshly or disrespectfully to someone in person may be difficult. However, online abuse and posting of improper material are considered to be acceptable. Hate speech has the potential to hurt a person or a group of people. Inappropriate material must be identified, in order to be filtered or banned from the web. CNN is a type of deep machine-learning model that has been suggested for such identification, because it performs better than conventional techniques in resolving text categorization problems. Our goal investigates how hate speech may be detected using NLP. In addition, a recent technique has been used in this field to a dataset. This classifier is assigned in each tweet to one of the three Twitter dataset categories of hatred, foul language, or neither. This model’s performance has been assessed with accuracy. The Naïve Bayes, the Decision Tree, KNN, Linear Regression, and the Random Forest are five algorithms that have been used. Of these, Linear Regression provided the greatest accuracy of 94%. It should be noted that when looking at each class separately, many hateful tweets have been mislabelled. It is advisable to look at the outcomes and faults in much detail, in order to comprehend the misclassification. Our analysis shows a better outcome in detecting hateful speech in social media.

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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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Content-defined chunking algorithms in data deduplication: performance, trade-offs and future-oriented techniques

2025 , Safa Ali Abo Hussein , R Badlishah Ahmad , Naimah Yaakob , Fathey Mohammed , Abdul Ghani Khan

In the digital era, the exponential growth of data presents significant challenges for storage efficiency and processing speed. This paper reviews Content-Defined Chunking (CDC), a cornerstone in data deduplication technology, aimed at addressing these challenges. We systematically examine various CDC algorithms, categorising them into hashing-based and hash-less methodologies, and evaluating their performance in deduplication processes. Through a critical analysis of existing literature, the study identifies the balance between chunking speed and deduplication efficacy as a pivotal area for enhancement. Our findings reveal the need for innovative CDC algorithms to adapt to the evolving data landscape, proposing future research directions for improving storage and processing solutions. This work contributes to the broader understanding of data deduplication techniques, offering a pathway towards more efficient data management systems.

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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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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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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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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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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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