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
Now showing 1 - 7 of 7
  • 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.
  • Publication
    Use of natural language processing for the detection of hate speech on social media
    (Semarak Ilmu Publishing, 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.
  • 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.
      28  1
  • Publication
    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.
      19  1
  • Publication
    Content-defined chunking algorithms in data deduplication: performance, trade-offs and future-oriented techniques
    (Semarak Ilmu Publishing, 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.
      1  1