Ong Bi Lynn
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Preferred name
Ong Bi Lynn
Official Name
Ong, Bi Lynn
Alternative Name
Bi Lynn, Ong
Ong, B. L.
Ong, Ong Bi Lynn
Lynn, Ong Bi
Ong, Bi Lynn
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Scopus Author ID
36728195700
Researcher ID
DKH-0788-2022

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  • 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
    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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