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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Norhaslinda Kamaruddin
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering

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Brain Developmental Disorders’ Modelling based on Preschoolers Neuro-Physiological Profiling Abdul Wahab; Norhaslinda Kamaruddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp542-547

Abstract

Frequently misunderstood by their teachers as being low performers, children with learning disabilities (LDs) such as dyslexia, ADHD, and Asperger’s Syndrome develop low self-confidence and poor self-esteem that may lead to the risk of developing psychological and emotional problems. On contrary, research has shown that a substantial number of these children are capable of learning, and hence, are high-functioning. Therefore, there is a need to provide for the early detection of LDs and instruction that focuses on their needs based on their profiles. Profiling is normally done through observations on the psychological manifestations of LDs by parents and teachers as third-party observers. The first party experience, which is reflected through brain manifestations, is often overlooked. Hence the aim of this paper is to present an alternative solution to profile young children with LDs using electroencephalogram (EEG) that capture brain signals to measure brain functionalities and correlate them with the different LDs. Studies on neurophysiological signals and their relationship to LDs are used to develop Computational Neuro-Physiological (CN-P) model to be an alternative in quantifying the children brain activation function related to learning experience. It is envisaged that such model can profile children with learning disabilities to provide effective intervention in timely manner which can help teachers to provide differentiated instruction for children with LDs. This is in line with the thrust of the Education National Key Result Area (NKRA), the Malaysia Education Blueprint 2013-2025, and the Special Education Regulations 2013.
Assessment Analytic Theoretical Framework Based on Learners’ Continuous Learning Improvement M. Hamiz; M. Bakri; Norhaslinda Kamaruddin; Azlinah Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp682-687

Abstract

Currently, university students are required to follow stringent curriculum structure regardless of their performance. Personalized learning is not being offered resulting the whole cohort must compy to a customized fixed curriculum design. This is because the designed curriculum does not take into account different students’ attainment. Furthermore, there is a mismatched between supply and demand of graduates’ skill sets to fulfil the requirement of industry. Due to these issues, employers face difficulties in finding suitable high-skilled worker which contributes to large number of unemployed graduates. Thus, a systematic intervention of students’ learning process is essential to construct informed and strategic responses in order to manage challenges and minimize skill mismatch, at the same time providing adequate fundamental knowledge. In this paper, an assessment analytics framework is proposed based on automated extracted skill sets from curriculum documents and individual performance to recommend adaptive learners’ learning system (ALLS). By preparing the graduates with the required industry skill sets, the graduates’ unemployment rate is envisaged to reduce.
Jobseeker-industry matching system using automated keyword selection and visualization approach Norhaslinda Kamaruddin; Abdul Wahab Abdul Rahman; Ramizah Amirah Mohd Lawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1124-1129

Abstract

Learning opportunities are available with the accessibility of new learning technologies, discovery of untraditional learning pathways and awareness of the importance of connecting current knowledge with new learning. Such situation allows the expansion in the number of courses, programs and professional certifications offered to the students resulting to the increment of the number of graduates annually. The graduates then employed by the industry for executing the job. However, there is a growing concern about the increment of unemployed graduates in the job market. One of the reasons of the mismatch between graduates’ skills and employers’ needs is that the jobseekers tend to choose wrong job because they are overwhelmed by the choices and typically they just randomly send the application because it is time consuming to filter relevant advert. Such action may have repercussion to the industry because the employers need to select relevant candidates to fill up the post from the unfiltered pile of applications making the selection process lengthy and time consuming. In this paper we proposed an automated approach to match the graduates’ and employers’ needs using a hybrid of text mining and visualization approach to facilitate jobseekers’ task of relevant job application. The important keywords are automatically extracted based on the frequency of the word used in the adverts. Then, the graduates’ skills are matched from their personalized profile. Relevant visualization approaches are incorporated to facilitate the selection. It is practical and feasible for the proposed approach to be incorporated in job searching websites that can optimize jobseekers and employers time and effort for a suitable match.
Interlaboratory data fusion repository system (InDFuRS) for tocotrienols-based treatment Norhaslinda Kamaruddin; Abdul Wahab
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1130-1135

Abstract

Tocotrienols and tocopherols are part of the vitamin E family and have shown to produce lots of benefits especially in health supplement product. Both tocotrienols and tocopherols exist in an edible oil but varies in their ratio. It is also observed that percentage of tocopherols is higher than tocotrienols in most of our diet. Recent researches have found that tocotrienols seems to have more benefit to health especially for delaying neuro-degeneration and this has led researchers to investigate tocotrienols rich fraction (TRF) from palm kernel oil. To date, the tocotrienols extraction process is still work in progress. Hence, it is imperative that all information and results from the various laboratories experiments to be made available thus data analysis can be optimized for optimal tocotrinols production. Data acquisition from inter-laboratory experiments are valuable for collaborative researches. Efforts from multiple sources need to be combined to make it accessible for data integration. The sources of fused data can be employed as secondary back up once the data is migrated to a central repository. Traditionally data has been residing in silos across organization. Such scenario posed as a major problem especially when there are insufficient human and computational resources to manage such data. In addition, longitudinal data collections always suffer from mismanagement of the data where the data are not labeled properly using mismatched data formatting resulting to poor data readability. Therefore, a repository to facilitate data fusion using a systematic cloud-based system is proposed to ensure the data are accessible with maintained data uniformity and format and yet the security of the data is ensured as well as cost effective and fault tolerant. It is envisaged a better solution can be identified to minimize repetition of experiments and looking towards at advancement of extraction processes.
Early detection of dysphoria using electroencephalogram affective modelling Norhaslinda Kamaruddin; Mohd Hafiz Mohd Nasir; Abdul Wahab; Frederick C. Harris Jr.
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5874-5884

Abstract

Dysphoria is a trigger point for maladjusted individuals who cannot cope with disappointments and crushed expectations, resulting in negative emotions if it is not detected early. Individuals who suffer from dysphoria tend to deny their mental state. They try to hide, suppress, or ignore the symptoms, making one feel worse, unwanted, and unloved. Psychologists and psychiatrists identify dysphoria using standardized instruments like questionnaires and interviews. These methods can boast a high success rate. However, the limited number of trained psychologists and psychiatrists and the small number of health institutions focused on mental health limit access to early detection. In addition, the negative connotation and taboo about dysphoria discourage the public from openly seeking help. An alternative approach to collecting ‘pure’ data is proposed in this paper. The brain signals are captured using the electroencephalogram as the input to the machine learning approach to detect negative emotions. It was observed from the experimental results that participants who scored severe dysphoria recorded ‘fear’ emotion even before stimuli were presented during the eyes-close phase. This finding is crucial to further understanding the effect of dysphoria and can be used to study the correlation between dysphoria and negative emotions.
Co-Authors A. H. S. Nurhasmiza Abdul Wahab Abdul Wahab Abdul Rahman Abdul Wahab Abdul Rahman Abdul Wahab Abdul Rahman Azlinah Mohamed Dini Handiyani Frederick C. Harris Jr. Khairul Ikhwan Mohamad Halim M. Bakri M. Hamiz Mohd Hafiz Mohd Nasir Muhammad Hafiq Iqmal Mohd Noh N. A. S. Nilam Nur Azmina Mohamad Zamani Nur Shahana Saat Nurul Izzati Mat Razi P. Marlia Raja Durratun Safiyah Ramizah Amirah Mohd Lawi Srie Azrina Zulkiflie Yasmeen Rozaidi Z. Othman