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Correlation of learning disabilities to porn addiction based on EEG Norhaslinda Kamaruddin; Nurul Izzati Mat Razi; Abdul Wahab
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2462

Researchers were able to correlate porn addiction based on electroencephalogram (EEG) signal analysis to the psychological instruments’ findings. In this paper we attempt to correlate the porn addiction to various cases of learning disorders through analyzing EEG signals. Since porn addiction involved the brainwave power at the frontal of the brain, which reflects the executive functions, this may have correlation to learning disorder. Only three types of learning disorder will be of interest in our study involving dyslexic, attention deficit and hyperactivity disorder (ADHD) and autistic children because they involved reduced intellectual ability observed from the lack of listening, speaking, reading, writing, reasoning, or mathematical proficiencies. Children with such disorder when expose to the internet unfiltered porn contents may have minimal understanding of the negative effects of the contents. Such unmonitored exposure to pornographic contents may result to porn addiction because it may trigger excitement and induced pleasure. Experimental results show strong correlation of learning disorders to porn addiction, which can be worthwhile for further analysis. In addition, this paper also indicates that analyzing brainwave patterns could provide a better insight into predicting and detecting children with learning disorders and addiction with direct analysis of the brain wave patterns.

Small and medium enterprise business solutions using data visualization Norhaslinda Kamaruddin; Raja Durratun Safiyah; Abdul Wahab
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2463

The small and medium enterprise (SME) companies optimize performance using different automated systems to highlight the operations concerns. However, lack of efficient visualization in reporting results in slow feedbacks, difficulties in extracting root cause, and minimal corrective actions. To complicate matters, the data heterogeneity has intensely increased, and it is produced in a fast manner making it unmanageable if the traditional methods of analytics are applied. Hence, we propose the use of a dashboard that can summarize the operational events using real-time data based on the data visualization approach. This proposed solution summarizes the raw data, which allows the user to make informed decisions that can give a positive impact on business performance. An interactive intelligent dashboard for SME (iid-SME) is developed to tackle issues such as measurement of cases completed, the duration of time needed to solve a case, the individual performance of handling cases and other tasks as a proof of concept. From the result, the implementation of the iid-SME approach simplifies the conveyance of the message and helps the SME personnel to make decisions. With the positive feedback obtained, it is envisaged that such a solution can be further employed for SME improvement for better profit and decision making.

Effective tocotrienol dosage traceability system using blockchain technology Norhaslinda Kamaruddin; Abdul Wahab
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Tocotrienol dosage, especially in vitamin E, is important for treatment and prevention of diseases. To date, the dosage is given based on the physician's knowledge and experience to suit the patient’s needs. The alteration of the dosage is depending on the way the patient’s body reaction and coping mechanism which is different from one to another. Hence, the optimal dosage is very difficult to achieve and may result in undesirable side effects. An alternative solution using blockchain technology to trace and chart the dosage of tocotrienol is proposed to capture the effective measure for the patient. With the advancement of the internet of things (IoT) and big data analytics technologies, an effective tocotrienol dosage is possible by utilizing the data gathered from the individual patient for tocotrienol dosage personalization profiling. Then, the output can be used to assist the physician to diagnose an appropriate amount of tocotrienol dosage for optimum effect. This paper discusses the theoretical framework of using blockchain technology to develop an effective tocotrienol dosage traceability system. It is envisaged that such an approach can be a guide to the health practitioners to administer the correct dosage for the patient and subsequently leads to a better quality of life.

Dynamic navigation indoor map using Wi-Fi fingerprinting mobile technology Srie Azrina Zulkiflie; Norhaslinda Kamaruddin; Abdul Wahab
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

This paper presents the exploitation of Wi-Fi signals sensors using fingerprinting method to capture the location and provide the possible navigation paths. Such approach is practical because current smartphones nowadays are equipped with inertial sensors that can capture the Wi-Fi signals from the Wi-Fi’s access points inside the building. From the comparative study conducted, the AnyPlace development tool is used for the development of dynamic navigation indoor map. Its components, namely; Architect, Viewer, Navigator and Logger are used for different specific functions. As a case study, we implement the proposed approach to guide user for navigation in Sunway Pyramid Shopping Mall, Malaysia as floor plan as well as using Google Maps as the base map for prove of concept. From the developer point of view, it is observed that the proposed approach is viable to create a dynamic navigation indoor map provided that the floor plans must be generated first. Such plan should be integrated with the SDK tool to work with the navigation APIs. It is hoped that the proposed work can be extended for more complex indoor map for better implementation.

Neuro-physiological porn addiction detection using machine learning approach Norhaslinda Kamaruddin; Abdul Wahab; Yasmeen Rozaidi
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp964-971

Pornography is a portrayal of sexual subject contents for the exclusive purpose of sexual arousal that can lead to addiction. The availability and easy accessibility of the Internet connectivity have created unprecedented opportunities for sexual education, learning, and growth for adolescences to be in the rise. Hence, the risk of porn addiction developed by teenagers has also increased due to highly prevalent porn consumption. To date, the only available means of detecting porn addiction is through questionnaire. However, while answering the questions, participants may suppress or exaggerate their answers because porn addiction is considered taboo in the community. Hence, the purpose of this project is to develop an engine with multiple classifiers to recognize porn addiction using electroencephalography (EEG) signals and to compare classifiers performance. In the experimental study, the neuro-physiological signals of EEG data were collected previously in Indonesia among students age 9 to 13 years old by researchers from the International Islamic University Malaysia (IIUM). The EEG data were pre-processed, and relevant features are extracted using Mel-Frequency Cepstral Coefficients (MFCC). Then, the features are classified to produce the outputs of valance and arousal. Subsequently, three different classifiers of Multilayer Perceptron (MLP), Naive Bayesian (NB), and Random Forest (RF) are employed to determine whether the participant is a porn addict or otherwise. The experimental results show that the MLP classifier yields slightly better accuracy compared to Naïve Bayes and Random Forest classifiers making the MLP classifier preferable for porn addiction recognition. Although this work is still at infancy stage, it is envisaged for the work to be expanded for comprehensive porn addiction recognition system so that early intervention and appropriate support can be given for the teenagers with pornography addiction problem.

Visualization of job availability based on text analytics localization approach Nur Azmina Mohamad Zamani; Norhaslinda Kamaruddin; Abdul Wahab; Nur Shahana Saat
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp744-751

Rate of employment is a strong indicator of economic stability of a country. It relates to the number of volumes of produced products and services. If the unemployment rate is high, the amount of gross domestic product (GDP) of a country may be declined. One of the main factors that contributes to low rate of employment is the mismatch between job seeker and the requirement of the job applied. This is due to the limited analysis performed on the relevant information on job advertisement; such as, skills, responsibilities of the job, location and expectation of the employers. The obscure job descriptions provided in the advertisement may result in application of unsuitable candidates that can cause rejection of the candidate and the potential employer may take a long time to filter and evaluate the applications. A system that is able to provide relevant information in a simple and catchy way is needed to simplify the task of job searching. In this paper we proposed a text analytics technique to extract users’ comments from social media such as Twitter and Facebook on job advertisement. The result is then displayed in a geotagged map that can reveal the density of job availability based on geographical location. The job seekers can easily observe and select their desired job location. The initial system shows potential of the inclusion of the proposed approach in job advertisement websites. In comparison to other job searching websites, this system can provide additional information on public view about the advertised job obtained from the social media text analytics. With this additional information, jobseekers have more confidence in job selection and allows employers to receive more suitable candidates for the available positions. It is hoped that the proposed system can tailor the job advertisements to the need of the jobseeker and making the job application more relevant hence reducing the potential employers’ processing time.

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

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.

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

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

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 â€˜pureâ€™ 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 â€˜fearâ€™ 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.

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