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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science

Marshima Mohd Rosli

Universiti Teknologi MARA

Author-ID : 2394357

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 10 Documents Claim Missing Document

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Title

A mapping study on blood glucose recommender system for patients with gestational diabetes mellitus Shuhada Mohd Rosli; Marshima Mohd Rosli; Rosmawati Nordin
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Blood glucose (BG) prediction system can help gestational diabetes mellitus (GDM) patient to improve the BG control with managing their dietary intake based on healthy food. Many techniques have been developed to deal with blood glucose prediction, especially those for recommender system. In this study, we conduct a systematic mapping study to investigate recent research about BG prediction in recommender systems. This study describes an overview of research (2014-2018) about BG prediction techniques that has been used for BG recommender system. As results, 25 studies concerning BG prediction in recommender system were selected. We observed that although there is numerous studies published, only a few studies took serious discussion about techniques used to incorporate the BG algorithms. Our result highlighted that only one study discusses hybrid filtering technique in BG recommender system for GDM even though it has an ability to learn from experience and to improve prediction performance. We hope that this study will encourage researchers to consider not only machine learning and artificial intelligent techniques but also hybrid filtering technique for BG recommender system in the future research.

Security issues and framework of electronic medical record: A review Jibril Adamu; Raseeda Hamzah; Marshima Mohd Rosli
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

The electronic medical record has been more widely accepted due to its unarguable benefits when compared to a paper-based system. As electronic medical record becomes more popular, this raises many security threats against the systems. Common security vulnerabilities, such as weak authentication, cross-site scripting, SQL injection, and cross-site request forgery had been identified in the electronic medical record systems. To achieve the goals of using EMR, attaining security and privacy is extremely important. This study aims to propose a web framework with inbuilt security features that will prevent the common security vulnerabilities in the electronic medical record. The security features of the three most popular and powerful PHP frameworks Laravel, CodeIgniter, and Symfony were reviewed and compared. Based on the results, Laravel is equipped with the security features that electronic medical record currently required. This paper provides descriptions of the proposed conceptual framework that can be adapted to implement secure EMR systems.

Physical activity prediction using fitness data: Challenges and issues Nur Zarna Elya Zakariya; Marshima Mohd Rosli
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.v10i1.2474

In the new healthcare transformations, individuals are encourage to maintain healthy life based on their food diet and physical activity routine to avoid risk of serious disease. One of the recent healthcare technologies to support self health monitoring is wearable device that allow individual play active role on their own healthcare. However, there is still questions in terms of the accuracy of wearable data for recommending physical activity due to enormous fitness data generated by wearable devices. In this study, we conducted a literature review on machine learning techniques to predict suitable physical activities based on personal context and fitness data. We categorize and structure the research evidence that has been publish in the area of machine learning techniques for predicting physical activities using fitness data. We found 10 different models in behavior change technique (BCT) and we selected two suitable models which are fogg behavior model (FBM) and trans-theoretical behavior model (TTM) for predicting physical activity using fitness data. We proposed a conceptual framework which consists of personal fitness data, combination of TTM and FBM to predict the suitable physical activity based on personal context. This study will provide new insights in software development of healthcare technologies to support personalization of individuals in managing their own health.

Co-Authors Jibril Adamu Muhammad Izzuddin Mohd Sani Muhammad Zabir Abdul Halim Nordin Abu Bakar Normaly Kamal Ismail Nur Atiqah Sia Abdullah Nur Zarna Elya Zakariya Nurazzah Abd Rahman Raseeda Hamzah Rosmawati Nordin Shaiful Bakhtiar bin Rodzman Shuhada Mohd Rosli Siti Nur Shahidah Zaman Shah Syed Ahmad Aljunid Wan Mohd Hafiz bin Wan Hussin Yun-Huoy Choo

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Journal : Bulletin of Electrical Engineering and Informatics