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All Journal Journal of Applied Data Sciences Data Science Insights
Norzariyah Yahya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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The Determinant Factors For The Issuance Of Central Bank Digital Currency (CBDC) In Malaysia Using Machine Learning Framework Normi Sham Awang Abu Bakar; Norzariyah Yahya; Norbik Bashah Idris; Engku Rabiah Adawiah Engku Ali; Jasni Mohamad Zain; Erni Eliana Khairuddin; Ahmad Firdaus Zainal Abidin; Sheikh Mohammad Tahsin Murtaj; Siti Sarah Maidin
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.176

Abstract

In order to identify the factors influencing the establishment of the Centre Bank Digital Currency (CBDC) in Malaysia, this study leverages the machine-learning technique to determine the most critical factors leading to CBDC issuance in Malaysia. The overall Central Bank Digital Currency Project Index (CBDCPI) was selected as a target variable,while two machine learning algorithms, Random Forest and XGBoost were used to identify the determining variables. The accuracy obtained through the Random Forest is 83% and subsequently, 80% in XGBoost. This study explored a new research frontier by creating two machine-learning models that treated retail and wholesale CBDCPI as target variables. The data used in the process are gathered from various official sources such as the Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank. The Circulation of Cash, Prevalence of Cryptocurrencies, Effect of CBDC on International Trade, the Search Interest, Financial Development Index, Innovation Value, and Trade Openness are some of the most critical factors determining whether CBDC will be issued in Malaysia. Generally, are identified as important factors determining whether CBDC will be issued in Malaysia. Eventually, the factors identified will be used to develop a framework for the implementation of CBDC in Malaysia.
The need for an enhanced IoT-based malware detection model using Artificial Intelligence (AI) algorithm: A Review Siti Sarah Maidin; Norzariyah Yahya
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.6

Abstract

The interconnected world using technology has opened the door for cyberattacks. For example, the utilization of Internet of Things (IoT) devices has increased the exposure to malware attacks. The massive amount of data generated by the IoT devices leads to the possibility of infections in the network. Due to the diverse nature of the IoT devices and the ever-evolving nature of their environment, it can be challenging to devise very comprehensive security measures. Therefore, the application of Artificial Intelligence (AI) in detecting malware has gained attention as a suitable tool for detecting malware due to its strength in malware classification. This research aims to review malware detection in IoT devices using AI and its challenges.
Current and Future Trends for Sustainable Software Development: Software Security in Agile and Hybrid Agile through Bibliometric Analysis Siti Sarah Maidin; Norzariyah Yahya; Muhammad Ashraf bin Fauri Fauzi; Normi Sham Awang Abu Bakar
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.473

Abstract

The industrial growth of digitalized era has given rise to a growing concern in software development. The present research investigates the prevailing and projected patterns in sustainable software development, especially those related to process innovation, with a particular emphasis on software security within Agile and Hybrid Agile approaches, employing bibliometric analysis. However, a comprehensive understanding of the security concerns of both agile and hybrid agile is crucial and needs further garnered. However, it is expected that a thorough comprehension of the hybrid agile model landscape would uncover various themes encompassing its implementation. The analysis aims to provide a comprehensive overview of the current, present, and future state of software security for agile and hybrid agile. The study employed a bibliometric approach to gather a total of 1593 journals from the Web of Science (WOS) database. This study utilizes co-citation and co-word analysis techniques to identify the most significant articles, delineate the fundamentals framework, and provide a prognosis for future development. The present investigation has successfully discovered four distinct co-citation and three distinct co-word clusters. This study offers valuable insights regarding the software security in agile and hybrid agile. The increasing evolution of the software ecosystem necessitates the prioritization of bridging the gap between agility and security. This paper provides a detailed roadmap for scholars and practitioners who are navigating this intersection
The Determinant Factors For The Issuance Of Central Bank Digital Currency (CBDC) In Malaysia Using Machine Learning Framework Normi Sham Awang Abu Bakar; Norzariyah Yahya; Norbik Bashah Idris; Engku Rabiah Adawiah Engku Ali; Jasni Mohamad Zain; Erni Eliana Khairuddin; Ahmad Firdaus Zainal Abidin; Sheikh Mohammad Tahsin Murtaj; Siti Sarah Maidin
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.176

Abstract

In order to identify the factors influencing the establishment of the Centre Bank Digital Currency (CBDC) in Malaysia, this study leverages the machine-learning technique to determine the most critical factors leading to CBDC issuance in Malaysia. The overall Central Bank Digital Currency Project Index (CBDCPI) was selected as a target variable,while two machine learning algorithms, Random Forest and XGBoost were used to identify the determining variables. The accuracy obtained through the Random Forest is 83% and subsequently, 80% in XGBoost. This study explored a new research frontier by creating two machine-learning models that treated retail and wholesale CBDCPI as target variables. The data used in the process are gathered from various official sources such as the Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank. The Circulation of Cash, Prevalence of Cryptocurrencies, Effect of CBDC on International Trade, the Search Interest, Financial Development Index, Innovation Value, and Trade Openness are some of the most critical factors determining whether CBDC will be issued in Malaysia. Generally, are identified as important factors determining whether CBDC will be issued in Malaysia. Eventually, the factors identified will be used to develop a framework for the implementation of CBDC in Malaysia.
Co-Authors Ahmad Firdaus Zainal Abidin Engku Rabiah Adawiah Engku Ali Erni Eliana Khairuddin Jasni Mohamad Zain Muhammad Ashraf bin Fauri Fauzi Norbik Bashah Idris Normi Sham Awang Abu Bakar Normi Sham Awang Abu Bakar Sheikh Mohammad Tahsin Murtaj Siti Sarah Maidin Siti Sarah Maidin