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All Journal Journal of Applied Data Sciences International Journal of Advanced Science and Computer Applications Data Science Insights International Journal Research on Metaverse
Yahya, Norzariyah
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Current and Future Trends for Sustainable Software Development: Software Security in Agile and Hybrid Agile through Bibliometric Analysis Maidin, Siti Sarah; Yahya, Norzariyah; Fauzi, Muhammad Ashraf bin Fauri; Bakar, Normi Sham Awang Abu
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
An Exploration into Hybrid Agile Development Approach Maidin, Siti Sarah; Yahya, Norzariyah
International Journal of Advanced Science and Computer Applications Vol. 2 No. 2: September 2023
Publisher : Utan Kayu Publishins

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/ijasca.v2i2.32

Abstract

The aim of this paper is to provide a review of the different hybrid agile models. This study raised the question “what are the types of hybrid agile models and their features”? The systematic review was done using Preferred Reporting Items for Systematic Reviews and Meta-Analyses Model for comprehensive searching. Scopus is used as the database for searching articles. A total of 131 papers related to agile and hybrid agile models were retrieved and finally after screening and filtering, only 26 papers were included in the study. This paper probes into the features of agile and hybrid agile models and thus recommends hybrid agile models as one of the best-suited models in software development due to several reasons. Some of the reasons are due to its comprehensiveness in managing a large-scale project with good documentation and developing better methods for business analysis. This paper concludes by providing insight into the different types of hybrid agile models in software development. This paper starts with an Introduction section, followed by a Materials and Method section, continued with a Results and Discussion section, and finally concludes the research in the Conclusion section.
The need for an enhanced IoT-based malware detection model using Artificial Intelligence (AI) algorithm: A Review Maidin, Siti Sarah; Yahya, Norzariyah
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.
Decision Support System Application in Disaster Management Yilin, Li; Zhaoji, Fu; Kowthalam, Vijay Rathnam; Guangfa, Wu; Binti Abdul Rahim, Yusrina; Maidin, Siti Sarah; Yahya, Norzariyah
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.21

Abstract

Disasters such as earthquake, flood, fire, and tsunami result in catastrophic human suffering, loss of property and other negative consequences. The continues threats of future disasters enforce human to find best possible ways to detect and take premeasured actions based on calculated risks to reduce these negative impacts of disasters. 
Price Trend Prediction and Discount Optimization for Video Games in Online Stores Using XGBoost and Time-Series Analysis: A Data Mining Approach for Metaverse-Driven Market Insights Maidin, Siti Sarah; Yahya, Norzariyah
International Journal Research on Metaverse Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i4.41

Abstract

This research explores the application of data mining techniques, specifically XGBoost, to predict game pricing trends and optimize discount strategies within the digital gaming market. Game prices are influenced by various factors, including production costs, market demand, and promotional strategies. This study analyzes historical pricing data from multiple online stores to identify key pricing patterns and factors that influence price changes over time. The model developed in this study predicts game prices by incorporating features such as retail price, discount percentages, past price trends (lags), and other time-based features. The findings reveal that retail price and recent price trends (e.g., 7-day rolling averages) are the most influential features in predicting future prices. Additionally, discount strategies significantly impact game sales, with certain discount ranges showing higher effectiveness in driving consumer purchases. The model also demonstrates variability in prediction accuracy, particularly at higher price points, highlighting the challenges of capturing complex price fluctuations in a dynamic digital marketplace. The significance of this study extends to the Metaverse market, where pricing and the use of digital assets like non-fungible tokens (NFTs) play a critical role. The model's application could aid in optimizing pricing strategies within virtual economies, enhancing both the consumer experience and retailer profitability. Future work includes integrating additional features such as user reviews and exploring its application to Metaverse game platforms. The practical implications of this research are significant for online game retailers looking to leverage data-driven insights for more effective pricing and promotional strategies.
Co-Authors Bakar, Normi Sham Awang Abu Binti Abdul Rahim, Yusrina Fauzi, Muhammad Ashraf bin Fauri Guangfa, Wu Kowthalam, Vijay Rathnam Maidin, Siti Sarah Yilin, Li Zhaoji, Fu