Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
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This Author published in this journals
All Journal Journal of Current Research in Blockchain International Journal Research on Metaverse
Lenus, Latasha
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Discovering Co-Occurrence Patterns Among Blockchain Address Categories Using the FP-Growth Association Mining Algorithm Lenus, Latasha
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.24

Abstract

This paper focuses on identifying recurring patterns among blockchain address categories using the FP-Growth algorithm, which is known for its efficiency in mining frequent itemsets within large datasets. The study provides insights into blockchain ecosystem dynamics by analyzing category associations across different blockchain networks like Ethereum and Bitcoin. Through this analysis, significant patterns were found, such as the frequent co-occurrence of categories related to smart contracts and exchanges, highlighting the central role of these categories in blockchain interactions. Additionally, the study delves into the influence of data sources on detected patterns, revealing that various data collection methods contribute to distinct biases, which affect category associations. The findings offer practical applications for blockchain analytics, such as improving classification models, anomaly detection, and enhancing regulatory compliance. This study contributes to blockchain research by showcasing how association rule mining can improve the categorization and understanding of blockchain address behaviors. The use of FP-Growth, as opposed to more traditional methods, enables faster and more comprehensive analysis, which is particularly valuable given the extensive nature of blockchain datasets. The research also points to potential directions for future work, such as integrating temporal data to observe changes over time and exploring additional blockchain networks to broaden the scope of insights. The study emphasizes the need for continuous advancements in blockchain address analysis to support security, transparency, and regulatory initiatives within this rapidly evolving digital ecosystem.
Predicting Consumer Perceptions of Metaverse Shopping Through Insights from Machine Learning Models Lenus, Latasha
International Journal Research on Metaverse Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i3.17

Abstract

This study investigates consumer perceptions of Metaverse shopping and the factors that influence these perceptions, using machine learning models to classify and analyze the data. Four models—Logistic Regression, Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)—were employed to predict whether consumers view Metaverse shopping favorably or unfavorably. Among these, the SVM model achieved the highest performance, with an accuracy of 94.17%, precision of 97.14%, and an AUC-ROC score of 98.13%. These results indicate that machine learning can reliably classify consumer perceptions based on demographic and experience-related data. Furthermore, the Random Forest model was used to analyze the importance of features influencing consumer attitudes. The findings revealed that experience-related factors—such as interactivity, personalization, and consumer engagement—were more significant in shaping perceptions than product-specific attributes. The most important feature, MC2 (interactivity), contributed 23.6% to the model’s predictive power, highlighting the importance of user experience in driving positive sentiment. These insights suggest that businesses aiming to enter the Metaverse retail space should focus on enhancing the overall shopping experience to foster positive consumer perceptions. Machine learning models provide valuable tools for understanding consumer behavior and tailoring virtual shopping environments accordingly. This research offers a data-driven approach to predicting and understanding consumer perceptions of the Metaverse, providing actionable insights for businesses in this emerging market.
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