Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Bhavana Srinivasan
Department of Animation and Virtual Reality, JAIN, Bangalore, India
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Comparative Analysis of Ensemble Learning Techniques for Purchase Prediction in Digital Promotion through Social Network Advertising Andhika Rafi Hananto; Bhavana Srinivasan
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.7

Abstract

This study conducts a comprehensive comparative analysis of ensemble learning techniques for predicting user purchases in social network advertising. The ensemble methods evaluated include Random Forest, Gradient Boosting Machines (GBM), AdaBoost, and Bagging. The dataset, consisting of 7,000 records of user interactions with social network advertisements, was preprocessed to handle missing values, encode categorical variables, and standardize numerical features. Performance metrics such as accuracy, precision, recall, F1 score, and ROC AUC score were used to evaluate each model. The Random Forest model achieved an accuracy of 0.875, precision of 0.821, recall of 0.821, F1 score of 0.821, and ROC AUC score of 0.948. The GBM model also performed well, with an accuracy of 0.875, precision of 0.846, recall of 0.786, F1 score of 0.815, and ROC AUC score of 0.948. The AdaBoost model showed the highest performance, with an accuracy of 0.9, precision of 0.917, recall of 0.786, F1 score of 0.846, and ROC AUC score of 0.969. The Bagging model achieved an accuracy of 0.875, precision of 0.821, recall of 0.821, F1 score of 0.821, and ROC AUC score of 0.939. Feature importance analysis revealed that Age and Estimated Salary were the most significant predictors across all models. Hyperparameter tuning was crucial in optimizing each model's performance, ensuring they were neither too simple nor too complex. The study's findings underscore the effectiveness of ensemble learning techniques in social network advertising and provide valuable insights for marketers. Future research could explore larger and more diverse datasets, other ensemble methods, and the computational efficiency of these models. This research contributes to predictive analytics in marketing, enhancing the accuracy and effectiveness of advertising strategies.
Comparative Analysis of LightGBM and XGBoost for Predictive Risk Assessment in Blockchain Transactions within the Metaverse Bhavana Srinivasan
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.23

Abstract

The growing integration of blockchain technology within the metaverse has created an urgent need for effective systems to assess and mitigate transaction risks. This study investigates the use of machine learning models, specifically LightGBM and XGBoost, for predictive risk analysis in blockchain transactions. A dataset comprising 50,000 blockchain transactions, with 75% categorized as low-risk and 25% as high-risk, was used to evaluate the performance of these models across key metrics. LightGBM emerged as the superior model, achieving an accuracy of 91.2%, surpassing XGBoost's 89.5%. Additionally, LightGBM recorded an AUC-ROC score of 0.94, outperforming XGBoost’s 0.92. In terms of computational efficiency, LightGBM demonstrated clear advantages. It required only 80 seconds for training and 10 milliseconds per prediction, whereas XGBoost needed 120 seconds for training and 15 milliseconds for prediction. Feature importance analysis further highlighted the pivotal role of the Risk Score, which contributed 40% and 35% to the predictive power of LightGBM and XGBoost, respectively. Other significant features included Amount (USD) and Session Duration, showcasing the relevance of both behavioral and transactional data in risk prediction. These results underscore LightGBM's suitability for real-time risk assessment, making it a reliable and efficient tool for managing large transaction volumes in blockchain ecosystems. However, this study also acknowledges some limitations, including the imbalanced dataset and the static nature of the models, which may struggle with evolving transaction patterns. Future research could address these challenges by employing advanced resampling techniques to balance the dataset, incorporating additional contextual features, and developing adaptive models capable of handling dynamic risk profiles. Through these advancements, this research contributes to the foundation for scalable and secure risk assessment systems, fostering trust in blockchain-based metaverse applications.
Navigating Financial Transactions in the Metaverse: Risk Analysis, Anomaly Detection, and Regulatory Implications Bhavana Srinivasan; Tri Wahyuningsih
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.5

Abstract

Blockchain technology has emerged as a disruptive force in the realm of finance, offering decentralized and transparent mechanisms for conducting financial transactions. This paper explores the landscape of blockchain-based financial transactions, focusing on risk analysis, anomaly detection, regulatory frameworks, and ethical considerations. Drawing on interdisciplinary insights from finance, computer science, economics, law, and ethics, the study investigates the opportunities and challenges presented by blockchain finance. Leveraging quantitative analysis, machine learning algorithms, case studies, and regulatory reviews, the research sheds light on the complexities of blockchain ecosystems. Key findings include the importance of robust risk management strategies, the role of anomaly detection in safeguarding financial integrity, and the evolving regulatory landscape surrounding blockchain transactions. The study identifies gaps in current research and proposes avenues for future investigation, emphasizing the need for interdisciplinary approaches to address the multifaceted challenges of blockchain-based finance. Ultimately, this research aims to inform stakeholders about the implications of blockchain technology in financial transactions and foster responsible innovation and sustainable development in digital finance ecosystems.
Co-Authors Andhika Rafi Hananto Tri Wahyuningsih