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48. Understanding the Complexities: Interrelationships of Critical Barriers to University Technology Transfer in Vietnam Using T-Spherical Fuzzy MCDM Approach

Author

Thu-Hien Tran, Phi-Hung Nguyen, Lan-Anh Thi Nguyen, and Thu-Hoai Thi Nguyen

Subjects
MCDM, Delphi, DEMATEL, spherical fuzzy sets, T-spherical fuzzy sets, university technology transfer barriers, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

By implementing new technologies, university technology transfer (UTT) is crucial for economic advancement. However, developing countries face numerous challenges regarding UTT. This study proposes a unique approach by combining Multi-Criteria Decision Making (MCDM) methods such as Delphi and Decision-Making Trial And Evaluation Laboratory (DEMATEL) with Fuzzy Theories, specifically Spherical Fuzzy Sets (SFSs) and T – Spherical Fuzzy Sets (T-SFSs), to examine the barriers in UTT. The distinctive aspect of this study lies in addressing the limitations of SFSs and upgrading them to T-SFSs by incorporating them with the DEMATEL approach. Initially, SFS Delphi is employed to identify and eliminate nonimportant barriers. Subsequently, SF DEMATEL is utilized to classify barriers into cause-effect groups and determine their interactive relationships. However, SFSs have revealed specific weaknesses during this step, prompting the adoption of T-SF DEMATEL as a replacement. The findings highlight the perfect compatibility of T-SFSs with the research model and identify several primary barriers that hinder UTT, including C14, inconsistent rules and regulations, C9, misalignment between research and commercialization objectives, C3- lack of recognition for university-industry linkages, and C5- lack of resources. This study not only improves the understanding of UTT for decision makers, entrepreneurs, and policymakers, facilitating the creation of solutions, but also serves as a reference for researchers in the application of T-SFSs in other research domains.

Published
2024
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49. Optimizing Academic Certificate Management With Blockchain and Machine Learning: A Novel Approach Using Optimistic Rollups and Fraud Detection

Author

Khoa Tan-Vo, Khanh Pham, Phu Huynh, Mong-Thy Nguyen Thi, Thu-Thuy Ta, Thu Nguyen, Tu-Anh Nguyen-Hoang, Ngoc-Thanh Dinh, and Hong-Tri Nguyen

Subjects
Credential revocation management, smart contract, Layer-2, optimistic rollups, machine learning, fraud detection, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Blockchain technology has brought a significant advancement in the development of academic certificate management systems by enhancing security, transparency, and decentralization. However, challenges such as certificate revocation, transaction costs, and latency still persist. This research proposes a novel mechanism combining smart contracts and Optimistic Rollups technique to address these issues. By leveraging the off-chain processing feature of Optimistic Rollups, the research has significantly reduced transaction latency and costs in certificate revocation. This integration not only optimizes performance but also maintains transparency and data integrity on the blockchain. Moreover, integrating machine learning for fraud detection not only reinforces the security of the certificate management system but also provides timely alerts before fraudulent transactions occur. The combination of blockchain to ensure decentralization and security, along with machine learning to detect and prevent fraud, creates a comprehensive and advanced certificate management system. The experimental outcomes validate the effectiveness of Optimistic Rollups in certificate revocation, showing a notable approximately 61.92% reduction in both transaction costs and latency. Moreover, the machine learning model displays impressive performance, achieving high accuracy in detecting fraudulent users, with an average F1-score of 99.42% and an AUC score nearing perfection. These results underscore the comprehensive and advanced nature of the certificate management system.

Published
2024
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50. Advancing Scholarship Management: A Blockchain-Enhanced Platform With Privacy-Secure Identities and AI-Driven Recommendations

Author

Tu-Anh Nguyen-Hoang, Ngoc Cu Hoang, Phu Thien Hua, Mong-Thy Nguyen Thi, Thu-Thuy Ta, Thu Nguyen, Khoa Tan-Vo, Ngoc-Thanh Dinh, and Hong-Tri Nguyen

Subjects
Scholarship management, blockchain, scalability, zero-knowledge, zk-rollup, machine learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Traditional scholarship management systems are often marred by inefficiencies, a lack of transparency, and significant concerns regarding data privacy. These challenges hinder the equitable distribution of educational funds and obscure the scholarship allocation process. This study aims to address these issues by proposing a blockchain-based scholarship management platform. However, scalability issues are introduced along with blockchain-based solutions, for which zk-rollup offers a promising solution. The integration of Self-Sovereign Identity Zero-Knowledge Proof assures secure and private submission of scholarship applications, safeguarding student data while maintaining transparency in the verification process. Additionally, a machine learning model is employed to predict scholarship qualification, functioning as a recommendation system that identifies and prioritizes deserving students. This data-driven approach proactively eliminates barriers that potentially impede deserving students from accessing financial aid, such as administrative oversights or a lack of self-assurance in their qualifications. Our experimental findings confirm the effectiveness of zk-rollups in enhancing transaction efficiency, demonstrating a reduction in transaction confirmation time by approximately 63.6% and a decrease in transaction costs by nearly 90%. Besides, the machine learning model achieved a good performance rating, achieving a balanced accuracy of 86.75% and a mean average precision of 91.68% on a realistically imbalanced test set, reflecting real-world conditions.

Published
2024
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