Blockchain Papers

Aurangzeb Khan, Nasrullah Khan, Muzzammil Siraj

No abstract is available for this record.

มนธรรม การย์บรรจบ

NFT (Non-Fungible Token) คือสินทรัพย์ดิจิทัลที่มีลักษณะเฉพาะตัว ไม่สามารถทดแทนกันได้ และสามารถซื้อขายผ่านเทคโนโลยีบล็อกเชนซึ่งทำหน้าที่จัดเก็บข้อมูลและยืนยันความเป็นเจ้าของสินทรัพย์ดิจิทัล ปรากฏการณ์ NFT ได้รับความสนใจอย่างแพร่หลายในช่วงปี ค.ศ.2020–2021 และส่งผลให้การสร้างสรรค์งานศิลปะดิจิทัลในรูปแบบ NFT กลายเป็นช่องทางใหม่ของศิลปินร่วมสมัย งานวิจัย แนวทางการสร้างสรรค์งานศิลปะเพื่อการขายในช่องทาง NFT มีวัตถุประสงค์เพื่อศึกษาแนวคิด กระบวนการสร้างสรรค์และการนำเสนอผลงานศิลปะดิจิทัลในรูปแบบ NFT ให้สอดคล้องกับความต้องการของกลุ่มเป้าหมายและสภาพการณ์ทางการตลาดในปัจจุบัน รวมถึงศึกษาโครงสร้างของตลาด NFT กระบวนการสร้าง การซื้อขายผลงาน และทำความเข้าใจสถานการณ์วงการ NFT ในประเทศไทยปัจจุบัน การวิจัยใช้ระเบียบวิธีวิจัยเชิงคุณภาพ โดยเก็บข้อมูลจากการสัมภาษณ์เชิงลึกศิลปิน NFT ชาวไทยที่มีชื่อเสียง จำนวน 6 ราย ซึ่งคัดเลือกแบบเฉพาะเจาะจงตามเกณฑ์ยอดขายผลงานและจำนวนผู้ติดตามบนสื่อสังคมออนไลน์ ผลการวิจัยพบว่า การสร้างสรรค์ผลงาน NFT ที่สอดคล้องกับตลาดจำเป็นต้องให้ความสำคัญกับเอกลักษณ์เฉพาะตัว คุณภาพผลงาน และการนำเสนออย่างสม่ำเสมอ ขณะที่โครงสร้างตลาด NFT มีลักษณะเป็นตลาดแข่งขันสมบูรณ์ในระดับแพลตฟอร์ม และตัวผลงาน NFT เป็นสินทรัพย์ดิจิทัลเฉพาะที่ไม่สามารถทดแทนได้ นอกจากนี้ สถานการณ์วงการ NFT ในประเทศไทยปัจจุบันอยู่ในช่วงชะลอตัว แต่ยังคงมีศักยภาพในการพัฒนาและเติบโตในอนาคตภายใต้การปรับตัวของศิลปินและบริบททางเทคโนโลยีร่วมสมัย

Dongwu Lin

This paper develops computational methods for optimizing revenue recognition in machine learning platforms operating on cloud computing infrastructure. We analyze how Artificial Intelligence as a Service (AIaaS) platforms leverage distributed computing architectures, containerization technologies (Docker, Kubernetes), and microservices patterns to deliver AI capabilities, creating complex revenue recognition challenges under IFRS 15. Our research employs algorithmic analysis to examine five critical technical challenges: (1) computational resource allocation tracking across multi-tenant cloud environments, (2) real-time transaction price determination using usage metering APIs and consumption-based billing algorithms, (3) automated revenue allocation across platform components using distributed ledger technologies, (4) temporal revenue recognition optimization through event-driven architectures and streaming data processing, and (5) network effect quantification using graph algorithms and data analytics.

Surinder Singh Khurana, Parvinder Singh, Naresh Kumar Garg

No abstract is available for this record.

Sanjana Lakkimsetty, Chennu Aryan Karthikeya, Lakshmi Prasanna Kumar Jetti, Swetha Ghanta · 5 authors

Non-Fungible Tokens (NFTs) represent a revolutionary class of digital assets, characterized by their uniqueness and value derived from metadata and visual traits. However, NFT markets suffer from volatility and a lack of transparent valuation systems, making it difficult for collectors and investors to estimate asset worth. This paper presents a comprehensive machine learning pipeline for predicting the market value of NFTs based on trait rarity and sale metadata. We apply rigorous preprocessing, compute rarity scores from trait distributions, and compare multiple regression models, including Random Forest, LightGBM, CatBoost, and Extra Trees. Our analysis demonstrates that tree-based models significantly outperform simpler regressors, with Extra Trees achieving the lowest RMSE of 136.91 and the highest$\mathrm{R}^{\mathrm{2}}$score of$\text{1. 0}$. Visual and statistical analyses further validate the effectiveness of our methodology in predicting NFT prices with high precision.

Ziyu Liu

Total Value Locked (TVL) explicitly reflects the total asset users deposit in Decentralized Finance (DeFi) protocols, similarly to the Asset Under Management (AUM) in traditional finance. This exposes liquidity providers to the risk of short-term liquidity depletion and highlight the urgent need for quantifiable and predictive risk management tools. As its short-term fluctuations can be effectively characterized by on-chain static features (e.g., fee tier, volatility), dynamic features (e.g., token balance changes, slippage), and technical indicators (e.g., MA), and since posterior calibration methods based on high-accuracy point forecasting models can provide more reliable estimations of downside risk boundaries, this study focuses on the USD Coin - Ethereum pool (0.3 % fee tier) of Uniswap V3. The model is constructed using 17 features, with eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine employed for TVL growth-rate regression forecasting. The results are compared with those from Long Short-Term Memory, Gated Recurrent Unit, and Naïve baselines. Furthermore, the research introduces the Liquidity-at-Risk (LaR95) metric to estimate downside risk through both residual-based and quantile regression approaches, and conduct interpretability analysis using SHAP values. XGBoost obviously outperforms Deep learning models on directional accuracy. XGBoost demonstrates a significantly superior performance to deep learning models in predicting the direction of TVL changes. The residual-based LaR95(liquidity-at-risk at the 95% confidence level) derived from its point forecasts exhibits a coverage rate closely aligned with the theoretical level, validating the effectiveness, robustness, and interpretability of the “high-accuracy prediction and residual calibration” framework in DeFi risk management.

Jinghua Yu, Jie Ding, Yunpeng Liu, Xiao Han

In recent years, deep learning (DL) has shown remarkable performance in smart contract vulnerability detection, with graph neural networks (GNNs) serving as a key technique for learning structured code representations. However, existing graph-based approaches suffer from semantic fragmentation, noisy node interference, and weak semantic alignment, which limit detection robustness and deployment efficiency. To address these challenges, we propose UCGVulDetector, a unified and efficient framework designed for smart contract security in blockchain-based communication systems. It consists of three modules: (1) Structural Simplification (UDP): a hierarchical pruning strategy that refines abstract syntax trees by removing redundant nodes while preserving key semantics; (2) Graph Information Enhancement (UGSF): constructing heterogeneous graphs from Solidity ASTs and integrating control-flow, data-flow, and state-slot-chain (SSC) relations to capture multi-dimensional semantics; and (3) Graph Encoding and Alignment (PGE+TSCC): employing a pairwise graph encoder combined with temperature-scaled contrastive learning to align vulnerability semantics in a shared latent space. These modules collaboratively unify structural and semantic information to enhance feature representation. Experiments on the SolidiFI and MESSI datasets demonstrate that UCGVulDetector achieves F1-score improvements of 10.53%$\mathbf{1 1. 5 0 \%}$%ver state-of-the-art methods, delivering more accurate and robust vulnerability detection performance.

Jiarong Lu, Bin Liao, Yi Liu, Kutorzi Edwin Yao

Ethereum has become a significant trading platform for financial activities such as Dapps, ICOs, and DeFi. However, it has also become a hub for criminal activities such as fraud, money laundering, and illicit fundraising. The construction of fraud detection models employing machine learning techniques is currently a mainstream research direction. Nevertheless, existing studies face significant challenges, including class imbalance in data samples and a lack of model interpretability. In this content, this work proposes a novel explainable model for Ethereum illicit account detection, ETHIAD (Ethereum Illicit Account Detection). Firstly, we pre-process the dataset by ADASYN oversampling and Lasso feature selection, etc., to more efficiently achieve feature modeling of transaction structures. Then, the ETHIAD model is trained using the XGboost algorithm, with an accuracy, precision, recall, F1 score, and AUC value of 99.70%, 99.51%, 99.02%, 99.26%, and 99.45%, respectively, the model outperforms the existing SOTA model by 0.05%-1.1%. Finally, we introduce SHAP framework to analyze the key influencing factors of illicit accounts from multiple perspectives, and the conclusions strongly enhance the explainability of the model.

Просолов, Владислав, Кушнерьов, Олександр, Сокол, Владислав, Трофименко, Руслан

Topicality. Fraudulent activities on the Ethereum blockchain pose a substantial risk to decentralized finance and require capable models not only to respond to already detected abuses but also to identify suspicious accounts proactively before losses escalate. The subject of study is the application of graph and temporal neural models to the task of classifying Ethereum accounts as benign or fraudulent, considering the structural relationships between addresses and the temporal dynamics of transactions. The purpose of this article is to develop and experimentally evaluate a neural architecture based on a multilayer perceptron as a baseline component for the subsequent integration of graph and temporal mechanisms, and to analyze its performance on the open Ethereum Fraud Detection dataset, which features a high-class imbalance. The following results were obtained. A baseline deep model for binary account classification was constructed using feature preprocessing, stratified data splitting, class weight balancing, L2 regularization, Dropout, and early stopping, which enabled the achievement of an ROC AUC value of approximately 0.98 under conditions of a pronounced dominance of the safe class. A detailed analysis of the confusion matrix and the precision, recall, and F1 metrics demonstrated an acceptable trade-off between reducing false positives and minimizing the proportion of missed fraudulent accounts, which is critical for real-world financial scenarios. Conclusion. The results indicate that a properly designed baseline neural model on tabular features can ensure high-quality proactive identification of fraudulent Ethereum accounts and serve as a starting point for further integration of graph and temporal architectures aimed at improving interpretability and robustness to the evolution of malicious behavior patterns.

Vladyslav Prosolov, Oleksandr Kushnerov, Vladyslav Sokol, Ruslan Trofymenko

Topicality. Fraudulent activities on the Ethereum blockchain pose a substantial risk to decentralized finance and require capable models not only to respond to already detected abuses but also to identify suspicious accounts proactively before losses escalate. The subject of study is the application of graph and temporal neural models to the task of classifying Ethereum accounts as benign or fraudulent, considering the structural relationships between addresses and the temporal dynamics of transactions. The purpose of this article is to develop and experimentally evaluate a neural architecture based on a multilayer perceptron as a baseline component for the subsequent integration of graph and temporal mechanisms, and to analyze its performance on the open Ethereum Fraud Detection dataset, which features a high-class imbalance. The following results were obtained. A baseline deep model for binary account classification was constructed using feature preprocessing, stratified data splitting, class weight balancing, L2 regularization, Dropout, and early stopping, which enabled the achievement of an ROC AUC value of approximately 0.98 under conditions of a pronounced dominance of the safe class. A detailed analysis of the confusion matrix and the precision, recall, and F1 metrics demonstrated an acceptable trade-off between reducing false positives and minimizing the proportion of missed fraudulent accounts, which is critical for real-world financial scenarios. Conclusion. The results indicate that a properly designed baseline neural model on tabular features can ensure high-quality proactive identification of fraudulent Ethereum accounts and serve as a starting point for further integration of graph and temporal architectures aimed at improving interpretability and robustness to the evolution of malicious behavior patterns.

Junchao Zhang, Yaohui Zhong, Huanchun Wei, Jiahui Huang · 8 authors

Smart contract vulnerabilities have led to massive losses in digital assets. While researchers have proposed numerous detection methods utilizing static analysis, fuzzing, and deep learning, most are limited to identifying vulnerabilities within individual contracts. Consequently, these approaches fail to effectively analyze cross-contract interactions via external function calls, resulting in false negatives and positives. To address these limitations, we present VulCrosser, a deep learning method tailored for vulnerability detection in contract interaction scenarios. VulCrosser enables comprehensive risk assessment by analyzing function call chain traces. Specifically, it constructs a Cross-Contract Dependency Graph (CCDG) to effectively model inter-contract dependencies, network dynamics, and interaction semantics. It then employs a heterogeneous graph neural network with a two-level attention mechanism to extract and integrate complex features from the graph, ultimately achieving accurate risk assessment. We evaluated VulCrosser on three common vulnerabilities: reentrancy, timestamp dependency, and transaction state dependency. Experimental results show that VulCrosser outperforms all baseline methods, improving detection accuracy by 5.04%, 4.39%, and 5.09%, and $F 1$ scores by $4.93 \%, 4.60 \%$, and 4.99%, respectively.

Zulian Wahid, Suryo Adhi Wibowo, Andry Alamsyah

The growth of Decentralized Finance (DeFi) demands advanced fraud detection, yet current methods face a trade-off: tabular models lack semantic understanding, while language models like RoBERTa struggle with structured data and high computational costs. This paper introduces a novel pipeline that transforms structured Ethereum transaction data into natural language sentences, enabling a standard RoBERTa model to analyze financial behavior efficiently using Gradient Accumulation. A stratified 5-fold cross-validation on a public dataset revealed a key performance trade-off: while Random Forest achieved the highest F1-Score (0.796), our RoBERTa GA model proved superior in the critical metric of Recall (0.769). This finding validates our semantic approach not merely as a competitive alternative, but as a strategically advantageous method when the primary goal is minimizing missed fraudulent transactions. Our work confirms the viability of applying NLP to blockchain security and provides a foundation for future language-model-driven monitoring systems.

Ambica Sethy, Abhishek Ray

No abstract is available for this record.

Ruoyu Bai, Meng Wang, Wanqing Liu, Liang Wang

In recent years, deep learning has been widely applied in smart contract vulnerability detection due to its automatic feature extraction and strong generalization capabilities. However, existing methods still face challenges such as redundant information in graph structures, insufficient utilization of data flow information, and single-scale feature extraction. To address these issues, we propose a function-level smart contract graph representation, namely the Multi-relational Semantic Graph (MSG), which employs various types of data flow edges to represent data dependency information within contracts. Subsequently, we introduce a detection model, REA_DCN, which combines a Residual Multi-scale Dilated Convolutional Network with a Multi-head Attention mechanism to capture syntactic and semantic features in the MSG. The model comprises two key modules: the Residual Multi-scale Dilated Convolutional Network (RE_DCN) can extract node features from three different dimensions, while the Multi-head Attention Network (MEA) is utilized for edge feature extraction. Experimental results on real-world datasets demonstrate that the highest score of REA_DCN in terms of accuracy, precision, recall and F1 score exceeds 97%, proving its effectiveness and feasibility.

Gerry Nevile Kurnia, Irni Yunita, Andry Alamsyah

Blockchain-based decentralized finance (DeFi) is a major financial innovation, enabling transparency and inclusion through programmable rails. The transition to DeFi 3.0 defined by cross-chain interoperability, multichain ecosystems, and tokenized real-world assets (RWAs) broadens functionality yet introduces potential systemic vulnerabilities. Prior research often treats protocol exploits or single risk families in isolation, leaving no unified lens connecting DeFi risks to financial resilience. This study develops a unified DeFi 3.0 risk taxonomy and maps it to resilience capacities. Using a three-lane systematic literature review (peer-reviewed, grey literature, preprints; 2021-2025; 43 sources), we identify twelve risk domains in three categories: technology and data infrastructure; market and economic; and governance, legal, and operational. We then assess resilience along three capacities absorptive (stablecoins, automated market makers/AMMs, insurance), adaptive (regulatory alignment, RWA tokenization, AI integration), and transformative (transparency, inclusion, ESG alignment). The resulting framework operationalizes resilience theory via this taxonomy, providing a structured reference for regulators, developers, and scholars to support innovation while strengthening systemic stability.

Neeraj Kumar, Kunal Abhishek

No abstract is available for this record.

Sanidhya Vishal Sharma, Swati Joshi

Behavioral finance has emerged as a critical framework for understanding market dynamics beyond traditional rational agent models. This research presents a comprehensive multimodal approach to behavioral finance analysis, integrating market data, macroeconomic indicators, news sentiment, cryptocurrency metrics, Web3 analytics, GitHub development activity, and social sentiment to test five advanced hypotheses regarding behavioral pattern identification and market anomaly detection. The study employs an ultra-comprehensive data pipeline processing 30,400 samples across seven distinct data sources, generating 91 engineered features representing behavioral biases, investment patterns, and market psychology. Advanced machine learning techniques including Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding, Variational Autoencoders, K-Means, Hierarchical Clustering, DBSCAN, Isolation Forest, One-Class SVM, and Elliptic Envelope are applied to identify behavioral structures and detect anomalies. Statistical validation through chi-square tests, ANOVA, Granger causality analysis, and lagged correlation studies demonstrates that three of five hypotheses (60%) achieve statistical significance at p < 0.05. Key findings reveal that behavioral structures exist and correspond to canonical biases (chi-square = 3406.780, p < 0.001), cluster assignments maintain moderate stability across market regimes (Jaccard similarity = 0.300), and sentiment and macroeconomic factors exhibit 65 significant causal relationships with behavioral patterns. However, multimodal data integration does not uniformly improve clustering quality (Silhouette score decrease of 0.116), and cluster-conditioned anomaly detection fails to outperform global methods (F1-score decrease of 0.017). These findings contribute to behavioral finance theory while providing practical applications for investment management, fraud detection, and regulatory compliance.

Jennifer Bala, Sikiru O. SUBAIRU, Noel M. DOGONYARO, Joseph A. OJENIYI · 5 authors

Blockchain technology, particularly Ethereum, has revolutionized decentralized finance by enabling transparent, secure, and programmable smart contracts. However, these same features have created avenues for financial crimes such as Ponzi schemes, where fraudulent actors exploit pseudonymity and the absence of centralized oversight to deceive investors. This study develops an optimized hybrid detection model that combines eXtreme Gradient Boosting (XGBoost) and Gated Recurrent Units (GRU) to identify Ponzi schemes in Ethereum transaction networks. The model integrates XGBoost’s capability for structured feature learning with GRU’s temporal sequence modeling to capture both static and dynamic behavioral patterns of smart contracts. Using a dataset of 3,866 labeled Ethereum contracts obtained from Kaggle, the research employed advanced preprocessing, temporal sequence enrichment, and class balancing through SMOTE-TS to mitigate data imbalance. Bidirectional optimization, incorporating attention-enhanced GRUs and Bayesian hyperparameter tuning for XGBoost, further improved learning performance and generalization. The model was evaluated using precision, recall, F1-score, ROC-AUC, and PR-AUC, achieving higher detection accuracy of 99% (F1-score = 0.945, ROC-AUC = 0.983) than standalone XGBoost or GRU models. Results demonstrate the hybrid model’s superior ability to detect temporal and statistical anomalies, reducing false negatives and improving early detection of fraudulent contracts. The approach contributes a scalable and interpretable framework for real-time Ponzi detection in blockchain ecosystems. This research not only enhances the reliability of Ethereum’s financial ecosystem but also offers regulators and developers a novel tool for proactive fraud prevention. Future work could extend this framework to multi-chain detection systems and real-time forensic monitoring.

Harshith Sai Veeraiah, Syed Badruddoja, Ram Dantu

Smart contract vulnerabilities hinder the development of decentralized finance (DeFi) applications due to overarching attacks and their impact on financial transactions. While rule-based static analysis tools can detect common exploits, they often fail to uncover subtle or rapidly evolving vulnerabilities. Moreover, dynamic analysis techniques over-rely on patterns, limiting token representation and explainability of attack detection. We introduce a novel architecture that unites Abstract Syntax Trees (ASTs) with a transformer-based deep learning framework to improve the detection of vulnerable smart contracts. By encoding Solidity-based smart contracts into ASTs, the structural context essential for capturing complex code dependencies is retained. Furthermore, the transformer model captures the context, dependencies, and semantics of vulnerabilities. Our performance evaluations show that the AST-transformer-based vulnerability detection method improved the detection rate precision by 4% compared to RNN, LSTM, GNN and vanilla transformer-based detection techniques. Additionally, we use SHapley additive explanation to determine the contribution of each to explain and reason the vulnerability detection. Moreover, we use saliency maps (heatmaps) to identify the line of code that is attributed to vulnerability detection.

Adedeji Daniel Gbadebo

This research examines deep-learning and machine-learning models for cryptocurrency price prediction, with a keen focus on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Solana (SOL). Cryptocurrencies exhibit high volatility, non-linear behavior and are able to react strongly to exogenous events, making their prediction and forecasting challenging. The primary aim of this research is to determine which predictive models yield optimal performance in characterizing these complexities and to provide empirical guidance on real-life investment and risk-management applications. Four approaches were used for this forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a combination of LSTM-GRU models, and Stochastic Gradient Descent (SGD) regression. The daily historical data were used to train and test each model on different forecast horizons, and performance was measured accordingly by Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. As shown in the results, it can be observed that GRU exhibited the lowest error rates in the majority of the assets, particularly in short-term predictions. LSTM demonstrated a promising ability to capture long dependencies, whereas the hybrid LSTM-GRU system showed a similar performance proficiency by combining the relative superiorities of the two respective models. On the other hand, the conventional SGD regression was the worst among all the deep-learning algorithms, thereby demonstrating the extreme capability of these algorithms in modelling non-linear time sequences. The results confirm GRU as the most viable model for AI-powered crypto prediction and demonstrate the potential of hybrid architecture, at least in certain situations. This study will contribute to the existing debates about the role of deep learning in predicting financial outcomes and provide valuable insights to traders, analysts, and researchers navigating the uncertainties of the digital asset world.

Shiva Johri, Amit Verma, Megha Sharma, N. Deepalakshmi

Cloud-based centralized credit scoring is accurate but suffers from network delays, high bandwidth costs, privacy concerns, and non-transparent decision-making processes. We present an edge intelligent finance framework that combines shallow neural models with symbolic, rule-based reasoning to make real-time, transparent loan decisions in the wild over low-cost edge devices. Trained via federated learning on popular benchmarks (German Credit, LendingClub, FICO), the framework achieves competitive discrimination performance (AUC ≈ 0.83) while reducing inference latency to 50ms and energy use to 0.3J/inference compared to their centralized deep models. An interpretable validation layer is proposed to encode fairness constraints and regulation rules, which outputs the interpretable rationales and group-gap metrics (<0.05). Under bandwidth constraints, stress tests indicate that accuracy and response times remain stable, as inference is performed locally. Our results demonstrate that under edge AI, efficient lending workflows and peer-to-peer ecosystems can be enhanced with privacy, fairness, and compliance through a series of information yellow-red-green cycles. We also address scalability, rule complexity, and deployment recommendations for financial institutions and regulators.

Geol Gladson Battu, Mukul Mangla, Ritesh Gupta, Vivek Banerjee · 6 authors

The authors presented a blockchain-backed cloud artificial intelligence (AI) system for smart contract risk assessment in decentralized finance (DeFi) that replaces the coarse-grained structure-awareness of graph neural networks (GNNs) with fine-grained structure-awareness while preserving contextually-rich interactions between nodes that are similar to transformer encoders. The program is able to identify a wide range of vulnerabilities and assign unambiguous risk rankings to smart contracts. People can be educated about decentralized finance without sacrificing their privacy through the use of a method called “federated learning.” This is just a single component of the job. The platform is constructed on top of a robust and adaptable cloud platform that utilizes on-chain risk evaluations that are anchored to ensure that they are able to be audited. The system has been proven to be accurate, fast, and simple to relocate based on a large number of simulations and real-world measurements of prohibited traffic behaviors, delay analysis, and economic consequences. The research also makes official the automatic and reliable risk analysis in decentralized finance, which makes the DeFi ecosystems significantly safer and more open.

Francisco Resende, Daniel Costa, Pedro Granate, Armando Teixeira · 8 authors

Non-Fungible Tokens (NFTs) are unique digital assets whose valuation presents a significant challenge due to their non-fungibility, low liquidity, and subjective features. This paper presents a machine learning-based approach to intra-collection NFT valuation using LightGBM, a gradient boosting model. The model was trained on historical sales, metadata, floor prices, and temporal dynamics across six prominent NFT collections. Our approach outperforms traditional valuation baselines, including floor price heuristics, rarity scores, and trait valuation models, achieving significantly lower prediction error (MAPE). The study demonstrates the potential of advanced ML models in enhancing valuation accuracy at a token-level for non-floor assets, with applications in NFT marketplace pricing, portfolio/NAV marking or NFT specialised lending.

Rawan Ghnemat, Hatem Mosa

No abstract is available for this record.