CCF-C
【影响因子】8.665
【主题类别】
区块链技术-协同技术-机器学习
【Abstract】Bitcoin's volatile nature has made its price prediction a sought-after mathematical model in the FinTech industry. Existing studies, however, need to look into the critical aspect of time-lagged sentiment in Bitcoin price forecasting. This omission is significant because time-lagged sentiment captures delayed market reactions that are not immediately apparent in price movements. Moreover, the correlation between time-lagged sentiment and technical indicators and the limitations of individual machine learning and deep learning models necessitates a comprehensive approach for accurate and reliable Bitcoin price predictions. This paper introduces the multimodal fusion Bitcoin (MFB), an innovative generalized multimodal fusion approach that effectively integrates BiLSTM and BiGRU layers for complex feature extraction. The model employs the BorutaShap algorithm for feature selection and utilizes attention mechanisms and spatial dropout for optimization and generalization. MFB's training and validation use news and tweet data combined with Bitcoin technical indicators to explore the impact of time-lagged sentiment on price movements, leading to more accurate and timely market predictions. The MFB performs superior Bitcoin prediction performance, achieving 97.63% accuracy and an MAE of 0.0065. Experiments highlight MFB's capability to outperform existing models, offering significant insights for investors in making informed decisions. MFB's innovative methodology, particularly in next-hour Bitcoin price forecasting, marks an advancement in financial forecasting. By capturing the nuanced dynamics of market sentiment and its delayed effects, MFB is a pioneering multimodal fusion approach in the FinTech domain, revolutionizing Bitcoin price prediction.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Bitcoin price prediction; Sentiment analysis; Multimodal fusion; Feature optimization; BiLSTM and BiGRU; Tweets data
【发表时间】2025
【收录时间】2024-11-01
【文献类型】 理论模型
【影响因子】5.711
【主题类别】
区块链应用-实体经济-营销领域
【Abstract】With the availability of enormous amounts of data come the difficulties of big data, privacy, and ransomware assaults, which result in Marketing fraud and spam. Blockchain offers an extensive array of possible applications in the Marketing field. Nevertheless, both Marketing research and practice exhibit a degree of hesitance toward using Blockchain technology and have not yet come around to completely understand and adopt the technology. Here, the aim is to examine the Blockchain concepts and their applications in Marketing through bibliometrics, network, and thematic analyses, which can provide several novel insights and perspectives into current research trends in this field by evaluating the most significant and cited research publications, keywords, institutions, authors' collaboration network, and finally countries that promote Industry 5.0 (I5.0) businesses. This study performs a detailed bibliometric and thematic-based Systematic Literature Review (SLR) on 124 of over 15000 research papers. Major outcomes include the identification of emerging themes such as the role of Blockchain in advertising, and dynamic pricing, as well as the need for further exploration of underdeveloped areas (e.g., consumer behavior and brand equity). The results contribute to theoretical and practical management elements and provide the groundwork for future study in this area. The overarching target of this research is to give a complete overview of applications and emerging trends of Blockchain technology in Marketing, thereby serving as a resource for future research topics for Marketing scholars and experts aiming to implement solutions based on Blockchain technology and algorithms to develop an I5.0 business.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchain; Internet of Things; Smart contract; Marketing; Systematic literature review; Network analysis
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 综述
【Author】 Duong, Cong Doanh Nguyen, Thanh Hieu Ngo, Thi Viet Nga Tran, Quang Yen Nguyen, Minh Hoa Pham, Thi Thu Phuong
【影响因子】4.643
【主题类别】
区块链应用-实体经济-食品领域
【Abstract】PurposeThis research applies the stimulus-organism-behavior-consequence framework to explore how blockchain-enabled traceability influences trust in organic food producers and retailers, which impacts consumers' purchase behaviors and subsequent outcomes.Design/methodology/approachUsing a purposive sample of 5,326 Vietnamese consumers, multiple linear and polynomial regression with response surface analysis were employed to examine the hypotheses.FindingsBlockchain-enabled traceability significantly enhances trust in both producers and retailers, which congruently and incongruently influences organic food purchase behaviors. This behavior also drives consumers' word-of-mouth and repurchase intentions. Serial mediation analysis confirms blockchain's impact through trust and purchase behaviors.Research limitations/implicationsStakeholders should adopt blockchain to boost transparency and trust, which increases consumer engagement. Policymakers can support this transition through regulations and incentives to enhance food security and sustainability.Originality/valueThis study expands on blockchain research by applying the stimulus-organism-behavior-consequence framework in the organic food supply chain, showing how blockchain-enhanced trust synergistically affects consumers' purchase behaviors, word-of-mouth and repurchase intentions.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Stimulus-organism-behavior-consequence model; Blockchain-enabled traceability; Organic food purchase behaviors; Trust in organic food producers; Trust in organic food retailers; Word-of-mouth intentions toward organic foods; Organic food repurchase intentions
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 实证数据
【影响因子】4.217
【主题类别】
区块链治理-技术治理-交易预测
【Abstract】This paper studies the Bitcoin volatility forecasting performance between popular traditional econometric models and machine learning techniques. We compare the 1-day to 2-month ahead forecasting performance of the Long Short-Term Memory (LSTM) and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM) model to the traditional models. We find that neural networks outperform Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models for all forecasting horizons. Furthermore, the LSTM model outperforms the Heterogeneous Autoregressive (HAR) model and by integrating the Markov Transition Field (MTF) into the CNN-LSTM model, we achieve superior forecasting results in the short-term, particularly for the 7-day forecasts.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Bitcoin; Volatility forecasting; Machine learning
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 案例研究
【Author】 Ran, Limin Shi, Ziqi Geng, Hanxiao
【影响因子】3.476
【主题类别】
区块链应用-实体经济-物流领域
【Abstract】Blockchain technology offers significant potential for enhancing efficiency in logistics operations by providing a decentralized and immutable ledger for tracking goods. This technology ensures transparency and traceability, enabling real-time visibility into the movement of products across the supply chain. By automating transactions and reducing the need for intermediaries, blockchain reduces administrative costs and minimizes delays. Our integration of Convolutional Neural Networks (CNNs) and Ant Colony Optimization (ACO) with blockchain has shown to improve delivery efficiency by 15%, reduce inventory management errors by 12%, and enhance fraud prevention mechanisms by 20%. Route optimization was improved by 18%, resulting in higher customer satisfaction rates and overall administrative efficiency. Additionally, data security was significantly bolstered, reducing instances of data breaches by 25%. Smart contracts further streamline processes by automatically executing predefined conditions, ensuring compliance and reducing errors. Enhanced data integrity and security mitigate risks of fraud and theft, fostering trust among stakeholders. Overall, the integration of blockchain in logistics promotes more efficient, reliable, and cost-effective supply chain management.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchains; Security; Industries; Costs; Real-time systems; Fraud; Supply chain management; Logistics; Smart contracts; Blockchain technology; logistics operations; supply chain management; smart contracts; transparency and traceability
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 实验仿真
【影响因子】3.476
【主题类别】
区块链应用-虚拟经济-数据要素
【Abstract】Data plays a role of great importance in research, and making it open means freedom to use, reuse, or redistribute it. For research purposes, science has been called to be more open and adhere to the FAIR principles (Findable, Accessible, Interoperable, Reusable), resulting in a growing trend of open research data. However, there are limitations to openness when working with sensitive data, one being the challenge of dealing with third parties, such as the marketplace intermediating the file transfer. In this work, we propose a blockchain FAIR research data marketplace that protects author rights, and we prove its feasibility and impact in fighting plagiarism. Our innovative approach consists of using the blockchain not only as a source of truth for author rights, access control to data, traceability of the data, and monetization but also for leaving the authors in total control of their data. Using smart contracts, we eliminate the need for a third party to mediate the dataset exchange and give authors autonomy over their data. We mix concepts such as hash, TLSH, and watermarking to provide proof of the data, a level of similarity between different datasets, and a way of identifying the sources of data misuse when shared with multiple individuals.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Open data; smart contracts; data protection; plagiarism detection; author autonomy; smart contracts; data protection; plagiarism detection; author autonomy
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 案例研究
【影响因子】3.476
【主题类别】
区块链治理-技术治理-地址分类
【Abstract】Cryptocurrencies have increasingly been used as a medium for illicit financial activities by criminals. Annually, billions of dollars' worth of Bitcoin penetrate cryptocurrency exchanges. Despite the critical need for advanced Bitcoin financial forensics to investigate these criminal activities, no novel methods have been developed to detect illicit Bitcoin operations. Existing approaches to identifying illegal Bitcoin activity are limited due to their inadequate consideration of graph data. To address these limitations, we present a novel approach, Hyperedge Classification, to detect illegal transactions with greater precision. This approach introduces a novel cluster-based Hyperedge-Node Switching technique, which enables effective hyperedge classification and visualization of hyperedge relationships. Additionally, we propose a framework named CENSor (Cluster-based Edge Node Switching Detector), which offers more powerful and robust detection capabilities compared to traditional techniques for both illegal entity detection and illegal transaction detection. Our cluster-based Hyperedge-Node Switching technique demonstrates its effectiveness with an F1-score of 0.867, outperforming comparative baselines. Moreover, CENSor visualizes the Bitcoin cluster graph and the Hyperedge-Node switched graph, highlighting the importance of utilizing appropriate graph information in Bitcoin analysis. Finally, we demonstrate that CENSor is resilient to an adversarial attack aimed at evading detection.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Bitcoin; Switches; Image edge detection; Forensics; Feature extraction; Visualization; Scalability; Cryptocurrency; Detection algorithms; illicit entity detection; hypergraph; graph neural network
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 案例研究
【Author】 Ning, Weiguang Zhu, Yingjuan Song, Caixia Li, Hongxia Zhu, Lihui Xie, Jinbao Chen, Tianyu Xu, Tong Xu, Xi Gao, Jiwei
【影响因子】2.838
【主题类别】
区块链技术-协同技术-去中心化机器学习
区块链技术-协同技术-联邦学习
【Abstract】Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state of the art. Various applications of federated learning based on blockchain are introduced. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development in the field.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】blockchain; federated learning; blockchain-based federated learning; distributed machine learning
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 综述
【DOI】 10.3390/app14209459
【影响因子】2.731
【主题类别】
区块链治理-市场治理-风险管理
【Abstract】Bitcoin adoption as a legal tender threatens a financial crisis because of the lack of regulatory frameworks and systems for exchanging Bitcoin into local currencies. This study analyzes monthly data from 2010 to 2022 using a structural vector autoregressive model, estimating Bitcoin's pass-through into remittance, money multiplier, the US Dollar index, and gold price. The results show that Bitcoin prices decrease the money multiplier and gold prices in both the short and long run, while remittances moderately increase in the long run. The implication of these results suggests the potential for international business opportunities to stimulate the credit, savings, and investment monetary policy channel. The results are robust to alternative SVAR identification strategies.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 观点阐述
【Author】 Udokwu, Chibuzor
【影响因子】2.592
【主题类别】
区块链应用-虚拟经济-协作平台
【Abstract】Small and medium-scale enterprises (SMEs) need a platform that actively enables collaboration with research institutions and consultants as SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system to enable, execute, and record collaboration activities and to track reputations among the organizations and individuals that use the platform. Blockchain provides an opportunity to build such a collaborative platform by enabling the verifiable recording of the results of the collaborations, aggregating the resulting reputation of the collaborating parties, and offering tokenized incentives to reward positive contributions to the platform. Cryptocurrencies from which blockchain tokens are derived are volatile, thereby reducing business organizations' interest in blockchain applications. Hence, there is a need to design a self-sustaining valuable token model that incentivizes user behaviours that positively contribute to the platform. This paper explores the application of game theory in analyzing token-based economic interactions between various groups of users in an implemented blockchain-based collaboration platform to design and simulate a token distribution system that provides a fair reward mechanism for users while also providing a dynamic pricing model for the utility value provided by platform tokens. To achieve this objective, we adopted the design science research method, a running case of a blockchain collaboration platform that enables research collaboration, and extensive form games in game theory, first to analyze and simulate token outcomes of users of the collaboration platform. Secondly, the research used a logarithmic model to show the dynamic utility pricing property of the developed token model where the self-sustainability of the token is backed by the availability of an internal resource within the platform. Thirdly, we applied a qualitative approach to analyze potential risks in the designed token model and proposed risk mitigation strategies. Thus, the resulting models and their simulations, such as token distribution models and a dynamic token utility model, as well as the identified token risks and their mitigation strategies, represent the main contributions of this work.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】blockchain application; token model; simulation; game theory
【发表时间】2024
【收录时间】2024-11-01
【文献类型】 实验仿真
【DOI】 10.3390/math12203252
【影响因子】2.557
【主题类别】
区块链技术-核心技术-区块传输
【Abstract】This study examines the potential of BIP-152's Compact Block Relay (CBR) to enhance the Bitcoin network. This work explores the block propagation efficiency through dynamic prefilling of transactions. In addition, an enhanced CBR model is proposed to reduce superfluous transaction requests, thus improving the block distribution process. The analysis considers the impact of the dynamically prefilled transactions on Bitcoin network scalability, comparing the advantages and disadvantages of this approach. We also conduct a comparative study of fixed-size and dynamically sized prefilled transactions to highlight the importance of adapting to network demands. Prefilling a fixed number of transactions without considering demand can cause inefficiencies and strain the network with unnecessary bandwidth use. Indiscriminate prefilling exacerbates these issues by inflating data packets unnecessarily, increasing latency and reducing network responsiveness. Our research indicates that the proposed solution can significantly reduce the number of round-trips between network nodes by an average of 29.77% and block reconstruction latency by 39.10% when compared with the CBR.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Bitcoin; Blockchain; Compact block relay (CBR); Round-trip; Block propagation; Delay
【发表时间】2025
【收录时间】2024-11-01
【文献类型】 理论模型