【Author】 Cammarano, Antonello Varriale, Vincenzo Michelino, Francesca Caputo, Mauro

【Source】TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE

【影响因子】10.884

【主题类别】

区块链技术-核心技术-核心技术集

【Abstract】This paper presents a methodology for discovering technological opportunities of cutting-edge technologies by capturing information from literature. Technological opportunities are expressed in the form of business applications of technologies in untested contexts, i.e. unexplored industries and business processes. The paper underscores the advantages of discovering opportunities starting from existing emerging practices presented in scientific papers, in contrast with the current state of the art that prefers other datasets. The business cases presented in journals are converted into the triad technology-industry-process and the impact on the business performance is reported. From the analysis of 33,285 papers, 14,739 existing triads have been captured. An artificial neural network has been trained using this dataset, enabling accurate forecasting of the potential impact of vacant combinations technology-industry-process. The methodology has been tested on 11 cutting-edge technologies: 3D printing, artificial intelligence, blockchain, computing, digital applications, geo-spatial technologies, immersive environments, internet of things, open & crowd-based platforms, proximity technologies, robotics. For each technology, a technological opportunity map is provided to show the best vacant areas for future implementation. The methodology distinguishes between combinations with uncertain and confident expected impact, so that companies can focus on the most promising areas. Implications for both practice and academia are discussed.

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【Keywords】Technological opportunity; Cutting-edge technologies; Vacant technology; Technological forecasting; Technological convergence; Emerging technologies

【发表时间】2024

【收录时间】2024-10-29

【文献类型】 理论模型

【DOI】 10.1016/j.techfore.2024.123811

【Author】 Arshad, Usama Khan, Gohar Alarfaj, Fawaz Khaled Halim, Zahid Anwar, Sajid

【Source】ANNALS OF OPERATIONS RESEARCH

【影响因子】4.820

【主题类别】

区块链应用-实体经济-客户关系管理

【Abstract】Customer churn prediction is important for businesses, especially in the telecommunications sector, where retaining customers is more cost-effective than acquiring new ones. Traditional ensemble learning methods have enhanced prediction accuracy by combining multiple models, but they often struggle with efficiently processing complex, high-dimensional data. This paper introduces Q-Ensemble Learning, a novel approach integrating Quantum Computing (QC) with ensemble techniques to improve predictive performance. Our framework incorporates quantum algorithms such as Quantum Support Vector Machine (Q SVM), Quantum k-Nearest Neighbors (Q k-NN), Quantum Decision Tree (QDT), and others into an ensemble, leveraging their superior computational capabilities. The predictions from each quantum classifier are aggregated using a consensus voting mechanism and recorded on a blockchain to ensure robust data transparency and security. Extensive experiments on publicly available telecom customer churn datasets demonstrate that Q-Ensemble Learning improves accuracy by 15%, precision by 12%, and recall by 10% compared to classical ensemble methods such as Random Forest and Gradient Boosting. These metrics highlight the framework's effectiveness in reducing false positives and negatives, significantly enhancing the reliability of churn prediction. The blockchain integration further ensures that the data handling process is transparent and secure, building trust in the model's decisions.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Ensemble learning; Customer; Churn prediction; Blockchain; Quantum

【发表时间】2024

【收录时间】2024-10-29

【文献类型】 实验仿真

【DOI】 10.1007/s10479-024-06346-1

【Author】 Li, Lingyun Li, Juan Liu, Ruhong Li, Ziyu

【Source】COMPUTERS & ELECTRICAL ENGINEERING

【影响因子】4.152

【主题类别】

区块链技术-协同技术-边缘计算

【Abstract】With the rapid development of the Internet of Things (IoT), terminal-edge-cloud collaborative computing (TECC), a hierarchical distributed computing model, has become an effective solution to meet the various application requirements in terms of high computing power, high storage capacity, and low-latency services. However, the TECC still faces many challenges in terms of data security and privacy protection. Integrating blockchain into TECC frameworks can enable trustful data exchange between nodes while ensuring the integrity and availability of data. This paper presents an in-depth survey of blockchain-based TECC technology. In particular, we first introduce the key of the TECC paradigm and blockchain briefly. Then we focus on the integration of blockchain into the TECC paradigm. Specifically, the paper divides the TECC frameworks into three categories: one-layer, two-layers and multiple layers. Moreover, this paper summarizes core technologies in blockchain-based TECC architectures, TECC based on lightweight blockchains, and summarizes the application scenarios from different perspectives. Finally, the paper outlines the future direction of blockchain-based TECC.

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【Keywords】Blockchain; Edge computing; Internet of things; Collaborative framework; Data secret

【发表时间】2024

【收录时间】2024-10-29

【文献类型】 综述

【DOI】 10.1016/j.compeleceng.2024.109737

【Author】 Gezer, Ali

【Source】ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING

【影响因子】2.807

【主题类别】

区块链治理-市场治理-市场分析

【Abstract】Cryptocurrencies, particularly Bitcoin have attracted a lot of attention in the last decades of humanity. Analyzing cryptocurrencies algorithmic differences, chaotic behavior and self-similarity in cryptocurrency metrics might give significant insights for identifying risks and opportunities. Determining the degree of chaos in crypto metrics is critical for understanding complexity, improving prediction capabilities, and supporting decision-making. This study focuses on the analysis of chaos and self-similarity in Bitcoin dynamics for predictability perspective. Return, rate of return and volume quantities in different scales are analyzed with using rescaled range method to reveal the degree of self-similarity. Hurst parameter extracts a comprehensive summary providing information on how current values depend on previous ones to reveal any persistence in Bitcoin metrics. Daily rate of return and return give Hurst degree around 0.64 while they are in between 0.52-0.55 for minutely and hourly based prices. However, an increasing persistence is observed with the increasing time window. Although the largest Lyapunov exponents stay in the positive region for prices and returns of Bitcoin, they are approximately zero for inspected statistics. Periodic characteristics of Bitcoin are also investigated to reveal any dependencies on halving mechanism of Bitcoin. Detailed self-similarity analysis on specific periods shows that bull and bear market seasons don't make any significant effect on the degree of Hurst parameter. Due to nonlinear and unpredictable characteristics of Bitcoin metrics, distribution fittings are applied to characterize BTC return and rate of return. While Wakeby distribution gives best fitting for daily return, Cauchy distribution gives best for hourly returns.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Bitcoin; Self-similarity; Hurst exponent; The largest Lyapunov exponent; R/S; Chaos

【发表时间】2024

【收录时间】2024-10-29

【文献类型】 实证数据

【DOI】 10.1007/s13369-024-09357-z

【Author】 Eshghi, Kamran Farivar, Samira

【Source】JOURNAL OF ORGANIZATIONAL COMPUTING AND ELECTRONIC COMMERCE

【影响因子】2.237

【主题类别】

区块链治理-市场治理-数字货币

【Abstract】Despite the growing adoption of cryptocurrencies as a payment method, the literature lacks a comprehensive exploration of its performance outcome. To address this gap, authors examine the impact of firms' adoption of cryptocurrencies as a payment method on shareholder value. Employing event study methodology, authors analyze the effect of cryptocurrency adoption announcements made by 27 U.S. firms between 2013 and 2020 on shareholder value. The results indicate that cryptocurrency adoption leads to positive abnormal returns, with an average of 0.65% on the announcement day. Further analysis reveals that firms' advertising intensity amplifies the positive impact of cryptocurrency adoption on shareholder value. Additionally, non-retail firms tend to receive greater benefits from cryptocurrency adoption compared to retail firms.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Cryptocurrency; blockchain; payment method; bitcoin; event study

【发表时间】2024

【收录时间】2024-10-29

【文献类型】 实证数据

【DOI】 10.1080/10919392.2024.2415746

【Author】 Shafin, Khandakar Md Reno, Saha

【Source】IET SOFTWARE CCF-B

【影响因子】1.150

【主题类别】

区块链技术-核心技术-区块链架构

【Abstract】The ongoing challenge in the world of blockchain technology is finding a solution to the trilemma that involves balancing decentralization, security, and scalability. This paper introduces a pioneering blockchain architecture designed to transcend this trilemma, uniting advanced cryptographic methods, inventive security protocols, and dynamic decentralization mechanisms. Employing established techniques such as elliptic curve cryptography, Schnorr verifiable random function, and zero-knowledge proof (zk-SNARK), alongside groundbreaking methodologies for stake distribution, anomaly detection, and incentive alignment, our framework sets a new benchmark for secure, scalable, and decentralized blockchain ecosystems. The proposed system surpasses top-tier consensuses by attaining a throughput of 1700+ transactions per second, ensuring robust security against all well-known blockchain attacks without compromising scalability and demonstrating solid decentralization in benchmark analysis alongside 25 other blockchain systems, all achieved with an affordable hardware cost for validators and an average CPU usage of only 16.1%.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】blockchain; consensus; decentralization; security; throughput; trilemma

【发表时间】2024

【收录时间】2024-10-29

【文献类型】 实验仿真

【DOI】 10.1049/2024/6874055