The impact of the artificial intelligence industry on the number and structure of employments in the digital economy environment

Zhang, Zhuo

doi:10.1016/j.techfore.2023.122881

Cited by 25 publications

(11 citation statements)

References 37 publications

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“…It would be beneficial to conduct further research to optimize the performance of our proposed method and explore its applicability in other domains. In addition, our findings suggest that using advanced machine learning techniques in combination with blockchain technology can enhance the security and trustworthiness of transaction systems, thereby creating new opportunities for innovation and growth in the digital economy [31].…”

Section: Challenges and Proposed Methodsmentioning

confidence: 87%

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

Yue,

Zhang,

Zhang

et al. 2024

Electronics

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Cryptocurrency, as one of the most successful applications of blockchain technology, has played a vital role in promoting the development of the digital economy. However, its anonymity, large scale of cryptographic transactions, and decentralization have also brought new challenges in identifying abnormal accounts and preventing abnormal transaction behaviors, such as money laundering, extortion, and market manipulation. Recently, some researchers have proposed efficient and accurate abnormal transaction detection based on machine learning. However, in reality, abnormal accounts and transactions are far less common than normal accounts and transactions, so it is difficult for the previous methods to detect abnormal accounts by training with such an imbalance in abnormal/normal accounts. To address the issues, in this paper, we propose a method for identifying abnormal accounts using topology analysis of cryptographic transactions. We consider the accounts and transactions in the blockchain as graph nodes and edges. Since the abnormal accounts may have special topology features, we extract topology features from the transaction graph. By analyzing the topology features of transactions, we discover that the high-dimensional sparse topology features can be compressed by using the singular value decomposition method for feature dimension reduction. Subsequently, we use the generative adversarial network to generate samples like abnormal accounts, which will be sent to the training dataset to produce an equilibrium of abnormal/normal accounts. Finally, we utilize several machine learning techniques to detect abnormal accounts in the blockchain. Our experimental results demonstrate that our method significantly improves the accuracy and recall rate for detecting abnormal accounts in blockchain compared with the state-of-the-art methods.

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Section: Challenges and Proposed Methodsmentioning

confidence: 87%

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

Yue,

Zhang,

Zhang

et al. 2024

Electronics

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“…Standardized frameworks for measuring and reporting supply chain sustainability metrics, if adopted more widely, could help address this gap. Further large-scale pilots and longitudinal case evaluations capturing long-term outcomes are also needed to establish evidenced best practices (Mehedintu and Soava, 2023;Memon and Ooi, 2023;Singh et al, 2023;Wang et al, 2023;Zhang, 2023). A key trend emerging from the four cases analyzed in depth relates to the importance of partnerships between technology providers, enterprises, governments and community stakeholders for effective co-creation and scaling of solutions.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, responsible development and sourcing of resources for advanced computing infrastructure underpinning AI and cloud infrastructures requires urgent attention. Further research is needed to fill literature gaps and standardize lifecycle analyses across different technology suites, industrial sectors, and regions (Mehedintu and Soava, 2023;Memon and Ooi, 2023;Singh et al, 2023;Wang et al, 2023;Zhang, 2023) (Di Vaio and Varriale, 2020;Paliwal et al, 2020;Varriale et al, 2020;Liu and Lin, 2021;Palomares et al, 2021;Tsolakis et al, 2021;Khan et al, 2022a;Khan et al, 2022b;Chang et al, 2022;Hassoun et al, 2022;Ma et al, 2022;Xue et al, 2022;Yi et al, 2022;Zkik et al, 2023a;Kwilinski, 2023;Leong et al, 2023;Mehedintu and Soava, 2023;Memon and Ooi, 2023;Richey et al, 2023;Saeed et al, 2023;Singh et al, 2023;Soori et al, 2023;Wang et al, 2023;Wu et al, 2023;Zhang, 2023). Frontiers in Environmental Science frontiersin.org partnerships, and address the digital divide to ensure equitable sharing of environmental protection benefits.…”

Section: Resultsmentioning

confidence: 99%

“…The input of stakeholders who are directly affected by or have a vested interest in environmental issues leads to more informed and comprehensive decision-making processes. Moreover, stakeholder participation enhances the legitimacy and acceptance of environmental initiatives (Varriale et al, 2020;Xue et al, 2022;Mehedintu and Soava, 2023;Memon and Ooi, 2023;Wang et al, 2023;Zhang, 2023). When stakeholders are actively involved in decision-making, they are more likely to support and implement the resulting solutions, which is vital for successful implementation and mitigation of adverse environmental effects.…”

Section: Cooperative Models For Environmental Participationmentioning

confidence: 99%

“…They encourage the development of pathways that prioritize positive impact over purely profit-driven motives. By adopting these inclusive and impact-optimized approaches, the resulting protocols are more likely to prevent downstream social and environmental costs and contribute to a more equitable and sustainable future (Varriale et al, 2020;Hassoun et al, 2022;Xue et al, 2022;Mehedintu and Soava, 2023;Memon and Ooi, 2023;Wang et al, 2023;Zhang, 2023).…”

Section: Inclusiveness Through Standards Co-creationmentioning

confidence: 99%

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Exploring the structure of the digital economy through blockchain technology and mitigating adverse environmental effects with the aid of artificial neural networks

Cai,

Hong

2024

Front. Environ. Sci.

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The rapid expansion of the digital economy has had a transformative impact on society, presenting both opportunities and challenges. This article aims to examine the structure of the digital economy and its implications, with a specific focus on the adverse environmental effects associated with its rapid growth. To address these challenges, the utilization of artificial neural networks is proposed as a viable solution. ANNs have proven to be effective in analyzing large volumes of data and extracting valuable insights. By integrating blockchain technology and harnessing the power of ANNs, this study seeks to develop management strategies that optimize resource allocation, reduce waste, and promote sustainability within the digital economy. Through comprehensive data analysis, patterns and trends can be identified, providing decision-makers with valuable information to make informed choices that minimize the environmental impact of digitalization. This research significantly contributes to the existing body of knowledge by enhancing our understanding of the digital economy’s structure, particularly in the context of blockchain technology. The ANN in this study estimated the impact of digital economy growth and structure improvement on adverse environmental effects, waste reduction, and environmental sustainability. The predictions showed that increasing digital economy growth led to increased waste reduction and promotion of environmental sustainability, while adverse environmental effects exhibited sinusoidal behavior. Linear regression confirmed the acceptable error of the network’s predictions compared to experimental results. Furthermore, it sheds light on the potential of ANNs to mitigate the adverse environmental effects associated with the digital economy. By emphasizing the importance of sustainable practices and exploring the applications of emerging technologies, this study offers valuable insights for policymakers, researchers, and industry practitioners seeking to navigate the complex landscape of the digital economy while minimizing its environmental consequences.

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Examining the complex causal relationships between the digital economy and urban tourist destination competitiveness

Zhang,

Zhang

2024

Econ Change Restruct

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The impact of the artificial intelligence industry on the number and structure of employments in the digital economy environment

Cited by 25 publications

References 37 publications

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

Exploring the structure of the digital economy through blockchain technology and mitigating adverse environmental effects with the aid of artificial neural networks

Examining the complex causal relationships between the digital economy and urban tourist destination competitiveness

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