AI-driven innovations in automation and urban management

Keyu Yao

doi:10.54254/2755-2721/57/20241327

Research Article

Open access

Published on 30 April 2024

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Yao,K. (2024). AI-driven innovations in automation and urban management. Applied and Computational Engineering,57,160-165.

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AI-driven innovations in automation and urban management

Keyu Yao *^,1,

¹ University of Sheffield

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/57/20241327

Abstract

This paper examines the transformative impact of Artificial Intelligence (AI) across diverse sectors, particularly focusing on industrial automation, smart homes, and intelligent cities. It delves into how AI-driven technologies enhance predictive maintenance, quality control, and supply chain optimization in industrial settings, contribute to energy management, security enhancement, and health monitoring in smart homes, and improve traffic management, waste management, and public safety in intelligent cities. Through detailed analyses and quantitative assessments, the study showcases the efficiency gains, cost reductions, and quality of life improvements facilitated by AI integration. It highlights the pivotal role of AI in addressing contemporary challenges and setting new standards for operational excellence, sustainability, and safety.

Keywords

Artificial Intelligence, Industrial Automation, Smart Homes, Intelligent Cities, Predictive Maintenance

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References

[1]. Zador, Anthony, et al. "Catalyzing next-generation artificial intelligence through neuroai." Nature communications 14.1 (2023): 1597.

[2]. Jalolov, Tursunbek Sadriddinovich. "Artificial intelligence python (PYTORCH)." Oriental Journal of Academic and Multidisciplinary Research 1.3 (2023): 123-126.

[3]. Beam, Andrew L., et al. "Artificial intelligence in medicine." New England Journal of Medicine 388.13 (2023): 1220-1221.

[4]. Gašević, Dragan, George Siemens, and Shazia Sadiq. "Empowering learners for the age of artificial intelligence." Computers and Education: Artificial Intelligence 4 (2023): 100130.

[5]. Filippi, Emilia, Mariasole Bannò, and Sandro Trento. "Automation technologies and their impact on employment: A review, synthesis and future research agenda." Technological Forecasting and Social Change 191 (2023): 122448.

[6]. Golovianko, Mariia, et al. "Industry 4.0 vs. Industry 5.0: co-existence, Transition, or a Hybrid." Procedia Computer Science 217 (2023): 102-113.

[7]. Tidrea, Alexandra, Adrian Korodi, and Ioan Silea. "Elliptic Curve Cryptography Considerations for Securing Automation and SCADA Systems." Sensors 23.5 (2023): 2686.

[8]. Forni, Tommaso, et al. "AI and data-driven infrastructures for workflow automation and integration in advanced research and industrial applications." Ital-IA thematic workshops. CEUR-WS, 2023.

Cite this article

Yao,K. (2024). AI-driven innovations in automation and urban management. Applied and Computational Engineering,57,160-165.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://www.confcds.org/

ISBN：978-1-83558-393-7(Print) / 978-1-83558-394-4(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.57

ISSN：2755-2721(Print) / 2755-273X(Online)

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