Substation intelligent security management and control practice based on artificial intelligence and big data analysis

Li, Xinzhuo; Zhang, Li; Zhang, Junjie; Luo, Xianyue

doi:10.1109/tocs56154.2022.10016106

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A quality control framework for intelligent supply chain management based on big data analytics and AI has attracted growing attention in the application of AI and ML for supply chain management [8][9]. Steel product quality control using machine learning in a supply chain management system [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence, Machine Learning and Data Science Enabled Supply Chain Management

Pandey,

Koksharova

et al. 2023

IJEAST

View full text Add to dashboard Cite

The planning, coordination, and execution that go into supply chain management are extensive and challenging. The application of data science, machine learning, and artificial intelligence (AI) technologies may greatly increase supply chain management's efficacy and efficiency. By examining the state of technology today, the advantages and difficulties of adopting these technologies, and the future paths for research and development, the goal of this study is to evaluate the effects of AI, ML, and DS on supply chain management. To provide readers a thorough grasp of the subject, the article offers a thorough assessment of the literature and case studies. The findings of this study demonstrate how AI, ML, and DS can revolutionize supply chain management by enhancing decision-making, boosting visibility, and lowering costs. However, overcoming key obstacles including data quality, integration, and privacy concerns is necessary for the effective deployment of these technologies. In order to overcome these difficulties and enhance the effectiveness of AI, ML, and DS in Supply Chain Management, future research should concentrate on creating more complex algorithms and models

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence, Machine Learning and Data Science Enabled Supply Chain Management

Pandey,

Koksharova

et al. 2023

IJEAST

View full text Add to dashboard Cite

show abstract

Construction of Intelligent Energy Safety Operation and Maintenance System based on AI Algorithm and Artificial Hard Rules

Mao,

Jiang,

Zhou

et al. 2023

2023 9th Annual International Conference on Network and Information Systems for Computers (ICNISC)

View full text Add to dashboard Cite

Analyzing Critical Factors Influencing the Quality Management in Smart Construction Site: A DEMATEL-ISM-MICMAC Based Approach

Zhang,

Liu,

et al. 2024

Buildings

View full text Add to dashboard Cite

The swift integration of advanced technologies in the construction sector has significantly propelled the adoption of smart construction sites (SCSs). Quality management (QM), a critical endeavor within the construction domain, is central to the operational success of construction projects. The establishment of quality management in smart construction sites (SCS-QM) specifically seeks to delineate the principal factors influencing quality management in the context of SCS, with the objective of enhancing overall project quality. This study has identified 19 pivotal factors impacting SCS-QM by drawing upon the 4M1E quality management framework and an extensive review of the literature. Utilizing the hybrid DEMATEL-ISM-MICMAC analytical framework, the research evaluates these factors in terms of significance, hierarchical structure, and interdependencies, thereby formulating targeted strategies for the advancement of SCS-QM. Through a systematic evaluation by nine experts, this study categorizes the influencing factors into nine levels, three layers, and four areas, further classifying them into four distinct impact typologies. The results underscore that those technologies, such as automation and intelligence, along with regulatory frameworks, comprehensive quality management standards, transparency of critical technologies, training of construction personnel, and effective process management, constitute the foundational elements crucial for enhancing SCS-QM.

show abstract

Substation intelligent security management and control practice based on artificial intelligence and big data analysis

Cited by 3 publications

References 0 publications

Artificial Intelligence, Machine Learning and Data Science Enabled Supply Chain Management

Artificial Intelligence, Machine Learning and Data Science Enabled Supply Chain Management

Construction of Intelligent Energy Safety Operation and Maintenance System based on AI Algorithm and Artificial Hard Rules

Analyzing Critical Factors Influencing the Quality Management in Smart Construction Site: A DEMATEL-ISM-MICMAC Based Approach

Contact Info

Product

Resources

About