sustainability-logo

Journal Browser

Journal Browser

Utilizing Artificial Intelligence as a Means to Achieve Sustainable Development

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (2 July 2024) | Viewed by 3726

Special Issue Editors


E-Mail Website
Guest Editor
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611715, China
Interests: artificial intelligence and its application; machine learning and its application

E-Mail Website
Guest Editor
School of Electronic Information and Artificial Intelligence, Leshan Normal University, Leshan 614000, China
Interests: Intelligent control; deep learning

E-Mail Website
Guest Editor
School of Computer Science, Sichuan University, Chengdu 610017, China
Interests: industrial control system security; privacy protection; authentication key negotiation protocols; intrusion detection; intelligent internet of things; data intelligence in industrial internet; blockchain

Special Issue Information

Dear Colleagues,

The sustainable development of the world cannot be achieved without the utilization of artificial intelligence, particularly because artificial intelligence has replaced several human mechanical labors, and thus have become an irreversible trend in world development. The indispensable role of artificial intelligence is evidenced by its large-scale application in all aspect of human life, ranging from transport, grid, manufacturing, and construction to education and social media.

In this context, this Special Issue aims to collate research exploring the rapid and close integration of artificial intelligence and sustainable development and the application of such integrated mechanisms across various fields and disciplines.

Potential topics include (but not limited to) the following:

  • Application of artificial intelligence;
  • Application of machine learning;
  • Application of data mining;
  • Application of artificial intelligence in education;
  • Application of artificial intelligence in electric power;
  • Application of artificial intelligence in transportation;
  • Application of artificial intelligence in manufacturing;
  • Application of artificial intelligence in construction;
  • Application of artificial intelligence in city;
  • Application of artificial intelligence in natural language processing;
  • Artificial intelligence interdisciplinary science.

Dr. Hongjun Wang
Prof. Dr. Peng Jin
Dr. Yanru Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • application of artificial intelligence
  • application of machine learning
  • application of data mining
  • application of artificial intelligence in education
  • application of artificial intelligence in electric power
  • application of artificial intelligence in transportation
  • application of artificial intelligence in manufacturing
  • application of artificial intelligence in construction
  • application of artificial intelligence in city
  • artificial intelligence interdisciplinary science

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

26 pages, 6711 KiB  
Article
Educational Practices and Algorithmic Framework for Promoting Sustainable Development in Education by Identifying Real-World Learning Paths
by Tian-Yi Liu, Yuan-Hao Jiang, Yuang Wei, Xun Wang, Shucheng Huang and Ling Dai
Sustainability 2024, 16(16), 6871; https://doi.org/10.3390/su16166871 - 10 Aug 2024
Cited by 1 | Viewed by 1235
Abstract
Utilizing big data and artificial intelligence technologies, we developed the Collaborative Structure Search Framework (CSSF) algorithm to analyze students’ learning paths from real-world data to determine the optimal sequence of learning knowledge components. This study enhances sustainability and balance in education by identifying [...] Read more.
Utilizing big data and artificial intelligence technologies, we developed the Collaborative Structure Search Framework (CSSF) algorithm to analyze students’ learning paths from real-world data to determine the optimal sequence of learning knowledge components. This study enhances sustainability and balance in education by identifying students’ learning paths. This allows teachers and intelligent systems to understand students’ strengths and weaknesses, thereby providing personalized teaching plans and improving educational outcomes. Identifying causal relationships within knowledge structures helps teachers pinpoint and address learning issues, forming the basis for adaptive learning systems. Using real educational datasets, the research introduces a multi-sub-population collaborative search mechanism to enhance search efficiency by maintaining individual-level superiority, population-level diversity, and solution-set simplicity across sub-populations. A bidirectional feedback mechanism is implemented to discern high-quality and low-quality edges within the knowledge graph. Oversampling high-quality edges and undersampling low-quality edges address optimization challenges in Learning Path Recognition (LPR) due to edge sparsity. The proposed Collaborative Structural Search Framework (CSSF) effectively uncovers relationships within knowledge structures. Experimental validations on real-world datasets show CSSF’s effectiveness, with a 14.41% improvement in F1-score over benchmark algorithms on a dataset of 116 knowledge structures. The algorithm helps teachers identify the root causes of students’ errors, enabling more effective educational strategies, thus enhancing educational quality and learning outcomes. Intelligent education systems can better adapt to individual student needs, providing personalized learning resources, facilitating a positive learning cycle, and promoting sustainable education development. Full article
Show Figures

Figure 1

30 pages, 59008 KiB  
Article
Managing Rockfall Hazard on Strategic Linear Stakes: How Can Machine Learning Help to Better Predict Periods of Increased Rockfall Activity?
by Marie-Aurélie Chanut, Hermann Courteille, Clara Lévy, Abdourrahmane Atto, Lucas Meignan, Emmanuel Trouvé and Muriel Gasc-Barbier
Sustainability 2024, 16(9), 3802; https://doi.org/10.3390/su16093802 - 30 Apr 2024
Viewed by 1756
Abstract
When rockfalls hit and damage linear stakes such as roads or railways, the access to critical infrastructures (hospitals, schools, factories …) might be disturbed or stopped. Rockfall risk management often involves building protective structures that are traditionally based on the intensive use of [...] Read more.
When rockfalls hit and damage linear stakes such as roads or railways, the access to critical infrastructures (hospitals, schools, factories …) might be disturbed or stopped. Rockfall risk management often involves building protective structures that are traditionally based on the intensive use of resources such as steel or concrete. However, these solutions are expensive, considering their construction and maintenance, and it is very difficult to protect long linear stakes. A more sustainable and effective risk management strategy could be to account for changes on rockfall activity related to weather conditions. By integrating sustainability principles, we can implement mitigation measures that are less resource-intensive and more adaptable to environmental changes. For instance, instead of solely relying on physical barriers, solutions could include measures such as restriction of access, monitoring and mobilization of emergency kits containing eco-friendly materials. A critical step in developing such a strategy is accurately predicting periods of increased rockfall activity according to meteorological triggers. In this paper, we test four machine learning models to predict rockfalls on the National Road 1 at La Réunion, a key road for the socio-economic life of the island. Rainfall and rockfall data are used as inputs of the predictive models. We show that a set of features derived from the rainfall and rockfall data can predict rockfall with performances very close and almost slightly better than the standard expert model used for operational management. Metrics describing the performance of these models are translated in operational terms, such as road safety or the duration of road closings and openings, providing actionable insights for sustainable risk management practices. Full article
Show Figures

Figure 1

Back to TopTop