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Applied Sciences
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Artificial Intelligence for Sustainable Development
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Artificial Intelligence for Sustainable Development

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 9343

Special Issue Editors

Dr. Valentino Santucci

E-Mail Website
Guest Editor
Department of Humanities and Social Sciences, University for Foreigners of Perugia, 06123 Perugia, Italy
Interests: natural language processing; evolutionary computation; computational optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Alfredo Milani

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Perugia, 06123 Perugia, Italy
Interests: online evolutionary algorithms; metaheuristic for combinatorial optimization; discrete differential evolution; semantic proximity measures; planning agents and complex network dynamics; emotion recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Fabio Caraffini

E-Mail Website
Guest Editor
Department of Computer Science, Computational Foundry, Swansea University, Bay Campus, Fabian Way, Skewen SA1 8EN, UK
Interests: evolutionary computation; swarm intelligence; computational intelligence; differential evolution; memetic computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has significantly evolved in the past decades and provided practitioners with tools making it easier to address challenging real-world applications of multiple nature and complexity levels. These include application domains of great societal, economic, and environmental impact, thus, playing a major role in achieving key Sustainable Development Goals (SDGs) amongst those defined by The United Nations (UN). According to the “2030 Agenda for Sustainable Development”, SDGs are grouped into 17 challenging areas (see https://sdgs.un.org/goals) encompassing a variety of fundamental issues related to social, economic, and environmental aspects including renewable energies adoption, management of water and other natural resources, healthcare and education quality, gender equality, sustainable agriculture, waste minimization, resilient urban development, industrial innovation, and risk management. Hence, this Special Issue focuses on the applications of current AI techniques and approaches (such as machine learning, deep learning, evolutionary computation, social network analyses, automated reasoning, etc.) to SDGs and related concepts like, e.g., modern smart infrastructures (smart grids, smart cities, smart buildings, etc.), intelligent transportation systems, renewable energies and environmental resources, enhanced education and healthcare, cybersecurity, community interaction, and all the other aspects related to societal enhancement.

Prof. Valentino Santucci
Prof. Alfredo Milani
Prof. Fabio Caraffini
Guest Editors

 

Keywords

  • applied artificial intelligence
  • AI for sustainable development goals
  • smart infrastructures such as smart grids, smart cities, and smart buildings
  • green computing
  • intelligent and safe transportation
  • technology-enhanced education
  • tele-medicine and AI for healthcare
  • applied optimization
  • cybersecurity
  • applied evolutionary computation

Published Papers (3 papers)

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Research

15 pages, 1656 KiB  
Article
Application of VR Technology to the Training of Paramedics
by Martin Boros, Eva Sventekova, Anna Cidlinova, Marek Bardy and Katerina Batrlova
Appl. Sci. 2022, 12(3), 1172; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031172 - 23 Jan 2022
Cited by 5 | Viewed by 2948
Abstract
The virtual world has long been a focus not only of the gaming sphere, but also of the manufacturing and educational industries. The virtual world and its technology have many advantages, the basic ones being, for example, the use of experiential learning, with [...] Read more.
The virtual world has long been a focus not only of the gaming sphere, but also of the manufacturing and educational industries. The virtual world and its technology have many advantages, the basic ones being, for example, the use of experiential learning, with which the human brain can remember some things better and faster. It was due to the advantages of virtual reality technology that we decided to create an educational system on safety and health at work, and we focused on the healthcare segment due to the COVID-19 pandemic. Thanks to the cooperation of a professional consortium, we created an educational system for safety and health at work and carried out several extensive laboratory measurements, the results of which we followed up in practical measurements with medical staff. The created system is inherently unique and applicable and can be used across several industries. The article presents three basic types of scenarios as well as an evaluation of satisfaction with the proposed system from test participants, i.e., nurses. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Development)
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20 pages, 13469 KiB  
Article
Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
by Antonio Agresta, Marco Baioletti, Chiara Biscarini, Fabio Caraffini, Alfredo Milani and Valentino Santucci
Appl. Sci. 2021, 11(22), 10575; https://0-doi-org.brum.beds.ac.uk/10.3390/app112210575 - 10 Nov 2021
Cited by 7 | Viewed by 1557
Abstract
Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best [...] Read more.
Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being able to accurately estimate the roughness coefficient, also known as Manning’s n coefficient, plays an important role when computational models are employed. In this piece of research, we propose an optimal approach for the estimation of ‘n’. First, an objective function is designed for measuring the quality of ‘candidate’ Manning’s coefficients relative to specif cross-sections of a river. Second, such function is optimised to return coefficients having the highest quality as possible. Five well-known meta-heuristic algorithms are employed to achieve this goal, these being a classic Evolution Strategy, a Differential Evolution algorithm, the popular Covariance Matrix Adaptation Evolution Strategy, a classic Particle Swarm Optimisation and a Bayesian Optimisation framework. We report results on two real-world case studies based on the Italian rivers ‘Paglia’ and ‘Aniene’. A comparative analysis between the employed optimisation algorithms is performed and discussed both empirically and statistically. From the hydrodynamic point of view, the experimental results are satisfactory and produced within significantly less computational time in comparison to classic methods. This shows the suitability of the proposed approach for optimal estimation of the roughness coefficient and, in turn, for designing optimised hydrological models. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Development)
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15 pages, 1642 KiB  
Article
Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment
by Laia Subirats, Santi Fort, Santiago Atrio and Gomez-Monivas Sacha
Appl. Sci. 2021, 11(21), 9923; https://0-doi-org.brum.beds.ac.uk/10.3390/app11219923 - 23 Oct 2021
Cited by 3 | Viewed by 3574
Abstract
Distance learning has been adopted as a very extended model during COVID-19-related confinement. It is also a methodology that can be applied in environments where people do not have easy access to schools. In this study, we automatically classify students as a function [...] Read more.
Distance learning has been adopted as a very extended model during COVID-19-related confinement. It is also a methodology that can be applied in environments where people do not have easy access to schools. In this study, we automatically classify students as a function of their performance and we describe the best self-learning methodologies in distance learning, which will be useful both in confinement or for people with difficult access to schools. Due to the different learning scenarios provided by the different confinement conditions in the COVID-19 pandemic, we have performed the classification considering data before, during, and after COVID-19 confinement. Using a field experiment of 396 students, we have described the temporal evolution of students during all courses from 2016/2017 to 2020/2021. We have found that data obtained in the last month before the final exam of the subject include the most relevant information for a correct detection of students at risk of failure. On the other hand, students who obtain high scores are much easier to identify. Finally, we have concluded that the distance learning applied in COVID-19 confinement changed not only teaching strategies but also students’ strategies when learning autonomously. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Development)
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