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Sustainability
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Artificial Intelligence and Smart Technologies for Achieving Sustainable Goals
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Artificial Intelligence and Smart Technologies for Achieving Sustainable Goals

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 9201

Special Issue Editors

Prof. Dr. Hend Al-Khalifa

E-Mail Website
Guest Editor
Faculty in the Information Technology Department and Head of iWAN Research Group, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Interests: natural language processing; artificial intelligence; data science; cognitive computing; innovation; emerging technologies; information technology; technology-driven innovation
Dr. Duaa AlSaeed

E-Mail Website
Co-Guest Editor
Faculty in the Information Technology Department and a member of iWAN Research Group, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Interests: computer vision; image processing; artificial intelligence; data science; usability; accessibility; natural language processing; innovation; emerging technologies; technology driven innovation
Dr. Heyam Al-Baity

E-Mail Website
Co-Guest Editor
Faculty in the Information Technology Department and a member of iWAN Research Group, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Interests: artificial intelligence; data science; optimization; biometrics; natural language processing; innovation; emerging technologies; technology driven innovation

Special Issue Information

Dear Colleagues,

The United Nations’ Sustainable Development Goals (SDGs) provide a blueprint for a fairer and more sustainable world for everyone. One of the key ways to achieve these goals is through the use of artificial intelligence (AI) and smart technologies.

Artificial intelligence (AI) has been hailed as a game-changer for sustainable development. It has the potential to help achieve the Sustainable Development Goals (SDGs) in a number of ways, including by providing decision support for sustainable development planning, helping to optimize resource use, and increasing transparency and accountability.

Smart technologies offer great potential for sustainable development applications. Smart technologies can help to improve the efficiency of resource use, e.g., by reducing wastage, and can also help to improve transparency and accountability.

In this Special Issue, we invite papers that explore the potential of AI and smart technologies for sustainable development. We are particularly interested in papers that describe applications of these technologies that have the potential to make a real difference to the achievement of SDGs. We welcome papers from all sectors, including academia, industry, government, and non-governmental organizations (NGOs).

Topics of interest include, but are not limited to, the following:

  • Applications of AI and smart technologies in sustainable development;
  • The potential of AI and smart technologies to help achieve the SDGs;
  • Barriers and challenges to the use of AI and smart technologies for sustainable development;
  • Future directions for the use of AI and smart technologies in sustainable development;
  • Ethical considerations in the use of AI and smart technologies for sustainable development;
  • The role of AI and smart technologies in sustainable development policy.

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. All papers will be 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.

Prof. Dr. Hend Al-Khalifa
Dr. Heyam Al-Baity
Dr. Duaa AlSaeed
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

  • AI and smart technologies in sustainable development

Published Papers (4 papers)

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Research

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37 pages, 5179 KiB  
Article
A Multi-Objective Simulated Annealing Local Search Algorithm in Memetic CENSGA: Application to Vaccination Allocation for Influenza
by Asma Khalil Alkhamis and Manar Hosny
Sustainability 2023, 15(21), 15347; https://doi.org/10.3390/su152115347 - 27 Oct 2023
Viewed by 878
Abstract
Flu vaccine allocation is of great importance for safeguarding public health and mitigating the impact of influenza outbreaks. In this regard, decision-makers face multifaceted challenges, including limited vaccine supply, targeting vulnerable people, adapting to regional variations, ensuring fairness in distribution, and promoting public [...] Read more.
Flu vaccine allocation is of great importance for safeguarding public health and mitigating the impact of influenza outbreaks. In this regard, decision-makers face multifaceted challenges, including limited vaccine supply, targeting vulnerable people, adapting to regional variations, ensuring fairness in distribution, and promoting public trust. The objective of this work is to address the vaccination allocation problem by introducing a novel optimization scheme with the simulated annealing (SA) algorithm. A dual-objective model is developed to both manage infection rates and minimize the unit cost of the vaccination campaign. The proposed approach is designed to promote convergence toward the best Pareto front in multi-objective optimization, wherein SA attempts to embed diversity and uniformity within a memetic version of the controlled elitism nondominated sorting genetic algorithm (CENSGA). To model the underlying vaccination allocation problem, the dynamics of the disease are described using the susceptible–exposed–infectious–recovered (SEIR) epidemiological model to better express hidden flu characteristics. This model specifically analyzes the effects of pulsive vaccination allocation in two phases aiming to minimize the number of infected individuals to an acceptable level in a finite amount of time, which can help in stabilizing the model against sudden flu endemics over the long run. The computational experiments show that the proposed algorithm effectively explores the extensive search space of the vaccination allocation problem. The results of the suggested framework indicate that the obtained Pareto front best represents complete vaccination campaigns. The findings of this research can help in evidence-based decision making that can optimize flu vaccine distribution, contribute to the prevention of illness and reduction in hospitalizations, and potentially save countless lives. Full article
(This article belongs to the Special Issue Artificial Intelligence and Smart Technologies for Achieving Sustainable Goals)
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14 pages, 2627 KiB  
Article
AraMAMS: Arabic Multi-Aspect, Multi-Sentiment Restaurants Reviews Corpus for Aspect-Based Sentiment Analysis
by Alanod AlMasaud and Heyam H. Al-Baity
Sustainability 2023, 15(16), 12268; https://doi.org/10.3390/su151612268 - 11 Aug 2023
Viewed by 1353
Abstract
The abundance of data on the internet makes analysis a must. Aspect-based sentiment analysis helps extract valuable information from textual data. Because of limited Arabic resources, this paper enriches the Arabic dataset landscape by creating AraMA, the first and largest Arabic multi-aspect corpus. [...] Read more.
The abundance of data on the internet makes analysis a must. Aspect-based sentiment analysis helps extract valuable information from textual data. Because of limited Arabic resources, this paper enriches the Arabic dataset landscape by creating AraMA, the first and largest Arabic multi-aspect corpus. AraMA comprises 10,750 Google Maps reviews for restaurants in Riyadh, Saudi Arabia. It covers four aspect categories—food, environment, service, and price—along with four sentiment polarities: positive, negative, neutral, and conflict. All AraMA reviews are labeled with at least two aspect categories. A second version, named AraMAMS, includes reviews labeled with at least two different sentiments, making it the first Arabic multi-aspect, multi-sentiment dataset. AraMAMS has 5312 reviews covering the same four aspect categories and sentiment polarities. Both corpora were evaluated using naïve biased (NB), support vector classification (SVC), linear SVC, and stochastic gradient descent (SGD) models. In the AraMA corpus, the aspect categories task achieved a 91.41% F1 measure result using the SVC model, while in the AraMAMS corpus, the best F1 measure result for aspect categories task reached 91.70% using the linear SVC model. Full article
(This article belongs to the Special Issue Artificial Intelligence and Smart Technologies for Achieving Sustainable Goals)
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18 pages, 636 KiB  
Article
Prediction of Gender-Biased Perceptions of Learners and Teachers Using Machine Learning
by Ghazala Kausar, Sajid Saleem, Fazli Subhan, Mazliham Mohd Suud, Mansoor Alam and M. Irfan Uddin
Sustainability 2023, 15(7), 6241; https://doi.org/10.3390/su15076241 - 5 Apr 2023
Viewed by 2080
Abstract
Computers have enabled diverse and precise data processing and analysis for decades. Researchers of humanities and social sciences are increasingly adopting computational tools such as artificial intelligence (AI) and machine learning (ML) to analyse human behaviour in society by identifying patterns within data. [...] Read more.
Computers have enabled diverse and precise data processing and analysis for decades. Researchers of humanities and social sciences are increasingly adopting computational tools such as artificial intelligence (AI) and machine learning (ML) to analyse human behaviour in society by identifying patterns within data. In this regard, this paper presents the modelling of teachers and students’ perceptions regarding gender bias in text books through AI. The data was collected from 470 respondents through a questionnaire using five different themes. The data was analysed with support vector machines (SVM), decision trees (DT), random forest (RF) and artificial neural networks (ANN). The experimental results show that the prediction of perceptions regarding gender varies according to the theme and leads to the different performances of the AI techniques. However, it is observed that when data from all the themes are combined, the best results are obtained. The experimental results show that ANN, on average, demonstrates the best performance by achieving an accuracy of 87.2%, followed by RF and SVM, which demonstrate an accuracy of 84% and 80%, respectively. This paper is significant in modelling human behaviour in society through AI, which is a significant contribution to the field. Full article
(This article belongs to the Special Issue Artificial Intelligence and Smart Technologies for Achieving Sustainable Goals)
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Review

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16 pages, 1609 KiB  
Review
The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework
by Heyam H. Al-Baity
Sustainability 2023, 15(18), 13725; https://doi.org/10.3390/su151813725 - 15 Sep 2023
Cited by 2 | Viewed by 3766
Abstract
Artificial Intelligence (AI) has proliferated in the last few years due to the vast data we pro-duce daily and available computing power. AI can be applied in many different sectors, such as transportation, education, healthcare, banking, and finance, among many others. The financial [...] Read more.
Artificial Intelligence (AI) has proliferated in the last few years due to the vast data we pro-duce daily and available computing power. AI can be applied in many different sectors, such as transportation, education, healthcare, banking, and finance, among many others. The financial industry is rapidly embracing AI due to its potential for high-cost savings in financial services. AI could transform the financial sector by creating opportunities for tailored, faster, and more cost-effective services. Saudi Arabia is emerging as a fast-growing market in this industry with a strong commitment to technology-driven institutions. While AI is gaining prominence and receiving government support, it has not yet become a critical component for enhancing the efficiency of financial transactions. Limited published research on AI adoption in the Saudi Arabian financial industry calls for a comprehensive literature review to examine the current state of AI implementation in this sector. Therefore, this study explores the benefits, limitations, and challenges of leveraging AI in finance, highlighting the importance of ethical and regulatory considerations for successful AI adoption in the sector. This study’s findings reveal that research has been conducted on how AI improves processes in the financial sector by integrating critical components and efficient algorithms tailored to the industry’s needs. Based on these findings, this study proposes a sequential framework at the macro and micro levels of management to guide AI’s development and integration into the financial sector. Additionally, the framework draws insights from the existing literature to provide a detailed understanding of opportunities, challenges, and areas for improvement to maximize AI’s potential in the Saudi Arabian financial sector. Full article
(This article belongs to the Special Issue Artificial Intelligence and Smart Technologies for Achieving Sustainable Goals)
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