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Algorithms
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Artificial Intelligence in Sustainable Development
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Artificial Intelligence in Sustainable Development

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1945

Special Issue Editors

Prof. Dr. Frank Werner

E-Mail Website
Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University Magdeburg, P.O. Box 4120, D-39016 Magdeburg, Germany
Interests: scheduling, in particular development of exact and approximate algorithms; stability investigations is discrete optimization; scheduling with interval processing times; complexity investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation and applications
Special Issues, Collections and Topics in MDPI journals
Dr. Tareq Hamadneh

E-Mail Website
Guest Editor
Department of Mathematics, Al Zaytoonah University of Jordan, Amman 11733, Jordan
Interests: numerical modeling; algebraic optimization; control theory

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the growing and evolving role of artificial intelligence in supporting and enhancing sustainable development on a global scale. The Special Issue will address a diverse range of topics related to the applications of artificial intelligence in various fields of sustainable development, such as sustainable agriculture, natural resource management, environmental conservation, energy provision, urban planning, healthcare, education, economic development, and more. This Special Issue invites scholars and industry experts to showcase the latest advancements in mathematical models related to artificial intelligence in sustainable agriculture, energy, health and technology. Potential topics include, but are not restricted to, the following:

- Applications of artificial intelligence in sustainable agriculture and improving crop productivity;

- Artificial intelligence techniques for providing and improving sustainable energy;

- Advances in artificial intelligence to enhance healthcare and promote sustainable health;

- Opportunities for collaboration and partnerships in developing and sharing artificial intelligence technologies to enhance sustainable development;

- Applied mathematical models for artificial problems.

Prof. Dr. Frank Werner
Dr. Tareq Hamadneh
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 250 words) can be sent to the Editorial Office for assessment.

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. Algorithms is an international peer-reviewed open access monthly 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 1800 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

  • applications of artificial intelligence
  • engineering problems
  • mathematical optimization
  • algorithms

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

Published Papers (2 papers)

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Research

26 pages, 544 KB  
Article
Physics-Aware Deep Learning Framework for Solar Irradiance Forecasting Using Fourier-Based Signal Decomposition
by Murad A. Yaghi and Huthaifa Al-Omari
Algorithms 2026, 19(1), 81; https://doi.org/10.3390/a19010081 - 17 Jan 2026
Viewed by 353
Abstract
Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish [...] Read more.
Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish between deterministic astronomical cycles, and random meteorological variability. The objective of this study was to develop and apply a new Physics-Aware Deep Learning Framework that identifies and utilizes physical attributes of solar irradiance via Fourier-based signal decomposition. The proposed method decomposes the time-series into polynomial trend, Fourier-based seasonal component and stochastic residual, each of which are processed within different neural network paths. A wide variety of architectures were tested (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN)), at multiple historical window sizes and forecast horizons on a diverse dataset from a three-year span. All of the architectures tested demonstrated improved accuracy and robustness when using the physics aware decomposition as opposed to all other methods. Of the architectures tested, the GRU architecture was the most accurate and performed well in terms of overall evaluation. The GRU model had an RMSE of 78.63 W/m2 and an R2 value of 0.9281 for 15 min ahead forecasting. Additionally, the Fourier-based methodology was able to reduce the maximum absolute error by approximately 15% to 20%, depending upon the architecture used, and therefore it provided a way to reduce the impact of the larger errors in forecasting during periods of unstable weather. Overall, this framework represents a viable option for both physically interpretive and computationally efficient real-time solar forecasting that provides a bridge between Physical Modeling and Data-Driven Intelligence. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Development)
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17 pages, 32387 KB  
Article
Neural Network Architectures for Secure and Sustainable Data Processing in E-Government Systems
by Shadi AlZu’bi, Fatima Quiam, Ala’ M. Al-Zoubi and Muder Almiani
Algorithms 2025, 18(10), 601; https://doi.org/10.3390/a18100601 - 25 Sep 2025
Cited by 2 | Viewed by 865
Abstract
In the digital transformation of public services, reliable and secure data handling has become central to effective E-government operations. This study introduces a symmetry-driven neural network architecture tailored for secure, scalable, and energy-efficient data processing. The model integrates weight-sharing and symmetrical configurations to [...] Read more.
In the digital transformation of public services, reliable and secure data handling has become central to effective E-government operations. This study introduces a symmetry-driven neural network architecture tailored for secure, scalable, and energy-efficient data processing. The model integrates weight-sharing and symmetrical configurations to enhance efficiency and resilience. Experimental validation on three E-government datasets (95,000–230,000 records) demonstrates that the proposed model improves processing speed by up to 40% and enhances adversarial robustness by maintaining accuracy reductions below 2.5% under attack scenarios. Compared with baseline neural networks, the architecture achieves higher accuracy (up to 95.1%), security (up to 98% attack prevention), and efficiency (processing up to 1600 records/sec). These results confirm the model’s applicability for large-scale, real-time E-government systems, providing a practical path for sustainable and secure digital public administration. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Development)
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