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New Trends in Neural Networks and Artificial Intelligence

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1167

Special Issue Editors


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Guest Editor
Associate Professor, Department of Mathematical Sciences and Computing, Faculty of Natural Sciences, Walter Sisulu University, Mthatha, South Africa
Interests: artificial intelligence; computer vision; machine learning; explanable AI; optimization; ethical AI

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Guest Editor
Department of Informatics, University of Pretoria, Pretoria P.O. Box 0028, South Africa
Interests: artificial intelligence; machine learning; deep learning; large language models; natural language processing (NLP); knowledge engineering
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Associate Professor, Department of Information Technology, Faculty of Information and Communication Technology, Soshanguve Campus, Tshwane University of Technology, Pretoria 0152, South Africa
Interests: wireless communication systems; cybersecurity; blockchain technology; Internet of Things; cloud computing; machine learning; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and neural networks are evolving rapidly, enabling breakthroughs in deep learning, reinforcement learning, generative modeling, and explainable AI. This special issue aims to highlight innovative research and applications that advance the theory, methodology, and practical deployment of AI. We invite original contributions that address novel architectures, training strategies, optimization methods, and interdisciplinary applications across healthcare, finance, robotics, climate science, and more. Submissions that explore ethical, responsible, and trustworthy AI are particularly welcome.

This Special Issue highlights recent advances and emerging trends in neural networks and artificial intelligence. It welcomes contributions on novel architectures, learning paradigms, optimization techniques, and applications across healthcare, finance, robotics, and more, with emphasis on explainable, ethical, and responsible AI that bridges theory and practice.

Dr. Ibidun Christiana Obagbuwa
Prof. Dr. Daramola J. Olawande
Dr. Topside E. Mathonsi
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. Applied Sciences 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

  • neural networks
  • deep learning
  • reinforcement learning
  • generative AI
  • explainable AI
  • trustworthy AI
  • transfer learning
  • federated learning
  • edge AI
  • NLP
  • computer vision
  • optimization methods
  • ethical AI
  • responsible AI
  • AI applications

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Published Papers (1 paper)

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Research

28 pages, 4043 KB  
Article
Comparative Benchmarking of Multi-Objective Algorithms for Renewable Energy System Design Using Pareto Front Quality Metrics
by Raphael I. Areola, Abayomi A. Adebiyi and Dwayne J. Reddy
Appl. Sci. 2026, 16(8), 3775; https://doi.org/10.3390/app16083775 - 12 Apr 2026
Viewed by 641
Abstract
Selecting the best multi-objective algorithms for photovoltaic energy storage system (PV-ESS) design remains challenging due to limited benchmarking across renewable energy studies. This study addresses this gap through a systematic evaluation of four widely used multi-objective optimization algorithms: NSGA-II, Multi-Objective Particle Swarm Optimization [...] Read more.
Selecting the best multi-objective algorithms for photovoltaic energy storage system (PV-ESS) design remains challenging due to limited benchmarking across renewable energy studies. This study addresses this gap through a systematic evaluation of four widely used multi-objective optimization algorithms: NSGA-II, Multi-Objective Particle Swarm Optimization (MOPSO), weighted-sum scalarization, and ε-constraint methods. Performance assessment utilized three Pareto front quality metrics: Inverted Generational Distance (IGD) for convergence quality, hypervolume (HV) for objective-space coverage, and spacing for solution distribution uniformity. The algorithms were tested on PV-ESS design problems in three developing economies (Nigeria, South Africa, India) under identical problem formulations and computational resources. NSGA-II achieved superior performance across all metrics in all three case studies. For convergence quality, NSGA-II attained a mean IGD of 0.0083, outperforming MOPSO by 29%, ε-constraint by 64%, and weighted-sum by 131%. For objective-space coverage, NSGA-II achieved a mean HV of 0. 700, representing 10–16% better coverage than other methods. For solution distribution, NSGA-II showed a mean spacing of 0.076, indicating 30–117% more uniform Pareto fronts. Computational efficiency analysis revealed that NSGA-II’s runtime is between 5.5 and 7.8 h per case, providing better quality–time ratios compared to ε-constraint methods (which are 18 times slower), while avoiding MOPSO’s premature convergence. Statistical validation confirmed NSGA-II’s superiority, with p < 0.01 across all quality metrics. These results establish NSGA-II as the best algorithm for lifecycle-aware PV-ESS optimization, offering quantitative, evidence-based guidance for practitioners selecting optimization tools for renewable energy system design. The demonstrated performance leads to $ 45,000–$ 60,000 lifecycle cost savings per MW/MWh of system capacity through improved Pareto front identification. Full article
(This article belongs to the Special Issue New Trends in Neural Networks and Artificial Intelligence)
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