Next-Gen Neural Networks: Robust, Interpretable and Energy Aware AI Systems
A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 382
Editors
Interests: artificial intelligence; computational optimization; intelligent optimization; computational finance; mathematical finance
Special Issues, Collections and Topics in MDPI journals
Interests: neural networks; nonlinear optimization; optimal control; robotic planning
Special Issues, Collections and Topics in MDPI journals
2. Section of Mathematics, Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: numerical analysis; scientific computing; applied numerical analysis; computational chemistry; computational material sciences; computational physics; parallel algorithm and expert systems
Special Issues, Collections and Topics in MDPI journals
Interests: portfolio optimization; big data; fintech management and decision making; fraud detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Neural networks remain the backbone of modern AI, enabling breakthroughs in computer vision, NLP, robotics, healthcare, and autonomous systems. However, next-generation neural models must address critical gaps in explainability, robustness, efficiency, and adaptability. This Special Issue seeks high-impact contributions—both theoretical and applied—that will chart the next wave of neural-network advancements.
Aims
- Highlight novel neural architectures, optimization strategies, and applications that meaningfully extend current capabilities.
- Advance the state of knowledge in key areas: energy efficiency, interpretability, security, and hybrid learning paradigms.
- Bridge foundational research and real-world deployment, showcasing high-impact case studies.
Target Contributions
We welcome the following topics:
- New architectures: transformers, GNNs, SNNs, quantum neural networks;
- Learning techniques: federated/self-supervised/edge-efficient training;
- Interpretability and explainability methods;
- Robustness: adversarial defenses, privacy-aware models, bias mitigation;
- Hybrid AI: neuro-symbolic systems, neuroevolution, quantum-ML hybrids;
- Applications in healthcare, finance, Industry 4.0;
- Human–AI collaboration interfaces and interactive systems;
- Reviews or meta-analyses that survey architectures, applications, or future research directions.
Prof. Dr. Vasilios N. Katsikis
Prof. Dr. Shuai Li
Prof. Dr. Theodore E. Simos
Prof. Dr. Xinwei Cao
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics 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 2600 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
- explainable AI
- transfer learning
- edge AI
- federated learning
- self supervised learning
- adversarial robustness
- neuro symbolic AI
- quantum neural networks
- efficient AI
- real world applications
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