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Recent Advances and Applications of Artificial Neural Networks
This special issue belongs to the section “E1: Mathematics and Computer Science“.
Special Issue Information
Dear Colleagues,
Artificial Neural Networks (ANNs) have become a central pillar of modern artificial intelligence, enabling breakthroughs in perception, language understanding, decision-making, and scientific discovery. Rapid progress in deep learning architectures, learning paradigms, optimization strategies, and hardware-aware deployment continues to expand the reach of neural methods into increasingly complex real-world settings.
This Special Issue, “Recent Advances and Applications of Artificial Neural Networks”, invites high-quality original research and survey articles that present novel ANN models, theoretical insights, training and regularization techniques, explainability and robustness methods, efficient and sustainable neural computing, and impactful applications across domains. We particularly welcome contributions that bridge methodological innovation with measurable practical benefits, including reproducibility and rigorous evaluation.
Topics of interest include (but are not limited to):
- Novel ANN architectures (CNNs, RNNs, Transformers, GNNs, spiking neural networks)
- Self-supervised, weakly supervised, and semi-supervised learning
- Federated, distributed, continual, and lifelong learning
- Neural architecture search, pruning, quantization, and model compression
- Optimization, regularization, and training stability
- Trustworthy AI: robustness, reliability, uncertainty, interpretability, fairness
- Edge/IoT deployment, real-time inference, energy-efficient neural computing
- Multimodal learning and data fusion
- Applications in healthcare, bioinformatics, finance, cybersecurity, manufacturing, robotics
- Computer vision, NLP, speech/audio, time-series forecasting, and anomaly detection
- ANN applications in scientific computing and engineering (digital twins, surrogate models)
Dr. Lien Minh Dang
Prof. Dr. Hyeonjoon Moon
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. 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
- artificial neural networks
- deep learning
- transformer networks
- graph neural networks
- spiking neural networks
- representation learning
- model compression
- explainable AI
- robust learning
- edge AI
- multimodal learning
- real-world applications
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