Innovations in Artificial Neural Network Applications

Dear Colleagues,

Rapid advances in artificial intelligence (AI) are transforming society, promising faster and more sustainable technological progress. However, current AI models, including large language models (LLMs), face limitations such as hallucinations, unreliability, lack of interpretability, etc. For this Special Issue of Applied Sciences, we invite research contributions aimed at improving AI systems, with a focus on enhancing artificial neural networks (ANNs) for diverse applications across scientific disciplines. A key emphasis is on integrating symbolic logic approaches and first principles into AI methodologies.

Traditional AI systems often operate as (statistical) black boxes, offering predictions or results with limited insight into their reliability or underlying mechanisms. These challenges can be addressed by integrating first principles (and other modern scientific frameworks) and/or symbolic logic to enhance methods for quantifying predictive accuracy and relating results to some key statistical parameters. This approach holds the potential to transform AI tools into more transparent, interpretable, and robust systems, fostering greater trust and broader applicability. We welcome submissions addressing these intersections, paving the way for AI systems and ANNs that align more closely with scientific rigor and especially practical utility.

Relevant topics include, but are not limited to, the following:

• Enhancing artificial neural networks;
• The integration of symbolic logic and first principles;
• Transforming AI tools into more transparent and interpretable systems;
• Greater trust and broader applicability.

Dr. Iztok Peruš
Prof. Dr. Milan Terčelj
Guest Editors

Manuscript Submission Information

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