Mathematical Methods and Artificial Intelligence in Information Recognition

Dear Colleagues,

This Special Issue of the journal Mathematics, entitled “Mathematical Methods and Artificial Intelligence in Information Recognition”, aims to present recent advances, methodologies, and applications that combine mathematical foundations with artificial intelligence (AI) and deep learning techniques for information recognition.

AI has become a cornerstone in addressing complex challenges of information recognition, where massive and heterogeneous data must be efficiently processed, analyzed, and interpreted. Rapid progress in the development of deep learning techniques has produced transformative breakthroughs, yet the success of these techniques is deeply rooted in solid mathematical principles, including optimization theory, statistical learning, linear algebra, and probability models. These mathematical frameworks not only provide theoretical guarantees for AI algorithms, but also ensure robustness, scalability, and generalization.

In medical imaging and healthcare, AI-driven models supported by advanced mathematical methods have significantly improved disease detection, segmentation, diagnosis, and treatment planning. Large language models (LLMs), underpinned by linear algebra, optimization, and information theory, have revolutionized natural language understanding, enabling progress in knowledge extraction, medical report generation, clinical decision support, and multimodal information recognition. Advances in core deep learning methodologies, such as novel architectures, regularization strategies, and self-supervised learning, highlight the synergy between mathematical theory and algorithmic innovation. Moreover, broader applications beyond medicine, including finance, security, smart cities, and industrial systems, demonstrate the wide-ranging impact of combining mathematics and AI in solving real-world recognition problems.

This Special Issue seeks to provide a comprehensive platform for disseminating significant contributions that connect mathematical methods with AI applications. It invites original research articles, reviews, and case studies that advance both the mathematical foundations and practical implementations of AI in information recognition.

Dr. Olarik Surinta
Guest Editor

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.

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