Advances in Machine Learning and Artificial Intelligence: Theory and Applications

Dear Colleagues,

This Special Issue delves into the dynamic world of machine learning (ML) and artificial intelligence (AI), offering a comprehensive exploration of their foundational principles, cutting-edge theories, and transformative real-world applications. ML, a critical subset of AI, is revealed here as a fascinating intersection of mathematical concepts and data-driven learning, showcasing the profound mathematical underpinnings that drive intelligent systems.

Theoretical foundations elucidate the intricate mathematical algorithms, statistical constructs, and abstract mathematical paradigms that form the bedrock of ML and AI. From the mathematical intricacies of supervised and unsupervised learning to the mathematical foundations of reinforcement and deep learning, this Special Issue will unravel the intricate tapestry of ML techniques that empower machines to glean insights from data, make decisions, and emulate human-like reasoning.

Furthermore, this Special Issue delves into the diverse applications of ML and AI across multifarious domains. It showcases the transformative impact of these technologies in healthcare, finance, manufacturing, energy, psychology and beyond, where they augment human capabilities, enhance decision-making, and catalyze innovation.

In essence, this Special Issue serves as a guiding light for mathematicians, researchers, practitioners, and enthusiasts alike, shedding mathematical insight on the theory and practice of ML and AI, and inspiring fresh mathematical perspectives on harnessing their potential to shape our increasingly mathematical, intelligent, and automated world.

Prof. Dr. María del Carmen Pegalajar Jiménez
Dr. Luis G. Baca Ruiz
Guest Editors

Manuscript Submission Information

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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.

• machine learning
• applications of artificial intelligence
• data analysis
• intelligent systems
• artificial reasoning
• deep learning
• time series
• data mining
• artificial neural networks
• mathematical algorithms
• multidomain applications

Title: Brain-inspired agents for Quantum Reinforcement Learning
Authors: E. Andrés; Manuel Pegalajar Cuellar; Gabriel Navarro
Affiliation: University of Granada
Abstract: In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.