Marco Antonio Aceves-Fernández

Universidad Autonoma de Queretaro, Mexico

Dr. Marco Antonio Aceves-Fernández obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves-Fernández has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed to more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics applications. He is also an Honorary President of the National Association of Embedded Systems (AMESE), a member of the Mexican Academy of Computing (AMEXCOMP), a senior member of the IEEE, and a board member of many institutions and associations. His research interests include intelligent and embedded systems.

Marco Antonio Aceves-Fernández

6books edited

4chapters authored

Latest work with IntechOpen by Marco Antonio Aceves-Fernández

The interest within the academic community regarding AI has experienced exponential growth in recent years. Several key factors have contributed to this surge in interest. Firstly, the rapid advancements in AI technologies have showcased their potential to revolutionize various fields, such as healthcare, finance, and transportation, sparking curiosity and enthusiasm among researchers and scholars. Secondly, the availability of vast amounts of data and computing power has enabled academics to delve deeper into AI research, exploring complex algorithms and models to tackle real-world problems. Additionally, the interdisciplinary nature of AI has encouraged collaboration among experts from diverse fields like computer science, neuroscience, psychology, and ethics, fostering a rich exchange of ideas and approaches. With contributions from a diverse group of authors, this book offers a multifaceted perspective on machine learning and data mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these dynamic and influential disciplines.

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