ACEVES FERNANDEZ, MARCO ANTONIO
CIENCIAS TECNOLÓGICAS · Nivel 2
Desde 2008 es Full professor (Tenure) / Senior Researcher en Universidad Autonoma de Queretaro
Marco Antonio Aceves-Fernández received the B.Sc. degree in telematics engineering from Colima University, Mexico, and the M.Sc. and Ph.D. degrees...
Marco Antonio Aceves-Fernández received the B.Sc. degree in telematics engineering from Colima University, Mexico, and the M.Sc. and Ph.D. degrees in the field of intelligent systems from the University of Liverpool, U.K. He is a full- time Professor with the Engineering Faculty, Autonomous University of Querétaro, Mexico. His research interest includes intelligent and embedded systems. He has been a member of the National Systems of Researchers (SNI), since 2009. He is also the Honorary President of the Mexican Association of Embedded Systems (AMESE), IEEE senior member, regular member of the Mexican Association of Computing and a Board Member for many Institutions and Associations.
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| Nombre | MARCO ANTONIO ACEVES FERNANDEZ |
|---|---|
| Área | INGENIERÍAS Y DESARROLLO TECNOLÓGICO |
| Campo | CIENCIAS TECNOLÓGICAS |
| Disciplina | COMPUTACIÓN |
| Especialidad | COMPUTACION PARA INGENIERIA |
| CVU | 39559 |
| Institución | |
|---|---|
| Tipo de institución | Pública |
| Dependencia | FACULTAD DE INGENIERIA |
| Entidad | QUERETARO |
| Nivel | 2 |
| Vigencia | Inicio: 01/01/2025 |
| Fin: 31/12/2029 |
Evolución en el SNII
- Candidato: 2010–2013
- Nivel I: 2014–2024
- Nivel II: 2025–2026
Últimas publicaciones Scopus
- 2027
- Characterization of the One-Dimensional Wavelet Profilometry Method with Comparison of Phase Unwrapping Algorithms Communications in Computer and Information Science · Vol. 3228 CCIS Conference Paper
- 2026
- Artificial Intelligence for Engineering Solutions: Trends, Challenges, and Future Directions Eng · Vol. 7 Editorial
- Semantic Firewalls with Online Ensemble Learning for Secure Agentic RAG Systems in Financial Chatbots AI Switzerland · Vol. 7 Article
- Proposal of a Modified Loss Function with the Gaussian Copula Density Function to Improve LSTM Predictions of PM10 and PM2.5 Concentrations Computers · Vol. 15 Article
- 2025
- Deep Learning Techniques for Retinal Layer Segmentation to Aid Ocular Disease Diagnosis: A Review Computers · Vol. 14 Review