Dr. OSCAR DIDIER SANCHEZ SANCHEZ
Resumen curricular:
Oscar D. Sanchez was born in Texcoco, Mexico, Mexico in 1988. He received the B.Sc. degree in Computer Engineering in 2011 from the University of Guadalajara, the M.Sc. degree in Electronics and Computer Engineering, and the Ph.D. degree in Electronic and Computer Science from the University of Guadalajara in 2013 and 2018, respectively. Since 2015 he belongs to the Computer Department at the University Center for Exact Sciences and Engineering. His research interests are modeling and identification of systems, bioinformatics, optimization and prediction with intelligent systems. Among the most relevant projects is the detection of failures in sensors for the prevention and maintenance of engines. In addition to working with deep neural networks for the detection and classification of diseases, mostly diabetes mellitus. He currently works at the University of Guadalajara where he carries out various research projects, mainly in the area of health and motors.
Perfil de Investigador SNII:
Bases de datos bibliográficas:
Scopus
ORCID
Publicaciones del académico:
- Online Imputation of Corrupted Glucose Sensor Data Using Deep Neural Networks and Physiological Inputs
- Convolutional Neural Networks for Robust Time Series Cleaning and Filtering
- Neural Network-Based Digital Twin for Hypertension Simulation
- Gaslike Social Motility: Optimization Algorithm with Application in Image Thresholding Segmentation
- Artificial Intelligence Innovations for Biomedical Engineering and Healthcare
- A Complex Network Epidemiological Approach for Infectious Disease Spread Control with Time-Varying Connections
- Fault-Tolerant Closed-Loop Controller Using Online Fault Detection by Neural Networks
- Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment
- Constrained Binary Optimization Approach for Pinned Node Selection in Pinning Control of Complex Dynamical Networks
- Intelligent Classification and Diagnosis of Diabetes and Impaired Glucose Tolerance Using Deep Neural Networks
- Development of an Artificial Intelligence System Based on the Windkessel Model for Arterial Hypertension Identification
- Intelligent Algorithms for On-Line Anomaly Detection in Photoplethysmography Signals
- Sensor Fault Tolerant Treatment for Type 1 Diabetes Mellitus Patients
- SEASONAL TIME-SERIES IMPUTATION OF GAP MISSING ALGORITHM (STIGMA)
- Real-Time Neural Control for Discrete Nonlinear Systems Under Unknown Input and State Disturbances
- Online Neural-Detection of False Data Injection Attacks on Financial Time Series
- Model-Free Neural Fault-Tolerant Control for Discrete-Time Unknown Nonlinear Systems
- Real-Time Neural Classifiers for Sensor Faults in Three Phase Induction Motors
- Real-Time Neural Classifiers for Sensor and Actuator Faults in Three-Phase Induction Motors
- Impulsive Pinning Control of Discrete-Time Complex Networks with Time-Varying Connections
- Feedback Control for Personalized Medicine
- Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks
- Learning impulsive pinning control of complex networks
- Neural identification of Type 1 Diabetes Mellitus for care and forecasting of risk events
- Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data
- Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data
- Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data
- Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data
- Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data