Director

Eugenio Morocho, Ph.D.

Integrantes

1. Israel Gustavo Pineda Arias
2. Diego Hernán Suntaxi
3. Oscar Vicente Guarnizo Cabezas
4. Fabricio Crespo (estudiante)
5. Samantha Quintanchala (estudiante)
6. Tito Rolando Armas (secretario)
7. Rigoberto Fonseca

 

 

 

Mision

Understand the ever changing problems in our society. Develop technological solutions to help people to solve those problems. Accelerate the dissemination of Yachay Tech’s scientific production.

Objetivos

Realizar investigación aplicada a la implementación de procesos inteligentes orientados a resolver problemas contemporáneos y enriquecer los estándares de calidad del país.

Proyectos

Exploring the Impact of Artificial Intelligence in Emerging Technologies

Publicaciones

Año Título Link
2024 A Study of ConvNeXt Architectures for Enhanced Image Captioning https://ieeexplore.ieee.org/document/10410861
2023 Deep Reinforcement Learning for Efficient Digital Pap Smear Analysis https://www.mdpi.com/2079-3197/11/12/252
2023 Hyperparameter Tuning in a Dual Channel U-Net for Medical Image Segmentation https://link.springer.com/chapter/10.1007/978-3-031-45438-7_23
2023 Unraveling the Power of 4D Residual Blocks and Transfer Learning in Violence Detection https://link.springer.com/chapter/10.1007/978-3-031-45438-7_14
2023 Mask R-CNN and YOLOv8 Comparison to Perform Tomato Maturity Recognition Task Link
2022 Implementation of a Lightweight CNN for American Sign Language Classification https://link.springer.com/chapter/10.1007/978-3-031-18347-8_16
2022 Comparative Study of Image Degradation and Restoration Techniques https://link.springer.com/chapter/10.1007/978-3-031-18272-3_17
2022 Plant Disease Classification and Severity Estimation: A Comparative Study of Multitask Convolutional Neural Networks and First Order Optimizers https://link.springer.com/chapter/10.1007/978-3-031-04447-2_21
2022 Deep Learning Approaches Based on Transformer Architectures for Image Captioning Tasks https://ieeexplore.ieee.org/document/9739703
2022 End-to-End License Plate Recognition System for an Efficient Deployment in Surveillance Scenarios https://link.springer.com/chapter/10.1007/978-3-030-96293-7_59