Completed Research
The team:
Roberto Raul Castro Izurieta (Main Researcher - Yachay Tech University)
Israel Pineda (Yachay Tech University)
Wansu Lim (Kumoh National Institute of Technology)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
Source code:
Slides:
Computer vision to identify the incorrect use of face masks for COVID-19 awareness
The team:
Jonnathan Fabricio Crespo Yaguana (Main Researcher - Yachay Tech University)
Luz Marina Sierra Martinez (Universidad del Cauca)
Diego Hernán Peluffo-Ordóñez (Mohammed VI Polytechnic University)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
Source code:
Slides:
The Future of Agriculture: Detecting Tomato Maturity Levels with YOLOv8
The team:
Jean Carlo Camacho Espín (Main Researcher - Yachay Tech University)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
Source code:
Avaliable on GitHub soon
Slides:
Available soon (after Jean Carlo's thesis defense)
A web application to learn sign language with deep learning
Deep learning and computer vision are used to create applications that facilitate a better interaction between humans and machines. In the educational domain, obtaining information about sign language is simple, but finding a platform that allows for intuitive interaction is quite challenging. A web app has been developed to address this issue by employing deep learning to assist users in learning sign language. In this study, two models for hand-gesture recognition were tested, utilizing 20,800 images; the models tested were Alexnet and GoogLeNet. The overfitting problem encountered in convolutional neural networks has been considered while training these models. Several techniques to minimize the overfitting and improve the overall accuracy have been employed in this study. AlexNet achieved an 87\% of accuracy rate when interpreting hand gestures whereas GoogLeNet achieved an 85\% accuracy rate. These results were incorporated into the web app, which aims to teach the alphabet of American sign language intuitively
The team:
Bryan Eduardo Jami Jami (Yachay Tech University)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
To be submitted
Source code:
Avaliable soon
Slides:
Available soon
Deep reinforcement learning for efficient nucleus cell location in digital pap smears
The team:
Carlos Julio Macancela Bojorque (Main Researcher - Yachay Tech University)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Oscar Chang (Yachay Tech University)
Results presented on:
To be submitted
Source code:
Avaliable soon
Slides:
Septorhinoplasty prediction with Denoising Diffusion Probabilistic Models
Septorhinoplasty is a complex surgical procedure aimed at improving the functional and aesthetic aspects of the nose. In this study, we propose a novel approach for predicting post-septorhinoplasty outcomes using Denoising Diffusion Probabilistic Models (DDPM). The methodology involves generating synthetic post-surgery images from preoperative patient photos through the application of DDPM-induced noise. Subsequently, a U-Net architecture is employed to denoise the synthesized images and predict the potential postoperative appearance.
The first step of our framework involves training the DDPM on a large dataset of pre-septorhinoplasty patient images to learn the underlying distribution of natural nose variations. This trained model is then used to synthesize a diverse set of postoperative nose images by introducing diffusion-based noise to the original preoperative images. The introduction of noise helps capture the inherent uncertainty and variability present in real-world surgical outcomes.
Next, we employ a U-Net, a popular architecture for image denoising, to process the synthetic postoperative images generated by the DDPM. The U-Net is trained on paired data consisting of the original preoperative images and their corresponding noise-introduced counterparts. This supervised training process enables the U-Net to effectively remove the induced noise while preserving the essential features of the post-surgery nose appearance.
Technical requitements:
The student is expected to use a dataset of images from rhinoplasty surgeries and generate an image based on CGANs (Conditional Generative Adversarial Networks).
The new image will be generated from a photo of a patient before the surgery.
The team:
Jonathan Javier Loor Duque (Yachay Tech University)
Dra. Rosaura Yokasta Bravo Pita (Hospital Quito N1 Policía Nacional)
Dr. Freddy Raúl Guzmán Suarez (Hospital Quito N1 Policía Nacional)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
To be submitted
Source code:
Avaliable soon
Slides:
Available soon
Ongoing Research
Decentralized-based blockchain system for enhancing the management of scientific publications
The team:
Julio Rogers Cajas Guncay (Main Researcher - Yachay Tech University)
Juan Fernando Riofrio Valarezo (Yachay Tech University)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
To be submitted
Source code:
Avaliable soon
Slides:
Avaliable soon
Knowledge Curation in the Digital Age: A Deep Learning-based Natural Language Processing Approach
The team:
Leo Thomas Ramos (Main Researcher - Yachay Tech University)
Juan Sebastián Ochoa Zambrano (Universidad Politécnica de Madrid)
Juan Garbajosa (Universidad Politécnica de Madrid)
Manuel Eugenio Morocho Cayamcela (Yachay Tech University)
Hypothesis:
Does human interaction empower collective intelligence?
Resources:
Results:
To be submitted
Source code:
Avaliable soon
Slides:
Available soon
Boosting Image Captioning Using ConvNeXt Deep Neural Networks
The team:
Leo Thomas Ramos (Main Researcher - Yachay Tech University)
Francklin Iván Rivas Echeverría (Yachay Tech University)
Manuel Eugenio Morocho Cayamcela (Yachay Tech University)
Results:
Under review
Source code:
Avaliable soon
Slides:
Available soon
Deep reinforcement learning for dynamic electromagnetic spectrum access
Required knowledge:
The team:
Mateo Sebastián Lomas (Main Researcher - Yachay Tech University)
Oscar Chang (Yachay Tech University)
Wansu Lim (Kumoh National Institute of Technology)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
To be submitted
Source code:
Avaliable soon
Slides:
Available soon
Deep learning hyperparameter optimization via genetic algorithms
The team:
Krishna Román (Main Researcher - Yachay Tech University)
Paola Quiloango (Yachay Tech University)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Rolando Armas (Yachay Tech University)
Results presented on:
To be submitted
Source code:
Avaliable soon
Slides:
Available soon
Development of a Generative Language Model-Based Supplementary Tool for High School and College Students Using META Language Models
The team:
Darío Sebastián Cabezas Erazo (Main Researcher - Yachay Tech University)
Rigoberto Fonseca Delgado (Yachay Tech University)
Paulina Vizcaino (Universidad Internacional del Ecuador - UIDE) - Co-advisor
Iván Reyes (Universidad Internacional del Ecuador - UIDE) - Co-advisor
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
To be submitted
Source code:
Slides:
End-to-end Sign Language Translation with Stochastic and Natural Language Processing Transformers (BERT)
The team:
Andrés Fabricio Quelal Flores (Main Researcher - Yachay Tech University)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
Source code:
Avaliable soon
Slides:
Available soon
Artificial intelligence-based eye-tracking algorithm as a neuro-scientific instrument
Technical requirements:
Develop and optimize an eye-tracking algorithm using artificial intelligence.
Analyze the social impact of the developed technology.
Potential applications:
Marketing focalization in websites. Political message analysis.
Introduction: Hypothesis (define dependent -reaction-, and independent variables -action-)
Hypothesis: website (communication channel).
Independent variable: Publicity placement in the website, content placement in the website.
Independent co-variables: Color, typography, grid divisions (right, left, top, bottom, etc.).
Co-variables: Social status (different universities -public universities-), gender, age, political view.
Dependent variable: Emotions, facial expression, etc.
Theoretical Framework: (Eye tracking + Social impact)
State of the Art: Eye tracking for marketing focalization (Technical + Social impact). Find Metric.
The team:
Saul Figueroa (Yachay Tech Uniersity)
Andrés Tirado (Yachay Tech Uniersity)
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University)
Results presented on:
To be submitted
Source code:
Avaliable soon
Slides:
Available soon
Open Research Calls for Yachay Students
If you are a Final Graduation Project student, you can apply for supervision on the following topics. The students will be the main researchers supervised by one of the DeepARC teams.
Plague pattern recognition based on inspection data collected in Galápagos
Description:
This project ams to read the database from the "Agencia de Regulación y Control de la Bioseguridad y Cuarentena para Galápagos". This database contains the information gathered by the units of inspection, which inspect all the ships,planes, etc. that enters the Galápagos Islands.
The plan:
Titulación I: The aim of this study is to find patters in the database that can help us to locate the ships,planes, etc. that introduce plagues to the islands. The hypothetical characteristics can be: the country of origin of the plane, the month of the year, the visiting motive, etc.
Titulación II: With the extracted patterns, the next step is to visualize the information (ARCGIS, etc), to help the units of inspection to optimize the inspection process.
Technical Requirements:
Basic knowledge of data mining, and machine learning.
Intermediate level in Python.
The team:
Jonathan Javier Loor Duque (Yachay Tech University)
Ariana Deyaneira Jiménez Narváez (Yachay Tech University)
Juan David Moromenacho (Universidad Internacional del Ecuador - UIDE)
Paulina Vizcaino (Universidad Internacional del Ecuador - UIDE) - Co-advisor
Iván Reyes (Universidad Internacional del Ecuador - UIDE) - Co-advisor
Andrés Castillo (Universidad Internacional del Ecuador - UIDE) - Co-advisor
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University) - Advisor
Results presented on:
To be submitted in an SCOPUS indexed conference/journal
Source code:
Avaliable soon on GitHub
Artificial intelligence-based model to help teachers grade academic writing essays
Functional requirements:
The grading system should be able to identify grammar, puntuation, spelling, capitalization, and other kinds of language errors.
The grading system should be able to provide a score based on the previous criteria.
Non-functional requitements:
The model shoud have a website-based front-end, intuitive and friendly for the final user.
Notes:
The use of open source libraries is encouraged (OpenAI-based APIs).
The team:
<Main Researcher>
Hamilton Quezada (Yachay Tech University, Language Center) - Co-advisor
Rigoberto Fonseca (Yachay Tech University) - Co-advisor
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University) - Advisor
Results presented on:
To be submitted
Source code:
Avaliable soon
An Evolutionary Approach for Deep Neural Network Parametrization
The team:
<Main Researcher>
Erick Cuenca (Yachay Tech University) - Co-advisor
Rolando Armas (Yachay Tech University) - Co-advisor
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University) - Advisor
Reading requisites:
Article: EvoDeep: a new Evolutionary approach for automatic Deep Neural Networks parametrisation
Code asociated with the article: Github
The dataset:
The student is free to select the application. That is, the imagery dataset to be used to train and test the DNN.
Results presented on:
To be submitted
Source code:
Avaliable soon
Machine learning as an enabler the next-generation WiFi networks
Technical requirements:
The student should have prior knowledge on wireless networks and machine learning.
The team:
<Main Researcher>
Juan Pablo Astudillo León (Yachay Tech University) - Co-advisor
Christian Tipantuña (Escuela Politécnica Nacional) - Co-advisor
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University) - Advisor
Results presented on:
To be submitted
Source code:
Avaliable soon
Online vs. offline computing: comparison, applications and use-cases
Required knowledge:
Technical requirements:
The student will be comparing the training complexity of machine learning models between offline hardware (e.g., NVIDIA Jetson Nano, Intel Neural Compute Stick 2, etc.), and current cloud computing hardware available (i.e., Google Colab, Kaggle Kernel, Azure Notbooks, etc.).
The team:
Leo Thomas Ramos (Yachay Tech University) - Author
Rolando Armas (Yachay Tech University) - Author
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University) - Author
Results presented on:
To be submitted
Source code:
Avaliable soon
Social media data to empower an AI-based sentiment analysis model
Technical requirements:
Use the information of a mobile App API (e.g., Twitter, Spotify, Youtube, Facebook, etc.) to develop a sentiment analysis model using AI.
Analyze the social impact of the developed technology.
Potential applications:
Political tendency prediction.
Recognition of social behavior patterns.
The team:
<Main Researcher>
Silvana Escobar (Yachay Tech Uniersity) - Co-advisor
Erick Cuenca (Yachay Tech Uniersity) - Co-advisor
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University) - Advisor
Results presented on:
To be submitted
Source code:
Avaliable soon
AI-based mass movement inventory model for early warning systems
Technical requirements:
Estimation of mass movement inventory (using supervised learning techniques)
Principal component analysis (PCA).
Pearson/Spearman correlation coefficient.
Potential applications:
Early warning system for mass movement.
Available data:
Orthophoto (from MAGAP 2014) RGB
Intensity of RGB channels.
Digital terrain model (DTM) (resolution 3m/pixel).
Altitude over sea level.
Slope on each point.
Difference in altitude between points.
Estimation of rivers.
Geology (type of rock i.e., clay, etc.).
Edaphology/Pedology (type of soil until 2m).
Soil coverage (i.e., foliage, grass, corn, etc.).
True positive, true negative, false positive, false negative. -> Expert labeled data
Mass movement inventory (type of mass movement, i.e., flow, falls, topless, debris flow, etc.) -> Expert labeled data
The team:
<Main Researcher>
María Gabriela Cajamarca (Yachay Tech University) - Co-advisor
Raisa Ivanova Torres-Ramírez (Yachay Tech University) - Co-advisor
Manuel Eugenio Morocho-Cayamcela (Yachay Tech University) - Advisor
Results presented on:
To be submitted
Source code:
Avaliable soon