Examinando por Materia "Redes neuronales convolucionales"

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    Aplicación de técnicas de clusterización para la clasificación de música dance electrónica
    (Universidad EAFIT, 2023) Murillo Martínez, Carlos Alberto; Alunno, Marco; Martínez Vargas, Juan David
    Audio processing is one of the essential tasks for a data scientist, and audio analysis has applications in a diverse range of fields, such as medicine, telecommunications, improving sound quality in music production, and even military applications (filtering suspicious or terrorist audio). This project aims to use hard clustering techniques (such as k-means or k-nearest neighbor) and soft clustering techniques (such as fuzzy clustering) to classify input songs using different metrics. The classification methods will be used to segment previously processed input audios and obtain a sample of representative segments of the songs, determining their similarity with other songs of the same genre. Another technique that has proven effective for audio classification is convolutional neural networks (CNNs), which have been used in a wide range of fields. In the music field, they have been used to classify violin bowing techniques [1] and even detect potential heart problems using heartbeat sounds [2]. In this project, we will use this technique up to the point of feature extraction, and then use classical classification techniques to determine which group a section of a song belongs to.
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    Detección temprana de melanoma : aplicación de técnicas de procesamiento de imágenes y aprendizaje profundo
    (Universidad EAFIT, 2025) Lacouture Fierro, Juan David; Álvarez Barrera, Claudia Patricia
    Skin cancer is the most common type of cancer worldwide, with melanoma accounting for only 1% of cases but causing most deaths associated with this disease. In the United States, 97,610 new cases of melanoma were diagnosed in 2023, with a mortality rate of 7,990. In Colombia, the incidence of melanoma has increased significantly in recent years. According to the Cuenta de Alto Costo, 7,881 new cases were reported in 2024, with 11.94% of diagnoses concentrated in Bogotá and the Central region. Additionally, the total number of cases treated in the country increased from 53,622 in 2017 to more than 105,000 in 2021. These figures place Colombia as the fourth country in the Americas with the highest incidence of melanoma, highlighting the urgent need to implement innovative tools for early diagnosis. This project develops a deep learning model to diagnose melanoma through medical imaging, utilizing convolutional neural networks and advanced image processing techniques. The model includes data collection, training, and validation, aiming to deliver rapid and accurate diagnoses. The research encourages for the integration of artificial intelligence into medical practice, enabling early diagnosis in regions with limited access to specialists and alleviating the burden on the healthcare system. In conclusion, this initiative represents a milestone in dermatological care in Colombia, benefiting both high-incidence areas and rural communities.
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    Modelos de clasificación de emociones basados en CNN y ViT
    (Universidad EAFIT, 2024) Ruiz Ramírez, Santiago; Montoya Múnera, Edwin Nelson
    The present project focuses on comparing the performance of convolutional neural network (CNN) and vision transformer (ViT) models to classify emotions in facial images. The problem lies in the accuracy of CNNs, which still faces challenges, while ViTs have emerged as a promising alternative, highlighting the importance of addressing emotions in the context of mental health, as these can influence the ability to creative work and are linked to different clinical study conditions.
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    Variability modeling language and tool to represent, configure and evaluate Convolutional Neural Network architectures
    (Universidad EAFIT, 2024) Murillo Portocarrero, Julián Alexander; Mazo Peña, Raúl
    The process of designing Convolutional Neural Network (CNN) architectures currently relies on manual design or the involvement of experts. This process is not only time-consuming but also expensive due to the sheer number of combinations that architects need to do to arrive at the right network hyper-parameters that fit the current problem. In this work, we analyze the current state-of-the-art, in which several approaches have been proposed to automate such processes and explore alternatives for the automated design of Convolutional Neural Networks and Neural Architecture Search (NAS). Additionally, this work proposes a method for hyper-parameter variability generation from a variability model of such convolutional neural networks. The variability model proposed in this master thesis is used to represent, intensively, the valid combinations of parameters corresponding to each convolutional neural network. The language, called CNN variability language, borrows some concepts from Software Product Lines (SPL) and was created on the VariaMos platform to enable architects and engineers not just to create CNN architectures but also to automatically generate configurations, generate executable Jupyter Notebooks for each configuration, and generate comparison reports to speed up the NAS process.