Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Smart camera FPGA hardware implementation for semantic segmentation of wildfire imagery(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-13) Garduño Martínez, Eduardo; Rodriguez Hernández, Gerardo; mtyahinojosa, emipsanchez; Gonzalez Mendoza, Miguel; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, GilbertoIn the past few years, the more frequent occurrence of wildfires, which are a result of climate change, has devastated society and the environment. Researchers have explored various technologies to address this issue, including deep learning and computer vision solutions. These techniques have yielded promising results in semantic segmentation for detecting fire using visible and infrared images. However, implementing deep learning neural network models can be challenging, as it often requires energy-intensive hardware such as a GPU or a CPU with large cooling systems to achieve high image processing speeds, making it difficult to use in mobile applications such as drone surveillance. Therefore, to solve the portability problem, an FPGA hardware implementation is proposed to satisfy low power consumption requirements, achieve high accuracy, and enable fast image segmentation using convolutional neural network models for fire detection. This thesis employs a modified UNET model as the base model for fire segmentation. Subsequently, compression techniques reduce the number of operations performed by the model by removing filters from the convolutional layers and reducing the arithmetic precision of the CNN, decreasing inference time and storage requirements and allowing the Vitis AI framework to map the model architecture and parameters onto the FPGA. Finally, the model was evaluated using metrics utilized in prior studies to assess the performance of fire detection segmentation models. Additionally, two fire datasets are used to compare different data types for fire segmentation models, including visible images, a fusion of visible and infrared images generated by a GAN model, fine-tuning of the fusion GAN weights, and the use of visible and infrared images independently to evaluate the impact of visible-infrared information on segmentation performance.
- Aspect based sentiment analysis in students’ evaluation of teaching(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Acosta Ugalde, Diego; Conant Pablos, Santiago Enrique; mtyahinojosa, emipsanchez; Guitérrez Rodríguez, Andrés Eduardo; Juárez Jiménez, Julio Antonio; Morales Méndez, Rubén; School of Engineering and Sciences; Campus Monterrey; Camacho Zuñiga, ClaudiaStudent evaluations of teachings (SETs) are essential for assessing educational quality. Natural Language Processing (NLP) techniques can produce informative insights from these evaluations. The large quantity of text data received from SETs has surpassed the capacity for manual processing. Employing NLP to analyze student feedback offers an efficient method for understanding educational experiences, enabling educational institutions to identify patterns and trends that might have been difficult, if not impossible, to notice with a manual analysis. Data mining using NLP techniques can delve into the thoughts and perspectives of students on their educational experiences, identifying sentiments and aspects that may have a level of abstraction that the human analysis cannot perceive. I use different NLP techniques to enhance the analysis of student feedback in the form of comments and provide better insights and understanding into factors that influence students’ sentiments. This study aims to provide an overview of the various approaches used in NLP and sentiment analysis, focusing on analyzing the models and text representations used to classify numerical scores obtained from the text feedback of a corpus of SETs in Spanish. I provide a series of experiments using different text classification algorithms for sentiment classification over numerical scores of educational aspects. Additionally, I explore two Aspect Based Sentiment Analysis (ABSA) models, a pipeline and a multi-task approach, to extract broad and comprehensive insights from educational feedback for each professor. The results of this research demonstrate the effectiveness of using NLP techniques for analyzing student feedback. The sentiment classification experiments showed favorable outcomes, indicating that it is possible to utilize student comments to classify certain educational scores accurately. Furthermore, the qualitative results obtained from the ABSA models, presented in a user-friendly dashboard, highlight the efficiency and utility of employing these algorithms for the analysis of student feedback. The dashboard provides valuable insights into the sentiments expressed by students regarding various aspects of their educational experience, allowing for a more comprehensive understanding of the factors influencing their opinions. These findings highlight the potential of NLP in the educational domain, offering a powerful tool for institutions to gain a deeper understanding of student perspectives and make data-driven decisions to enhance the quality of education.
- Caption generation with transformer models across multiple medical imaging modalities(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06) Vela Jarquin, Daniel; Santos Díaz, Alejandro; dnbsrp; Soenksen, Luis Ruben; Montesinos Silva, Luis Arturo; Ochoa Ruiz, Gilberto; School of Engineering and Sciences; Campus Monterrey; Tamez Peña, José GerardoCaption generation is the process of automatically providing text excerpts that describe relevant features of an image. This process is applicable to very diverse domains, including healthcare. The field of medicine is characterized by the vast amount of visual information in the form of X-Rays, Magnetic Resonances, Ultrasound and CT-scans among others. Descriptive texts generated to represent this kind of visual information can aid medical professionals to achieve a better understanding of the pathologies and cases presented to them and could ultimately allow them to make more informed decisions. In this work, I explore the use of deep learning to face the problem of caption generation in medicine. I propose the use of a Transformer model architecture for caption generation and evaluate its performance on a dataset comprised of medical images that range across multiple modalities and represented anatomies. Deep learning models, particularly encoder-decoder architectures have shown increasingly favorable results in the translation from one information modality to another. Usually, the encoder extracts features from the visual data and then these features are used by the decoder to iteratively generate a sequence in natural language that describes the image. In the past, various deep learning architectures have been proposed for caption generation. The most popular architectures in the last years involved recurrent neural networks (RNNs), Long short-term memory (LSTM) networks and only recently, the use of Transformer type architectures. The Transformer architecture has shown state-of-the art performance in many natural language processing tasks such as machine translation, question answering, summarizing and not long ago, caption generation. The use of attention mechanisms allows Transformers to better grasp the meaning of words in a sentence in a particular context. All these characteristics make Transformers ideal for caption generation. In this thesis I present the development of a deep learning model based on the Transformer architecture that generates captions for medical images of different modalities and anatomies with the ultimate goal to aid professionals improve medical diagnosis and treatment. The model is tested on the MedPix online database, a compendium of medical imaging cases and the results are reported. In summary, this work provides a valuable contribution to the field of automated medical image analysis