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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- Interactive recipe suggestions for diet and allergen management: utilizing llaMA with HEI and DQI for healthier eating(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-15) Estrada Beltrán, Diego; González Mendoza, Miguel; emipsanchez; Gutiérrez Uribe, Janet Alejandra; Domínguez Uscanga, Astrid; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus MonterreyChoosing daily meals can be a complex and overwhelming task, especially when considering nutritional requirements, ingredient availability, preparation time, cooking complexity, dietary restrictions, and allergens. Inadequate nutrition is linked to a variety of health problems, including cardiovascular diseases, obesity, and psychological disorders, highlighting the need for effective dietary management solutions. Existing machine learning approaches, such as food recommender systems, recipe generators, and recipe completion models, often focus on suggesting ingredients or generating recipes based on training data and with some ingredients to start from, but they typically do not address the challenge of creating complete daily meal plans that meet personalized nutritional needs. The advent of Large Language Models (LLMs), including Meta’s LLaMA, OpenAI’s ChatGPT, and Google’s Gemini, offers a promising new avenue for enhancing personalized meal recommendations due to their accessibility and interactive capabilities. This thesis introduces a novel system that leverages LLaMA 3.1 combined with Retrieval-Augmented Generation (RAG) to provide daily meal suggestions tailored to individual users’ nutritional profiles, dietary preferences, and allergen restrictions. Our system evaluates meal recommendations against established nutritional metrics such as the Healthy Eating Index (HEI) and Diet Quality Index (DQI) to ensure they align with dietary guidelines and promote healthy eating. Through the integration of LLaMA’s advanced language understanding and RAG’s contextual retrieval capabilities, the system delivers precise, personalized, and accessible meal recommendations, offering a practical tool for improving dietary management and supporting healthier eating habits. The results demonstrate the effectiveness of this approach in addressing the complexities of meal planning, making it a valuable resource for individuals seeking to optimize their dietary choices through informed and interactive guidance.
- Towards a real-time lightweight facial reconstruction model(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-04-30) Hernández Manrique, Victor; González Mendoza, Miguel; emimmayorquin; Vilchis Zapata, Carlos Leonel; Luévano García, Luis Santiago; Rudomín Goldberg, Issac Juan; Escuela de Ingeniería y Ciencias; Campus Monterrey3D facial reconstruction algorithms are highly effective for diverse uses, including facial recognition, virtual reality, and medical imaging. Yet, the intricacy and computational demands of these methods, coupled with the limited availability of datasets, have confined their use to a specific set of researchers and experts. Furthermore, in response to the demand for resource-efficient solutions, the development of lightweight processes has become a key area of research in computer vision. These models aim to find an equilibrium between model size, computational demands, and accuracy. They offer advantages like efficient use of resources, quicker inference times, and enhanced accessibility. Particularly for 3D facial reconstruction models, lightweight architectures open up possibilities for deployment on less powerful hardware, given that these techniques typically depend on high-performance processors like NVIDIA graphics cards. This thesis presents an overview of 3D face creation, followed by state-of-the-art methods which were analyzed in a comparative table, offering an survey of the fundamental characteristics of each method. As well as that, a benchmark comparison among various leading lightweight models in a facial reconstruction framework, aiming to decrease its computational complexity to enable testing on a mobile device. A quantitative evaluation, such as its losses over the training and testing stages, the inference speed achieved and an evaluation in cutting-edge datasets were presented. Additionally, an analysis on the qualitative aspect, for example, the 3D pose or depth estimation. Those aspects were the base to select a lightweight backbone. Finally, an user interface was developed using Python and Kivy. The model was runned on a constrained-device, such as a single-core of a commercial laptop, to examine its performance. EfficientNetLite was determined as a suitable replacement for the current backbone, since its characteristics and scores obtained over several examinations presented a similar behavior to MobileNet-V1, the default backbone of the facial reconstruction model selected.
- Automatic detection and segmentation of prostate cancer using deep learning techniques(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-20) Quihui Rubio, Pablo César; González Mendoza, Miguel; puemcuervo, emimayorquin; Alfaro Ponce, Mariel; Mata Miquel, Christian; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, GilbertoProstate cancer is a major cause of death among men worldwide, and detecting it usually involves invasive procedures. Magnetic resonance imaging (MRI) has become a common research area for detecting this cancer because it represents a less invasive option. However, segmenting the prostate gland from MRI images can be a complicated task that requires expert opinions, which is both time-consuming and inconsistent. This thesis proposes a novel deep-learning architecture to automate and obtain accurate and reliable segmentation of the prostate gland in MRI scans. Precise segmentation is crucial for radiotherapy planning, as it determines the tumor’s location and size, which affects treat- ment effectiveness and reduces radiation exposure to surrounding healthy tissues. Therefore, a thorough comparison between architectures from the state-of-the-art is also performed. Convolutional neural networks have shown great potential in medical image segmenta- tion, but the uncertainty associated with their predictions is often overlooked. Therefore, this work proposes a novel approach incorporating uncertainty quantification to ensure reliable and trustworthy results. The models were evaluated on a dataset of prostate T2-MRI scans obtained in collab- oration with the Centre Hospitalarie Dijon and Universitat Politecnica de Catalunya. The results showed that the proposed architecture FAU-Net outperforms most existing models in the literature, with an improvement of 5% in the Dice Similarity Coefficient (DSC) and In- tersection over Union (IoU). However, the best model overall was R2U-Net, which achieved segmentation accuracy and uncertainty estimation values of 85% and 76% for DSC and IoU, respectively, with an uncertainty score lower than 0.05. In addition to the proposed model and comparison between models for prostate seg- mentation and uncertainty quantification, a web application was presented for easier access to the trained models in a clinical setting. This web app would allow medical professionals to upload MRI scans of prostate cancer patients and obtain accurate and reliable segmentation quickly and easily. This would reduce the time and effort required for manual segmentation and improve patient outcomes by facilitating better treatment planning. Overall, this work presents a novel strategy for prostate segmentation using deep learn- ing models and uncertainty quantification. The proposed method provides a reliable and trust- worthy segmentation while quantifying the uncertainty associated with the predictions. This research can benefit prostate cancer patients by improving treatment planning and outcomes.
- Sign language recognition with tree structure skeleton images and densely connected convolutional neural networks(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05) Laines Vázquez, David Alberto; González Mendoza, Miguel; puemcuervo; Sánchez Ante, Gildardo; Cantoral Ceballos, José Antonio; Méndez Vázquez, Andrés; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, GilbertoThis thesis presents a novel approach to Isolated Sign Language Recognition (ISLR) using skeleton modality data and deep learning. The study proposes a method that employs an image-based spatio-temporal skeleton representation for sign gestures and a convolu tional neural network (CNN) for classification. The advantages of the skeleton modality over RGB, such as reduced noise and smaller parameter requirements for processing, are taken into account. The aim is to achieve competitive performance with a low number of parameters compared to the existing state-of-the-art in ISLR. Informed by the literature on skeleton-based human action recognition (HAR), this research adapts the Tree Structure Skeleton Image (TSSI) method to represent a sign gesture as an image. The process in volves first extracting the skeleton sequences from sign videos using the MediaPipe frame work, which offers fast inference performance across multiple devices. The TSSI represen tation is then processed using a DenseNet, chosen for its efficiency and fewer parameters. The proposed method, called SL-TSSI-DenseNet, is trained and evaluated on two chal lenging datasets: the Word level American Sign Language (WLASL) dataset and the Ankara University Turkish Sign Language (AUTSL) dataset. Specifically, the WLASL-100 subset of the WLASL dataset and the RGB Track of the AUTSL dataset are selected for the experi ments. The results demonstrate that SL-TSSI-DenseNet outperforms other skeleton-based and RGB-based models benchmarked on the WLASL-100 dataset, achieving an accuracy of 81.47% through the use of data augmentation and pre-training. On the AUTSL dataset, it achieves competitive performance with an accuracy of 93.13% without pre-training and data augmentation. Additionally, an augmentation ablation study is conducted to iden tify the most effective data augmentation technique for the model’s performance on the WLASL-100 dataset. Furthermore, it provides insights into the effectiveness of various data augmentation techniques.
- Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for cerebral angiography(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-11) Herrera Montes de Oca, Daniela; González Mendoza, Miguel; puemcuervo, emipsanchez; Alfaro Ponce, Mariel; Mata Miquel, Christian; Falcon Morales, Luis Eduardo; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, GilbertoThe anatomical variations of the cerebrovascular system of infants may have an impact on the neurological development of the infants and may even lead to death. Intracranial vascular abnormalities include multiple diseases such as arteriovenous malformations and moyamoya. For evaluating this kind of diseases, there are non-invasive imaging techniques such as TRANCE which is a type of MRI. It allows the visualization of the vasculature isolated from the white matter. In the process of analyzing the images, they go through manual techniques for processing the images and segmenting the vessels depending on the patient. Besides the images are challenging to analyze due to the nature of the images they present noise related to their acquisition. This is difficult not only for the analysis and the diagnosis of the specialists but also for the performance of AI techniques. This thesis focuses on the problem of denoising these images and evaluating them using unsupervised methods due to the lack of clean images available and in the automation of segmentation. The purpose is to enhance the images for a better visual analysis of the specialist and segmentation for improving the quantification by doing feature extraction of the vessels. For doing so a pipeline was proposed. It consists of using a combination of traditional methods and deep learning-based unsupervised methods for denoising the images. The results were evaluated quantitatively using non-reference image quality evaluators and qualitatively by specialists. For the segmentation, a model was trained using noisy images. Then it was tested using the noisy images and the denoised ones. In total there were 6 comparisons of denoising techniques. The use of unsupervised denoising models utilizing noise2void and probabilistic Noise2Void, in contrast to the application of traditional approaches, as well as the combination of both was compared. The non-reference image quality evaluators, the NIQE and PIQE scores, were used to evaluate the results qualitatively and quantitatively. Using Noise2Void and PPN2V GMM produced the best outcomes, according to the scores. However, employing a combination of traditional methods and deep learning-based methods, the vessels showed a reduction of noise in the central and most dense areas, according to the qualitative results. The segmentation was done with a UNet model. The two approaches were compared. The results showed that training the model with noisy images and obtaining the segmentation using the denoised images showed an improvement of 9.4\% of the dice score and almost 16\% in the Hausdorff distance.
- Real time distraction detection by facial attributes recognition(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-09) López Esquivel, Andrés Alberto; González Mendoza, Miguel; puemcuervo; Gutiérrez Rodríguez, Andrés Eduardo; Marín Hernández, Antonio; School of Engineering and Sciences; Campus Monterrey; Chang Fernández, LeonardoThe deficit of attention on any critical activity has been a principal source of accidents leading to injuries and fatalities. Therefore the fast detection of it has to be a priority in order to achieve the safe completion of any task and also to ensure the display of the maximum capabilities of the user when achieving the respective activity. While multiple methods has been developed, a new trend of non-intrusive vision based methodologies has been strongly picked by both the research and industrial communities as one with the most potential effectiveness and usability on real life scenarios. In this thesis research, a new attention deficit detection system is presented. Low-weight Machine Learning algorithms will allow the use in remote applications and a variety of goal devices to avoid accidents caused by the lack of attention in complex activities. This research describes its impact, its functioning and previous work. In addition, the system is broken down into its most basic components and its results in various evaluation stages. Finally, its results in semi-real environments are presented and possible applications in real life are discussed, while being compared to state of the art implementations such as CNN’s, Deep learning and other ML implementations
- Analyzing fan avidity for soccer prediction(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-09) Miranda Peña, Ana Clarissa; GONZALEZ MENDOZA, MIGUEL; 123361; González Mendoza, Miguel; emijzarate/puemcuervo; Hernandez Gress, Neil; Alvarado Uribe, Joanna; Escuela de Ingeniería en Ciencias; Campus Monterrey; Hervert Escobar, LauraBeyond being a sport, soccer has built up communities. Fans showing interest, involvement, passion and loyalty to a particular team, something known as Fan Avidity, have strengthen the sport business market. Social Networks have made incredibly easy to identify fans’commitment and expertise. Among the corpus of sport analysis, plenty of posts with a well substantiated opinion on team’s performance and reliability are wasted. Based on graph theory, social networks can be seen as a set of interconnected users with a weighted influence on its edges. Evaluating the spread influence from fans' posts retrieved from Twitter could serve as a metric for identifying fans’ intensity, if adding sentiment classification, then it is possible to score Fan Avidity. Previous work attempts to engineer new key performance indicators or apply machine learning techniques for identifying the best existing indicators, however, there is limited research on sentiment analysis. In order to achieve the Master's Degree in Computer Science, this thesis aims to strengthen a machine learning model that applies polarity and sentiment analysis on tweets, as well as discovering factors thought to be relevant on a soccer match. The final goal is to achieve a flexible mechanism which automatizes the process of gathering data before a match, with the main objective of quantifying credit on fans' sentiment along with historical factors, while evaluating soccer prediction. The left alone sentiments' model could accomplish independence from the type of tournament, league or even sport.
- ANOSCAR: An image captioning model and dataset designed from OSCAR and the video dataset of activitynet(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-07-01) Byrd Suárez, Emmanuel; GONZALEZ MENDOZA, MIGUEL; 123361; González Mendoza, Miguel; puemcuervo; Ochoa Ruiz, Gilberto; Marín Hernandez, Antonio; School of Engineering and Sciences; Campus Estado de México; Chang Fernández, LeonardoActivity Recognition and Classification in video sequences is an area of research that has received attention recently. However, video processing is computationally expensive, and its advances have not been as extraordinary compared to those of Image Captioning. This work uses a computationally limited environment and learns an Image Captioning transformation of the ActivityNet-Captions Video Dataset that can be used for either Video Captioning or Video Storytelling. Different Data Augmentation techniques for Natural Language Processing are explored and applied to the generated dataset in an effort to increase its validation scores. Our proposal includes an Image Captioning dataset obtained from ActivityNet with its features generated by Bottom-Up attention and a model to predict its captions, generated with OSCAR. Our captioning scores are slightly better than those of S2VT, but with a much simpler pipeline, showing a starting point for future research using our approach, which can be used for either Video Captioning or Video Storytelling. Finally, we propose different lines of research to how this work can be further expanded and improved.
- TYolov5: A Temporal Yolov5 detector based on quasi-recurrent neural networks for real-time handgun detection in video(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12-01) Duran Vega, Mario Alberto; GONZALEZ MENDOZA, MIGUEL; 123361; González Mendoza, Miguel; puemcuervo; Ochoa Ruiz, Gilberto; Morales González Quevedo, Annette; Sánchez Castellanos, Héctor Manuel; School of Engineering and Science; Campus Monterrey; Chang Fernández, LeonardoTimely handgun detection is a crucial problem to improve public safety; nevertheless, the effectiveness of many surveillance systems, still depend of finite human attention. Much of the previous research on handgun detection is based on static image detectors, leaving aside valuable temporal information that could be used to improve object detection in videos. To improve the performance of surveillance systems, a real-time temporal handgun detection system should be built. Using Temporal Yolov5, an architecture based in Quasi-Recurrent Neural Networks, temporal information is extracted from video to improve the results of the handgun detection. Moreover, two publicity available datasets are proposed, labeled with hands, guns, and phones. One containing 2199 static images to train static detectors, and another with 5960 frames of videos to train temporal modules. Additionally, we explore two temporal data augmentation techniques based in Mosaic and Mixup. The resulting systems are three real-time architectures: one focused in reducing inference with a mAP(50:95) of 56.1, another in having a good balance between inference and accuracy with a mAP(50:95) of 59.4, and a last one specialized in accuracy with a mAP(50:95) of 60.6. Temporal Yolov5 achieves real-time detection and take advantage of temporal features contained in videos to perform better than Yolov5 in our temporal dataset. Making TYolov5 suitable for real-world applications.
- "Implementación de mejores prácticas de administración de servicios de informática mediante la implantación de ISO 9001:2000 en Banjercito S.N.C."-Edición Única(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2007-10-01) Lozano García, Mario Alberto; González Mendoza, Miguel; Rodríguez Abitia, Guillermo; Trejo Ramírez, Raúl Antonio; Lucio Nieto, Teresa; ITESM-Campus Estado de México