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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- Design and validation of interaction systems in autonomous vehicles(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Mandujano Granillo, Jesús Arturo; Lozoya Santos, Jorge de Jesús; emipsanchez; Félix Herrán, Luis Carlos; School of Engineering and Sciences; Campus Monterrey; Tudón Martínez, Juan CarlosAs autonornous vehicle trust through Hurnan-M portant area of researc how the vehicle perceiv driver's behavioral state control. To address th rnentary systerns that i visualization interface feature-based rnonitorinThe research follo lection, and experirnent e6 Neighborhood Elect sensors, GNSS localiza ating Systern (ROS) and alizing vehicle state, en The DMS project lever L5l 5 carnera cornbined such as Eye Aspect Rati tern integrates a calibra and Randorn Forest (RF The HMI systern scenarios, including autonornous path following, gear and speed rnonitoring, and obstacle de tection. The interface successfully cornmunicated critica} information such as driving mode, environrnental object detection, localization, and systern actuation feedback. For the bio rnetric systern, the classification rnodels achieved high accuracy using SVM when tested on unseen data. The validation process confirrned the ability to detect a range of driver states, including eye closure, head distraction, and yawning, by processing extracted features and derived behavioral rnetrics such as blink rate. The outcornes of this thesis highlight the irnportance of designing AV interfaces that cornmunicate its situational awareness, while being capable of acquiring user behavioral in forrnation. The findings support future integration of such systerns into shared rnobility con texts, with potential extensions including user feedback rnodalities, real-time driver alerting mechanisrns, and multi-user calibration capabilities for broader deployment.
- Face detection and feature extraction for classification tasks on thermal images of Covid-19 patients(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Ramírez Treviño, Luis Javier; Tamez Peña, José Gerardo; emimmayorquin; Santos Díaz, Alejandro; Martínez Ledesma, Juan Emmanuel; School of Engineering and Sciences; Campus MonterreyThis thesis presents a methodology for diagnosing COVID-19 patients using computer vision, infrared thermography, and machine learning. The study focuses on the analysis of thermal images, which offer a non-invasive and contactless alternative to traditional imaging methods like computed tomography (CT) and radiography. The research leverages a database of thermal images from 252 patients, including both COVID-19 positive and negative cases, to explore the potential of infrared thermography in detecting respiratory diseases. The proposed methodology involves two main approaches: one using a Convolutional Neural Network (CNN) to extract features from the full thermal image, and another incorporating a face detection step to focus on facial features. Three face detection algorithms—Haar Cascades, Local Binary Patterns (LBP), and CNNs (specifically YOLOv5)—were evaluated, with achieved accuracies of 93%, 98%, and 100%, respectively. Feature extraction was performed using the VGG-16 CNN architecture, pre-trained on the ImageNet dataset, followed by classification using traditional machine learning models such as Logistic Regression, AdaBoost, Support Vector Machines (SVM), Random Forest, and Gradient Boosting. The methodology was tested on two classification tasks: gender classification and COVID-19 symptom classification. For gender classification, the full-body approach achieved accuracies ranging from 0.933 to 0.996, while the face-only approach yielded slightly lower accuracies (0.868 to 0.923). For symptom classification, the full-body approach achieved accuracies between 0.607 and 0.650, outperforming previous work using radiomic features on the same dataset. The face-only approach for symptom classification resulted in accuracies ranging from 0.544 to 0.612, still demonstrating improvement over prior results. The study concludes that the proposed methodology is effective for classification tasks on thermal images, particularly for gender classification. While the results for symptom classification are not yet reliable enough for standalone diagnostic use, the high sensitivity scores suggest potential as a screening tool. The research highlights the promise of infrared thermography combined with machine learning for medical applications, especially in scenarios where traditional imaging methods are impractical or pose risks due to radiation exposure. Future work could explore data augmentation, additional patient data, and applications in other medical domains.
- Detecting empathy on textual communication(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11) Montiel Vázquez, Edwin Carlos; RAMIREZ URESTI, JORGE ADOLFO; 21998; Ramírez Uresti, Jorge Adolfo; emijzarate/puemcuervo; Monroy Borja, Raúl; González Mendoza, Miguel; Montes y Gómez, Manuel; School of Engineering and Sciences; Campus Estado de México; Loyola González, OctavioEmpathy is a necessary component of human communication. The ability to understand and relate to others provides depth to any conversation between people, and is the basis for any exchange that deals with highly emotional topics. Current technological developments have raised interest in human-like behavior from computer systems regarding communication. This has led to the development of the area known as Affective computing, which is based on the study and processing of concepts related to emotions through artificial intelligence. However, in this area, empathy has been largely ignored in favor of other concepts such as emotion and feeling. This can be attributed to the complexity inherent of the concept. Nevertheless, there are now several methods that can be used to finally study and take advantage of empathy in computer applications. We provide a comprehensive study on the nature of empathy and a method for detecting it in textual communication. Thanks to this research, we present a database of conversations with their respective measurement of empathy. This metric, the Empathy score, is the first method for measuring empathy on texts based on psychological research. In order to detect the value of empathy on conversations, we apply machine learning classification. A pattern-based classification approach was taken in order to predict the Empathy score of utterances in our database, which allowed us to explore the advantages presented by these algorithms in psychologically-adjacent computing research. We were able to use methods found in computer science for the study and detection of empathy, and prove the viability of contrast pattern-based classification for measuring empathy levels on textual conversations.