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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- The role of capitalization and character repetition in identifying depression on social Media: a bilingual approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-24) Burgueño Paz, Luis Humberto; Zareei, Mahdi; emipsanchez; Roshan Biswal, Rajesh; School of Engineering and Sciences; Campus Monterrey; García Ceja, Enrique AlejandroDepression is a mental disorder that affects millions of people worldwide, but a significant portion of the affected people don’t receive adequate treatment. There has been an increasing interest from researchers to detect this condition through social media posts in order to prompt for early treatment. However, most of the research has been focused on the Caucasian Western English-speaking population, limiting the applicability of their findings across diverse cultural contexts. While research has shown the use of nonverbal cues to convey sentiment, their role on depression detection remains under-explored. This thesis aims to assess the effect of nonverbal cues, specifically capitalization and character repetition, on depression detection using datasets both in English and Spanish. This effect was explored through three existing datasets. The first dataset included a collection of Reddit posts and comments in the English language and was selected to assess the effect on a dataset coming from one of the most reputable mental health competitions in Natural Language Processing. The second dataset consisted of a collection of Spanish- language messages from Telegram to verify whether findings in the English language would hold for Spanish. The third dataset, also built from Reddit posts, was used to analyze the impact of these features when classifying by depression severity levels rather than binary labels. Four classifiers were used throughout this research: Logistic Regression, Random Forest, Support Vector Machine, and Neural Network. Overall, the impact of capitalization and character repetition for depression detection was found to be minimal. These features had the most effect on English Reddit data with binary labels, while showing limited impact on Spanish data or when classifying by severity levels. Additionally, models using only character repetition outperformed those relying on capitalization features.
- Detection of epileptic seizures through brain waves analysis using Machine Learning algorithms.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-17) Alvarado Elizalde, Cristian Yair; MARTINEZ LEDESMA, JUAN EMMANUEL; 200096; Martínez Ledesma, Juan Emmanuel; puemcuervo; Cuevas Díaz Durán, Raquel; Santos Díaz, Alejandro; Martínez Torteya, Antonio; School of Engineering and Sciences; Campus Estado de MéxicoElectroencephalogram(EEG) is an effective and non-invasive technique commonly used for monitoring brain activity. EEG readings are analyzed to determine changes in brain activity that may be useful for diagnosing neurological disorders and other seizure disorders. On the other hand, around 50 million people worldwide have epilepsy, making it one of the most common neurological diseases globally. The risk of premature death in people with epilepsy is up to three times higher than in the general population. Over the years, different researchers had been trying to detect seizures with different methods and with different approaches, but none algorithm has been fully implemented in the life of the people that have this disease, and for this reason, I developed a solution for this problem. The solution that I developed was to extract the information obtained by making a classification analysis using data acquired through the EEGs in a time-lapse of 1 second and once done, compare the results of the Machine Learning methods to find the best algorithms for solving the problem. The main objective of the algorithm is to find the most precise detection during epileptic seizures using public data, by extracting the temporal features from the electroencephalogram and with this learn the general structure of a seizure to make an effective detection in the less time possible.
