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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- Environmental assessment of urban rivers through a dual lens approach: machine learning based water quality analysis and metagenomic characterization of contamination effects(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Fernández del Castillo Barrón, Alberto; Gradilla Hernández, Misael Sebastián; emipsanchez; García González, Alejandro; Pacheco Moscoa, Adriana; Brown, Lee; Oscar Alejandro Aguilar Jiménez; School of Engineering and Sciences; Campus Monterrey; Senés Guerrero, CarolinaUrban rivers are critical ecosystems increasingly threatened by pollution. Effective water quality monitoring and contamination assessment are essential for informed management decisions. The Santiago River, a key hydrologic system in Mexico, has become one of the country’s most polluted rivers, posing significant ecological risks and public health concerns for nearby communities. This study underscores the urgent need for comprehensive environmental evaluation and enhanced monitoring approaches. Chapter one introduces the motivation behind monitoring water quality in highly polluted rivers, presenting the problem statement and contextual background of the Santiago River basin. It outlines the research question and provides an overview of the proposed dual-lens approach: combining water quality analysis via machine learning algorithms with metagenomic characterization of contamination effects. Key contributions of this work to the field are also highlighted. Chapter two reviews global monitoring strategies from highly polluted rivers, focusing on nine rivers across developed and developing countries to offer a comparative perspective on water quality management needs. In Chapter three, regression and classification machine learning models are developed to predict the Santiago River Water Quality Index (SR-WQI), designed as complementary tools to strengthen the current monitoring program. Chapter four analyzes the historical water quality patterns of the Santiago River to identify the most variable and representative data for training machine learning models. This chapter also reveals that redundant data can hinder model performance by leading to overfitting. Chapter five investigates spatial variations in the microbial composition of Santiago River sediments and examines correlations with water quality. Using high-throughput sequencing, potential microbial biomarkers were identified and impacts of physicochemical parameters and heavy metals on microbial communities were assessed. Finally, chapter five highlight the main findings of this thesis and covers some limitations, perspectives for future research and final remarks.
- Explainable AI for trading 50 consumer discretionary stocks in the S&P 500(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Sanromán Iñiguez, Paulina Monserrat; Mendoza Montoya Omar; emipsanchez; Antelis Ortiz, Javier Mauricio; Guizar Mateos, Isaí; School of Engineering and Sciences; Sede EGADE Monterrey; Bernal Ponce, Luis ArturoThis document presents a study that merges computer science techniques with finance, focusing on the development of an Explainable Supervised Machine Learning (SML) model aimed at achieving a balance between predictive accuracy and interpretability in price forecasting for Algorithmic Trading (AT). Utilizing SHAP (SHapley Additive exPlanations), both global explanations are provided to facilitate feature selection and determine the importance of various macroeconomic and technical indicators derived from historical data of 50 companies within the Consumer Discretionary sector of the S&P 500 Index. The study also employs hyperparameter tuning on lagged values to assess whether the price movements from one day can effectively predict subsequent market prices. Algorithmic Trading (AT) currently constitutes approximately 60% to 75% of total trading activity in U.S. equity markets, European financial markets, and major Asian capital markets (Groette, 2024). Projections indicate a significant growth trajectory for this sector. The driving force behind this expansion is the advancement of Artificial Intelligence (AI). As AI models incorporate more data, they tend to become increasingly intricate and opaque, evolving into what are commonly referred to as black box models. This complexity raises critical concerns surrounding explainability, interpretability, and transparency, as well as adherence to regulatory standards. Neglecting these issues can lead to severe market disruptions, including panic selling, liquidity evaporation, increased asset correlations, and a lack of clarity regarding the decision-making processes of AI models. Such challenges underscore the imperative for developing transparent and interpretable AI solutions in AT to mitigate risks and enhance market stability.
- Modeling of carbon sequestration and productivity for maize and oats crops using artificial neural network(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-25) Aguilar Chavez, Fernanda; Valiente-Banuet, Juan Ignacio; emipsanchez; Clarke Crespo, Emilio; González Viejo, Claudia; School of Engineering and Sciences; Campus QuerétaroClimate change presents a critical challenge to global food security, especially as the global population continues to rise. A major driver of this phenomenon is the accumulation of greenhouse gases, particularly CO₂, which intensifies Earth's warming. Key contributors to elevated CO₂ levels include fossil fuel combustion and agricultural activities. However, agricultural systems have the potential to mitigate this effect by capturing atmospheric CO₂. Notably, few models account for the net CO₂ flux in agricultural systems, which is critical for understanding their true carbon sequestration potential. This study introduces a machine learning-based approach to model CO₂ sequestration and productivity in two forage crops, a variety of maize (Zea mays) and oats (Avena sativa), under diverse environmental conditions. The model leverages critical variables such as degree days, NDVI, and water balance. Using an artificial neural network (ANN), the study achieved robust predictive accuracy for both crops, with determination coefficients (R) of 0.95 for maize and 0.96 for oats, and low mean squared errors (MSE = 0.02). These results highlight the model’s high performance and reliability, offering a valuable tool for predicting carbon sequestration and productivity in forage crops while addressing a key gap in net CO₂ flux modeling.
- 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.
