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.
- Modeling the relationship between the gut microbiome and progressive neurodegenerative diseases: case study Alzheimer’s disease(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-05-26) Trejo Castro, Alejandro Ismael; RANGEL ESCAREÑO, CLAUDIA; 200229; Rangel Escareño, Claudia; tolmquevedo; Alanis Funes, Gerardo Javier; Chávez Santoscoy, Rocío Alejandra; Fernández Figueroa, Edith Araceli; School of Engineering and Sciences; Campus MonterreyAlzheimer’s Disease (AD) has been known since 1906 and many of the symptoms and signs from the first case continue in the conceptualization of AD, such as memory loss, visuospatial disorders, impaired verbal communication, delirium, impotence and personality changes, such as depression and irritability, is the most common cause of dementia and neurodegenerative disease. It is expected to see an increment of up to 225% in the number of patients during a 40-year time frame (2010 - 2050). Clinically, the hallmark pathology of AD is the accumulation of amyloid-β (Aβ) protein fragments outside the neurons and accumulation of abnormal tau tangles within neurons. However, the microbiome composition is unique to a patient and, current studies have also proven the existence of a correlation with the microbiota that results in inflammation patterns and the accumulation of proteins related to AD. Nevertheless, no study so far has presented a model representing the interaction between the microbiota and the current tests to diagnose AD. In this study for the master’s program in Computer Science, we will approach a novel characterization of AD integrating clinical data, gut microbial metabolites and serum lipids metabolites. From a systems biology perspective, we intend to explain these covariates through machine learning and feature selection algorithms that would serve to find biomarkers between those who advance to the disease and those who does not. Data has been collected from various sources, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the Alzheimer’s Disease Metabolomics Consortium (ADMC). Our findings suggest that the combination of gut microbial metabolites with the well-known neuropsychological tests could enhance the diagnosis and prediction of AD. This research project invite the researcher to carry out more experiments about the microbiome since we realized is becoming the key to better comprehend AD and probably other neurodegenerative diseases.
- 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.
- 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
- Vision system for quality inspection of automotive parts based on non-defective samples(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-11) Vázquez Nava, Alberto; Ahuett Garza, Horacio; puelquio; Orta Castañón, Pedro Antonio; Urbina Coronado, Pedro Daniel; School of Engineering and Sciences; Campus MonterreyNowadays, companies in the automotive industry focus on delivering high-quality products to their customers, however, this task tends to be more complex as new car models emerge because new quality requirements must be learned. Currently in some companies, vision systems are used for the part quality inspection process, however, their learning process requires many correct and defective data to generate better predictions. Although it is possible to learn from correct samples, it is difficult to learn from defective parts because they are difficult to find in a company with strict quality standards. In this work, the implementation of machine learning classifier algorithms is proposed to detect correct and defective samples of different part types from the learning of only samples that meet quality standards. The feature extraction from images corresponding to suspension control arms and engine front covers was carried out, then a data augmentation process was applied to be analyzed by classifying algorithms in two stages: Part Identification and Geometric Quality Inspection. As a result, it was obtained that the Support Vector Machine classifier was the best algorithm in both stages, resulting in 100.0% accuracy in identifying the parts, 96.0% accuracy in detecting defective suspension control arms and 100.0% accuracy in finding defective front cover arms.
- Estudio exploratorio para determinar el estilo de manejo y su efecto en el consumo de energía en vehículos eléctricos, aplicando algoritmos de inteligencia artificial(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-05-04) Rojas Ruiz, Carlos Arturo; Santana Díaz, Alfredo; emipsanchez; Balbuena Campuzano, Carlos Alberto; Lopez Damian, Efraín; Escuela de Ingeniería y Ciencias; Campus TolucaEste documento presenta la síntesis de una investigación sobre el estilo de manejo y cómo este influye en la autonomía de vehículos eléctricos, mostrando por medio de gráficos la relación existente entre el tipo de conducción y el consumo de energía. La primera parte consta de la definición de conceptos relacionados con el estilo de manejo y con algoritmos de inteligencia artificial, al igual que se presenta información acerca de los vehículos eléctricos y de la dinámica de estos. Posteriormente se presentan las generalidades que se tuvieron en cuenta para la ejecución de las pruebas con las que se entrenarían los algoritmos de inteligencia artificial, información acerca del vehículo que se usó, las características del circuito y la configuración de los participantes y las consignas de manejo usadas, conducción suave, normal y deportiva. Luego de recopilar la información de las pruebas, estas se procesan por medio de la librería Pandas del lenguaje Python, extrayendo valores estadísticos que caracterizan cada una de las corridas y se compilan en una tabla para el entrenamiento de los algoritmos de inteligencia artificial. Tras obtener la tabla que contiene los parámetros que definen a cada prueba, se entrenan los algoritmos con el 75% de estas pruebas, el 25% restante se empleó para la evaluación, comparando lo que se identifica con la etiqueta original, ya sea conducción suave, normal o deportiva. Son varios los algoritmos que se entrenan y se evalúan por medio de métricas como la exactitud en prueba o el tiempo de cómputo. La serie de métricas de evaluación permiten seleccionar el algoritmo con el mejor desempeño para proponerlo como método para la identificación de futuras pruebas. El algoritmo propuesto es la máquina de vectores de soporte con función polinómica de grado 2, que logra un desempeño del 100% de exactitud en prueba y 96,2% en datos de entrenamiento, presentando también el menor tiempo de cómputo de todos los algoritmos. Luego de seleccionar el algoritmo definitivo, se realiza un análisis estadístico de la relación entre el consumo de energía por kilómetro y el nivel o tipo de conductor, de tal forma que se genera un factor por cada nivel de manejo, para corregir el valor generado por la aplicación “Estimador energético y generador de ciclos WLTC para itinerarios considerando la información cartográfica disponible en la red” teniendo en cuenta la influencia del conductor. Finalmente se comprueba la relación entre el estilo de manejo y el consumo de energía en el vehículo, esto se logra al comparar las pruebas reales con los valores calculados teóricamente por la aplicación y se confirma con 120 pruebas teóricas, que tienen en cuenta el factor del conductor en el cálculo energético.
- Virtual and auditory reality to characterize emotion regulation based on psycho-physiological pattern recognition(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021) Ramírez Lechuga, Sharon Elizabeth Esther; ALONSO VALERDI, LUZ MARIA; 167578; Alonso Valerdi, Luz María; emipsanchez; González Mendoza, Miguel; Vilchis Zapata, Carlos Leonel; Mercado García, Victor Rodrigo; School of Engineering and Sciences; Campus Estado de México; Ibarra Zarate, David IsaacEmotions play an essential role in everyday life, as they are involved in every event that a human being may experience. The literature review shows that a poor ability to manage emotions could be a critical factor in physical, mental, cognitive, and affective illnesses. Therefore, emotion regulation (ER) is crucial in human development, as it refers to the modulation and adjustment of one’s emotions. This thesis aimed to characterize ER strategies, cognitive reappraisal (CR), and expressive suppression (ES) from psychophysiological (physiological and psychometric) responses by eliciting high arousal emotions with different valence (anger and happiness) using two different virtual environments with the speech and interaction of a digital human. Demographic and psychometric information was acquired from the participants, the latter through two questionnaires, one focused on identifying the use of ER strategies (Emotion Regulation Questionnaire) and another to measure an emotional response (Self-Assessment Manikin), additional information was collected related to the participant’s perception of the digital human. Participants’ electrophysiological signals were recorded and subsequently processed to extract the frequency domain characteristics: power spectral density and power spectral entropy. The results show that Beta (30 Hz) and gamma (45-50 Hz) frequencies were positively associated with significant changes in emotion recognition. It can be inferred that in the ES strategy, the alpha wave has a higher increase in emotion with positive valence compared to a negative valence emotion. The findings of this study suggest that the CR strategy presents a greater use of cognitive resources. Furthermore, in situations where the induction of positive emotion is probable, the frontal, occipital, and parieto-occipital regions show greater activation if the CR strategy is used, in the case of the ES strategy, the left hemisphere of the brain has greater activation, mainly in the parieto-occipital, occipital and central regions. On the contrary, in a situation with possible induction of negative emotion, using the CR strategy, participants seem to have higher activation in central brain regions, while when using the ES strategy, the occipital, parieto-occipital and central regions with a slight tendency towards the right hemisphere show the highest activation. The results are consistent with the valence hypothesis. Finally, different machine-learning models were used to classify and identify the two ER strategies. These models were k-nearest neighbors, decision trees, random forest, extra tree classifier, neural networks, AdaBoost classifier, logistic regression, and support vector machine with linear kernel. The models that performed the best were: KNN (accuracy = 0.88 ± 0.01) and the AdaBoost classifier (accuracy = 0.88 ± 0.02). The evaluation metrics results showed that the nonlinear models obtained the best results in the classification of the ER strategy, regardless of the target emotion of the VE. This thesis has provided a deeper insight into the characterization of the main ER strategies through the psychometric and physiological patterns, as well as to identify the differences caused by the contrast of the valence of emotions. The physiological findings of this research show the complexity of human beings in coping with and reacting to negative emotions. This would be a fruitful area for future work on the neural networks involved in emotion recognition and regulation to provide better-tailored solutions to different problems related to emotions and the development of emotional intelligence in the future.
