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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- A robust and interpretable machine learning framework for vanadium oxide supercapacitors(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06-13) Ortiz Aldana, Emmanuel Alexei; Kumar, Rudra; emimmayorquin; Mallar, Ray; Sánchez Ante, Gildardo; Kumar, Kishant; School of Engineering and Sciences; Campus Monterrey; Ebrahimibagha, DariushAs global energy demands intensify, the development of efficient, scalable and reliable energy storage systems becomes increasingly urgent. While lithium-ion batteries dominate the current market, their low power density makes them unsuitable for current fluctuations degrading their life expectancy. Supercapacitors (SCs) with pseudocapacitance materials such as vanadium oxide offer an attractive option, with high power density, long life cycle and fast charge-discharge rate. However, their low energy density remains a major bottleneck limiting broader adoption. Current supercapacitor research is focused on improving the specific capacitance and thus expanding their energy density, nevertheless this is mostly done on traditional trial and error experiments, making it time-consuming, slow and expensive. Materials Informatics offers a paradigm shift by implementing machine learning (ML) techniques to uncover patterns in existing data and accelerate the design of novel materials. Despite promising results, many current materials ML studies suffer from limitations such as small data range, improper data preprocessing, target leakage, and lack of reproducibility due to unshared code and datasets. In this work a robust machine learning framework was developed for vanadium oxide SCs, designed to extract interpretable insights from manually gathered literature data. A rigorous cross-validation (CV) pipeline was implemented to ensure reliable model evaluation, avoiding common pitfalls such as overfitting and data leakage. Among the evaluated models, a Voting Regressor combining Ridge Regression, Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) achieved the best performance with a mean absolute error (MAE), root mean squared error (RMSE), and 𝑅2 of 81 𝐹 𝑔 ⁄ , 104 𝐹 𝑔 ⁄ and 0.61, respectively. To extract insights from the models, interpretability algorithms, including permutation importance (PI) and SHapley Additive exPlanations (SHAP) values were employed. Binder-free electrodes, wider potential windows, and a low current density are consistently associated with higher specific capacitance predictions. These findings highlight the potential of interpretable methods to uncover the ML models behavior and lead guided design of SCs.
- Deep learning causal study between the gut microbiome composition and autism spectrum disorder manifestation(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Oláguez González, Juan Manuel; Alfaro Ponce, Mariel; emimmayorquin; Sosa Hernández, Víctor Adrián; Breton Deval, Luz de María; Schaeffer, Satu Elisa; School of Engineering and Sciences; Campus Monterrey; Chairez Oria, Jorge IsaacAutism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental conditions characterized by early impairments in communication and social interaction, often accompanied by repetitive behaviors. Although its etiology remains unclear, both genetic and environmental factors—including gastrointestinal disturbances—have been implicated. Recent research has highlighted a potential link between ASD and alterations in gut microbiota composition (GMC), with some studies reporting microbial imbalances associated with symptom severity. However, inconsistent methodologies, non-reproducible results, and demographic biases hinder the generalizability of current findings. This thesis investigates the use of machine learning (ML) techniques to model and explore the relationship between gut microbial profiles and ASD. ML offers powerful tools for analyzing complex, nonlinear data across heterogeneous populations, addressing methodological inconsistencies and uncovering patterns that traditional statistical approaches may miss. The objectives of this work are to: (1) identify key microbial predictors of ASD across diverse cohorts, (2) quantify the relative importance of specific bacterial taxa, and (3) simulate simplified microbiota dynamics relevant to ASD. The research was carried out in three stages. First, classical ML algorithms were applied to uncover hidden relationships between microbial profiles and ASD diagnosis, revealing how different bacterial genera may contribute to ASD manifestation in cohorts with diverse GMCs. Second, in silico simulations were performed to visualize the impact of diet on gut microbiota structure and to observe clustering behaviors among bacteria under different dietary regimes. Finally, a semi-supervised model was developed using synthetic data and engineered features, grouping bacteria according to their primary metabolic functions and incorporating these functional categories as novel predictors. In conclusion, focusing on bacterial metabolic functions rather than isolated taxa provides a more robust and interpretable framework for understanding the GMC-ASD relationship, potentially supporting earlier diagnosis and improved insights into the environmental dimensions of ASD.
- Data-driven modeling and bayesian optimization of cooling towers for the reduction of water consumption(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025) Alatorre Cuéllar, Karla Valeria; Montesinos Castellanos, Alejandro; emipsanchez; Hernández Romero, Ilse María; López Guajardo, Enrique Alfonso; School of Engineering and Sciences; Campus MonterreyThis study presents a data-driven framework that integrates Machine Learning and Bayesian Optimization to minimize water consumption in industrial cooling towers while preserving cooling efficiency. Using historical operational and environmental data from a power generation facility, several regression models (Linear Regression, Random Forest, XGBoost, and Neural Networks) were developed to predict makeup water flow. Random Forest and XGBoost achieved the highest accuracy, with R2 scores of 0.982 and 0.972, respectively. Bayesian Optimization was employed to efficiently tune hyperparameters, yielding substantial improvements in predictive performance such as reducing RMSE by up to 18.6%. The methodology also incorporated feature importance analysis, which identified critical operational drivers such as blowdown flow and inlet water temperature. Overall, Random Forest was preferred due to its superior predictive accuracy, ease of interpretation, and practical integration into operational dashboards. By combining predictive modeling, optimization, and interpretability, the study offers a powerful methodology for a data-driven tool to support decision-making and identify opportunities for minimizing makeup water use in cooling tower operation.
- Adaptive learning for providing inclusive contents based on student profile in digital education(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Alvarado Reyes, Ignacio; Molina Espinosa, José Martín; emipsanchez; Icaza Longoria, Inés Alvarez; Suárez Brito, Paloma; School of Engineering and Sciences; Campus Estado de MéxicoThe application of artificial intelligence technologies in educational fields has been increasing in the last years, especially with the implementation of adaptive learning technologies, designed to monitor different characteristics of students and provide them with content and suggestions aimed at improving their performance and avoiding problems they may have on digital platforms. In this study, the reference framework for student classification was explored with a proposal of the contents and accessibility functions that could be applied based on their learning characteristics, complemented by an implementation of adaptive learning technologies consisting of a classifier based on the decision tree algorithm that automatically processes student data and classify them within the classes defined in the framework. For the implementation of the classifier, it was trained with two data sets, initially with data generated in the laboratory and later with experimental data, obtained through a survey aimed at higher education students. Both instances of the trained algorithm demonstrated high accuracy for the classification process (99.98% with synthetic data and 95.94% with experimental data). Subsequently, through the same survey, the suggestions related to the classes assigned to the students were validated, as well as the suggested accessibility features and content. The suggestions seem to have a favorable acceptance range with rejection percentages between 0% and 6% for the content selections and between 14% and 34% for the accessibility options. With this dynamic implementation of educational content and digital accessibility features, we seek to provide personalized learning for different student profiles while seeking to implement more features related to compliance with concerns about diversity and inclusion.
- Tool Condition Monitoring System for Competitive Aluminum Milling(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Navarro Macías, Horacio Armando; Morales Menendez, Ruben; emimmayorquin; Guedea Elizalde, Federico; School of Engineering and Sciences; Campus Monterrey; Vallejo Guevara, Antonio Jr.In recent years, the auto parts industry has experienced a significant transformation, transitioning from gasoline-powered vehicles to electric vehicles, influenced by the Connected, Autonomous, Shared, and Electric (CASE) technologies trends. This shift is increasing the demand for advanced components like sensors and ECUs, requiring enhanced manufacturing techniques such as die casting and machining. However, North American manufacturers face a risk in competitiveness due to must of this mechanical parts are supplied by Asian suppliers, posing risks to increase manufacturing cost related to tariffs and logistics. To stay competitive and embrace these trends, North America needs to establish a CASE manufacturing hub to localize production. Denso is a Japanese mobility supplier that has provided advanced automobile technologies, components, and systems to major manufacturers since 1949, operating in 38 countries Denso (1 10). Established in 1996, Denso México (DNMX) has grown significantly, with four plants—two in northern Mexico, one in Silao, and a recent addition in Irapuato. As of March 2023, DNMX employs over 7,000 people, making it one of the largest facilities within Denso North America and playing a key role in the North American market for CASE products (Connected, Autonomous, Shared, and Electric vehicles). To improve competitiveness in the auto-parts and support the localization of parts the strategy of DNMX is to focus on enhancing the Monozukuri spirit1. The approach involves establishing a manufacturing foundation thru integration of advance industry 4.0 strategies, including IoT, automation, and data analytics, to optimize processes and improve efficiency and quality. In the context of CASE, the emphasis is on producing essential components like aluminum-machined cases for electric parts and inverter motors. To gain a competitive advantage, there is a significant investment in advanced technologies for machining processes, aiming to ensure cost efficiency, enhance productivity, maintain quality, and extend tool life. The real-time autonomous Tool Condition Monitoring System (TCMS) is a key element of this strategy, enhanced by Artificial Intelligence (AI), which leverages machine learning to analyze real-time data, predict tool wear, and prevent potential failures. The development and deployment of the AI-driven TCMS follow the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, a robust framework widely adopted for data analytics projects. CRISP-DM ensures a structured approach through six phases: business understanding, where goals and objectives align with organizational strategy; data understanding, involving detailed exploration of machining and tool condition data; data preparation, including cleaning and structuring data for analysis; modeling, where machine learning algo-rithms predict tool wear and failure; evaluation, assessing model accuracy and alignment with objectives; and deployment, integrating the AI system into manufacturing processes. This methodology enhances the iterative refinement of predictive capabilities, ensuring alignment with strategic objectives and operational realities. By adopting CRISP-DM, DNMX ensures the systematic development of its AI-integrated TCMS, enhancing machining accuracy and reliability, optimizing maintenance schedules, and reducing downtime. This structured approach continuously improves the system, reinforcing DNMX’s leadership in the North American auto-parts industry and contributing to the transformation towards electric vehicles.
- Enhancing BGP security with MAD anomaly detection system and machine learning techniques(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Romo Chavero, María Andrea; Cantoral Ceballos, José Antonio; emipsanchez; Botero Vega, Juan Felipe; Navarro Barrón, Francisco Javier; School of Engineering and Sciences; Campus Monterrey; Pérez Díaz, Jesús ArturoAnomalies in the Border Gateway Protocol (BGP) represent a signicant vulnerability in the Internet’s infrastructure, as they can cause widespread disruptions, trafc misdirection, and even security breaches. Proactive detection of these anomalies is vital to preserving network stability and preventing potential cyberattacks. In response to this challenge, we present the Median Absolute Deviation (MAD) anomaly detection system, which combines traditional statistical methods with advanced machine learning (ML) techniques for more precise and dynamic detection. Our approach introduces a novel adaptive threshold mechanism, allowing the system to adjust based on the changing conditions of network trafc. This dynamic thresholding signif- icantly improves the accuracy, precision, and F1-score of anomaly detection compared to the previous xed-threshold version. Additionally, we integrate the MAD system with a diverse ML classiers, including Random Forest, XGBoost, LightGBM, CatBoost, and ExtraTrees to enhance the system’s ability to identify complex patterns that indicate unusual BGP behavior.We evaluate our detection system on well-documented BGP anomaly events, such as the Slammer worm, Nimda, Code Red 1 v2, the Moscow blackout, and the Telekom Malaysia misconguration. The results show that our system when combined with ML models achieves an overall accuracy and F1-score of 0.99, demonstrating its effectiveness across various anomaly types. By using both statistical and ML models, the system is able to capture irregularities that could signal security threats, offering a more comprehensive detection solution.This research highlights the importance of combining statistical anomaly detection with ML to obtain a balance between accuracy and computational efciency. The system’s low resource requirements and minimal pre-processing make it highly scalable, allowing it to be potentially deployed in real-time on large-scale networks.
- Crowd-scouting: enhancing football talent identification through the use of machine learning and wisdom of crowds(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Díaz de León Rodríguez, Iván; Zareei, Mahdi; emimmayorquin; Roshan Biswal, Rajesh; School of Engineering and Sciences; Campus Estado de México; Hinojosa Cervantes, Salvador MiguelThe identification of talented young footballers is a cornerstone of success in professional football. This capability empowers established clubs to nurture potential superstars who elevate team performance and propel them towards championship contention. Smaller clubs strategically leverage this skill set to develop talent for an eventual sale, boosting their financial situation and, in some instances, even mounting their own title challenges. Ultimately, the ability to recognize future elite players has consistently translated into a significant competitive advantage throughout the history of the sport. This thesis delves into this domain by comparing the performance of three supervised machine learning models (Random Forest, Gradient Boosting, and Support Vector Machines). The models were trained using two comprehensive datasets encompassing data for 1,086 male professional footballers. The first one incorporates player statistics, game-related attributes, and transfer market values. The second one incorporates YouTube metrics to leverage the well-established concept of the wisdom of crowds. This concept presumes that the collective intelligence of a large group can outperform individual judgment. The wisdom of the fans has the potential to optimize scouting efforts. Historical and literary evidence suggests that the most effective strategies combine data with human judgment, particularly for complex tasks such as talent identification. SVM demonstrated the highest effectiveness, achieving superior sensitivity and identifying the greatest proportion of elite players within the dataset under the baseline scenario following a 5-fold cross-validation. Although its performance declined after the inclusion of crowd-sourced features, SVM continued to capture the largest portion of elite players, despite its lower precision score. The crowd-sourced features exhibited surprising potential when integrated with tree-based models, enhancing both sensitivity and precision in identifying the minority class. These models successfully captured a significantly larger share of the minority class while preserving their discriminative capacity. Integrating the collective knowledge of football fans improved the performance of a classification algorithm in identifying elite players using the selected features; thus, thereby validating the hypothesis stated in this dissertation. Furthermore, the feature importance analysis and other valuable insights gleaned from the study pave the way for further research endeavors. By providing this comparative analysis, the study aims to encourage the adoption of advanced data analytics, statistical methods, and more crowd-sourced data within football clubs worldwide. This approach can empower them to optimize resource allocation and refine their talent identification strategies.
- Multimodal data fusion algorithm for image classification(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Beder Sabag, Taleb; Vargas Rosales, César; emipsanchez; Pérez García, Benjamín de Jesús; School of Engineering and Sciences; Campus MonterreyIImage classification algorithms are a tool that can be implemented on a variety of research sectors, some of these researches need an extensive amount of data for the model to obtain appropriate results. A work around this problem is to implement a multimodal data fusion algorithm, a model that utilizes data from different acquisition frameworks to complement for the missing data. In this paper, we discuss about the generation of a CNN model for image classification using transfer learning from three types of architectures in order to compare their results and use the best model, we also implement a Spatial Pyramid Pooling layer to be able to use images with varying dimensions. The model is then tested on three uni-modal data-sets to analyze its performance and tune the hyperparameters of the model according to the results. Then we use the optimized architecture and hyperparameters to train a model on a multimodal data-set. The aim of this thesis is to generate a multimodal image classification model that can be used by researchers and people that need to analyze images for their own cause, avoiding the need to implement a model for a specific study.
- Machine translation for suicide detection: validating spanish datasetsusing machine and deep learning models(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Arenas Enciso, Francisco Ariel; Zareel, Mahdi; emipsanchez; García Ceja, Enrique Alejandro; Roshan Biswal, Rajesh; School of Engineering and Sciences; Sede EGADE MonterreySuicide is a complex health concern that affects not only individuals but society as a whole. The application of traditional strategies to prevent, assess, and treat this condition has proven inefficient in a modern world in which interactions are mainly made online. Thus, in recent years, multidisciplinary efforts have explored how computational techniques could be applied to automatically detect individuals who desire to end their lives on textual input. Such methodologies rely on two main technical approaches: text-based classification and deep learning. Further, these methods rely on datasets labeled with relevant information, often sourced from clinically-curated social media posts or healthcare records, and more recently, public social media data has proven especially valuable for this purpose. Nonetheless, research focused on the application of computational algorithms for detecting suicide or its ideation is still an emerging field of study. In particular, investigations on this topic have recently considered specific factors, like language or socio-cultural contexts, that affect the causality, rationality, and intentionality of an individual’s manifestation, to improve the assessment made on textual data. Consequently, problems like the lack of data in non-Anglo-Saxon contexts capable of exploiting computational techniques for detecting suicidal ideation are still a pending endeavor. Thus, this thesis addresses the limited availability of suicide ideation datasets in non-Anglo-Saxon contexts, particularly for Spanish, despite its global significance as a widely spoken language. The research hypothesizes that Machine- Translated Spanish datasets can yield comparable results (within a ±5% performance range) to English datasets when training machine learning and deep learning models for suicide ideation detection. To test this, multiple machine translation models were evaluated, and the two most optimal models were selected to translate an English dataset of social media posts into Spanish. The English and translated Spanish datasets were then processed through a binary classification task using SVM, Logistic Regression, CNN, and LSTM models. Results demonstrated that the translated Spanish datasets achieved scores in performance metrics close to the original English set across all classifiers, with limited variations in accuracy, precision, recall, F1-score, ROC AUC, and MCC metrics remaining within the hypothesized ±5% range. For example, the SVM classifier on the translated Spanish sets achieved an accuracy of 90%, closely matching the 91% achieved on the original English set. These findings confirm that machine-translated datasets can serve as effective resources for training ML and DL models for suicide ideation detection in Spanish, thereby supporting the viability of extending suicide detection models to non-English-speaking populations. This contribution provides a methodological foundation for expanding suicide prevention tools to diverse linguistic and cultural contexts, potentially benefiting health organizations and academic institutions interested in psychological computation.
- Anomaly detection in a cost-effective conveyor belt testing system(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-09) Solórzano Souza, Ana Paula; Navarro Durán, David; mtyahinojosa, emipsanchez; Galluzzi Aguilera, Renato; Sotelo Molina, Carlos Gustavo; Navarro Durán, David; School of Engineering and Sciences; Campus Ciudad de MéxicoThe industrial manufacturing sector faces significant challenges regarding energy efficiency and operational sustainability, particularly in the management of motor driven systems such as conveyor belts. While traditional diagnostic methods effectively identify faults, a critical gap remains in closing the control loop to enable autonomous corrective actions, specifically for mechanical tension regulation y conveyor belts. This research addresses this limitation by designing, implementing, and validating a low-cost, small-scale prototype capable of soft real-time energy monitoring, anomaly detection, and automatic physical correction. The system utilizes an ESP32 microcontroller and an INA219 sensor to analyze voltage and current signals, employing unsupervised machine learning algorithms such as Isolation Forest (IF) and One-Class Support Vector Machine (OC-SVM). This serves to diagnose tension states defined by specific deflection thresholds. To execute the physical corrections, the platform integrates a custom built rack and pinion mechanism driven by a servomotor, which automatically regulates the belt tension. Methodologically, the study characterized optimal operational conditions at a 30% PWM duty cycle and applied a median filter that reduced signal variability. Four models were trained using exclusively optimal-state data under univariate and multivariate configurations. Experimental results demonstrate that electrical parameters serve as reliable indicators of mechanical tension in a small scale conveyor belt. The Univariate One-Class SVM model applied to voltage yielded the highest performance, achieving an F1-Score of 0.8624. Meanwhile, the Multivariate OC-SVM model demonstrated high reliability with a Precision of 90.51% and a Recall of 72.95%. Upon detection of anomalies (slippage or excessive tightness), the system successfully triggered autonomous adjustments via a rack and pinion mechanism. These findings validate the feasibility of using accessible monitoring to implement intelligent, self-correcting maintenance systems, offering a scalable solution to minimize downtime and energy waste in industrial environments.