Tesis

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Colección de Tesis y Trabajos de grado (informe final del proyecto de investigación, tesina, u otro trabajo académico diferente a Tesis, sujeto a la revisión y aceptación de una comisión dictaminadora) presentados por alumnos para obtener un grado académico del Tecnológico de Monterrey.

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Now showing 1 - 10 of 16
  • Tesis de maestría / master thesis
    Glucose measurement via noninvasive methods
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12-02) Huerta Ruiz, Samuel Natán; González Hernández, Hugo Gustavo; emipsanchez; Moreno Moreno, Jesús; School of Engineering and Sciences; Campus Ciudad de México; Oliart Ros, Alberto
    Noninvasive glucose measurement methods are wide-ranging; they use several different technologies to try and get accurate results. Some try to measure glucose through lacerations on the skin and chemicals, others try to do it analyzing the color of the sclera, and others try to do it analyzing the sweat. For this thesis, a completely noninvasive and chemical free approach is used. Glucose levels are classified into three useful categories (low, medium, and high) trough the use of machine learning and descriptors from chaos theory to obtain a a satisfactory Support Vector Machine (SVM) model. Several classification models are compared by the following metrics: Area Under the Receiver (AUC), accuracy, precision, recall, and their combined information (F1). And lastly, a multipurpose system that uses principles from Internet of Things (IoT) is implemented to integrate a sampling device powered by Arduino with a web app, which in turn uses cloud computing to process data and store it in a remote server to effectively train machine learning models written in Python.
  • Tesis de doctorado
    Real-time armed individual detection in video surveillance usingdeep learning and heuristic approaches
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Amado Garfias, Alonso Javier; Conant Pablos, Santiago Enrique; emipsanchez; Ortiz bayliss, José Carlos; Tarashima Marín Hugo; Gutiérrez Rodríguez, Andrés Eduardo; School of Engineering and Sciences; Campus Monterrey
    This researchaimstoenhancetheautomaticidentificationofarmedindividualsinvideo surveillanceinreal-time.Theproposedmethodologyinvolvesthedevelopmentofalgorithms specifically designedforthedetectionofindividualscarryinghandguns,whichincludepistols and revolvers.Toachievethis,theYOLOv4modelhasbeenselectedtodetectindividuals, handguns, andfaces.Subsequently,real-timeinformationisextractedfromtheYOLOmodel, including boundingboxcoordinates,distances,andintersectionareasbetweenhandgunsand individualswithineachvideoframe.Thisinformationfeedsourheuristicsanddifferentma- chine learning(ML)proposed,facilitatingtherecognitionofarmedindividuals.Severalchal- lenges mustbeaddressed,suchasocclusion,concealedguns,andproximityofindividualsto one another.Itencouragesthedevelopmentandcomparisonofdifferenttypesofsolutions. Theyaremadeupofthreeheuristics,seven-armedpeopledetectors(APD),and44APDto use ineachvideoframe(APD4F). The heuristicsaretheDeterministicMethodofCenters(DMC),theDeterministicMethod of Distances(DMD),andtheDeterministicMethodofIntersections(DMI).Furthermore, the APDmodelsareRandomForestClassifier(RFC-APD),MultilayerPerceptron(MLP- APD), k-Nearest-Neighbors(KNN-APD),SupportVectorMachine(SVM-APD),Logistic Regression(LR-APD),NaiveBayes(NB-APD),andGradientBoostingClassifier(GBC- APD). Thereby,IproposetocreateselectorsfordecidingwhichAPDtouseineachvideo frame (APD4F)toimprovethedetectionresults.Besides,weimplementedtwotypesof APD4Fs, onebasedonaRandomForestClassifier(RFC-APD4F)andanotherinaMultilayer Perceptron (MLP-APD4F).Wedeveloped44APD4FscombiningsubsetsofsixAPDs.The most ofAPD4FoutperformedoftheindependentuseofallAPDs.Amultilayerperceptron- based APD4F,whichcombinesanMLP-APD,aNB-APD,andaLR-APD,presentedthebest performance, achievinganaccuracyof95.84%,arecallof99.28%andanF1scoreof96.07%. This researchalsoproposesasolutiontooptimizetheproblemofdetectingarmedpeople when theweaponisnotvisible.Therefore,weapplyrecurrentneuralnetworks,suchasLong Short TermMemory(LSTM),topredictthecoordinatesoftheguns.Inthisway,itispossible to haveapredictionofarmedpeopleatalltimes.ThemeasurementbetweentheYOLO handgun detectionboundingboxesandtheLSTMpredictionresultedinanIoUof65.23%. When thefirearmdetectionbytheobjectdetectorisinterrupted,theweapon’spositionis generated bytheLSTMmodelsthat,togetherwiththeAPDs,identifythearmedpeople. When theLSTMsdeliveredtheirpredictionstotheAPDs,theNB-APDdemonstratedthe best performance,achievinganaccuracyof80.93%.TheLSTMsallowedtheanalysisof 5,288 recordsofthetestvideothatcouldnotbeanalyzedbeforeduetothelackofknowledge of thegun’sposition.
  • Tesis de maestría
    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 Arturo
    This 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.
  • Tesis doctorado / doctoral thesis
    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, Carolina
    Urban 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.
  • Tesis de maestría / master thesis
    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 Alejandro
    Depression 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.
  • Tesis de doctorado
    Security automation in software defined networks
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-01) Yungaicela Naula, Noé Marcelo; YUNGAICELA NAULA, NOE MARCELO; 781291; Vargas Rosales, César; puemcuervo, emipsanchez; Zareei, Mahdi; Ramírez Velarde, Raúl Valente; Rodríguez Cruz, José Ramón; School of Engineering and Sciences; Campus Monterrey; Pérez Díaz, Jesús Arturo
    The exponential increase of devices connected to the internet, and the conventional networking operation, based on distributed and static network management, have made networking an incredibly complex task. Software-Defined Networking (SDN) solves the problems arising from the static nature of conventional networking by introducing dynamism to the networking operation. SDN separates the data plane and control plane, centralizes the network control, and automates the network management. In particular, SDN technology is an effective solution to provide security to different network environments. This study solves the security problem in SDN-based networks using state-of-the-art artificial intelligent (AI) techniques. An automated security framework is proposed which integrates two components: 1) Reactive, and 2) Proactive parts. The reactive component uses Deep Learning (DL) to identify complex DDoS threats and Reinforcement Learning (RL) to mitigate them. The proactive component leverages Network Function Virtualization (NFV) to provide scalability to the proposed security framework. Extensive experiments using datasets, simulations, and physical deployments demonstrate the effectiveness of the proposed security automation framework.
  • Tesis de maestría
    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 Monterrey
    Alzheimer’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.
  • Tesis de maestría
    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 Isaac
    Emotions 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.
  • Tesis de maestría
    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 México
    Electroencephalogram(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.
  • Tesis de doctorado
    A novel functional tree for class imbalance problems
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-11) Cañete Sifuentes, Leonardo Mauricio; CAÑETE SIFUENTES, LEONARDO MAURICIO; 787723; Monroy Borja, Raúl; puemcuervo, emipsanchez; Morales Manzanares, Eduardo; Gutiérrez Rodríguez, Andrés Eduardo; Cantú Ortiz, Francisco; Conant Pablos, Santiago; School of Engineering and Sciences; Campus Estado de México; Medina Pérez, Miguel Angel
    Decision trees (DTs) are popular classifiers partly because they provide models that are easy to explain and because they show remarkable performance. To improve the classification performance of individual DTs, researchers have used linear combinations of features in inner nodes (Multivariate Decision Trees), leaf nodes (Model Trees), or both (Functional Trees). Our general objective is to develop a DT using linear feature combinations that outperforms the rest of such DTs in terms of classification performance as measured by the Area Under the ROC Curve (AUC), particularly in class imbalance problems, where one of the classes in the database has few objects compared to another class. We establish that, in terms of classification performance, there exists a hierarchy, where Functional Trees (FTs) surpass Model Trees, that in turn surpass Multivariate Decision Trees. Having shown that Gama's FT, the only FT to date, has the best classification performance, we identify limitations that hinder its classification performance. To improve the classification performance of FTs, we introduce the Functional Tree for class imbalance problems (FT4cip), which takes care in each design decision to improve AUC. The decision of what pruning method to use led us to the design of the AUC-optimizing Cost-Complexity pruning algorithm, a novel pruning algorithm that does not degrade classification performance in class imbalance problems because it optimizes AUC. We show how each design decision taken when building FT4cip contributes to classification performance or to simple tree models. We demonstrate through a set of tests that FT4cip outperforms Gama's FT and excels in class imbalance problems. All our results are supported by a thorough experimental comparison in 110 databases using Bayesian statistical tests.
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