Tesis
Permanent URI for this communityhttps://hdl.handle.net/11285/345119
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.
Para enviar tu trabajo académico al RITEC, puedes consultar este Infográfico con los pasos generales para que tu tesis sea depositada en el RITEC.
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- 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.
- 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.
- 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.
- 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.
- Automatic multi-target clinical classification and biomarker discovery in cancer(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-05-10) Ayton, Sarah Gabrielle; JOSE GERARDO TAMEZ PENA; 3059469; Treviño Alvarado, Víctor; puemcuervo, emipsanchez; Tamez Peña, José Gerardo; Martínez Ledesma, Juan Emmanuel; Pavlicova, Martina; Maley, Carlo C.; Fuentes Aguilar, Rita Q; Robles Espinoza, C. Daniela; School of Engineering and Sciences; Campus MonterreyPrecision medicine relies on accurate and interpretable biomarker and subtype discovery. Many multi-omics subtyping algorithms have been developed to manage subtype identification across platforms but have yet to be evaluated with respect to identification of clinically prognostic subtypes. Further, many comprehensive characterization studies of cancer, which have identified multi-omics subtypes or molecular subtype signatures, have done so through the use of manually-derived expert-designed trees. Despite interpretability, current decision tree approaches are unable to explainably reproduce subtyping findings, owing to the complex nature of molecular and clinical factors driving the disease. Current machine learning (ML) approaches do not achieve interpretability (explainability) across disease endpoints, and models constructed manually by trained experts can be subjective. We develop a multi-objective decision tree (MuTATE) framework which performs automated, explainable, and multi-outcome segmentation to construct interpretable trees, simultaneously identifying biomarkers and subtypes of clinical relevance across disease endpoints. Molecular, clinical, and survey data may be input to identify prognostic biomarkers with either preventive or therapeutic implications. We provide a proof-of-concept for multi-objective, quantitative, explainable trees, enabling interpretable, automated molecular insights for precision medicine. This comprehensive approach can improve therapeutic decisions and has applications across complex diseases, and the availability of our method as an R package enables improved access to comprehensive and quantitaive disease modeling to identify those who may benefit from different treatment plans.
- Towards the generation of heuristics for the Job Scheduling Problem via Crowd Computing: A video game approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-10-01) Mendoza Leal, José Martín; TERASHIMA MARIN, HUGO; 65879; Terashima Marin, Hugo; puemcuervo; Monroy Borja, Raúl; Soria Alcaráz, Jorge Alberto; School of Engineering and Sciences; Campus MonterreyThis thesis was conducted for the Master’s in Computer Science Program within the research line of Bio-inspired algorithms with the objective of demonstrating that heuristics for the Job Shop Scheduling Problem (JSSP) can be generated from strategies used by humans when solving the same type of problem, and that this can be done by crowdsourcing using video games. Heuristics are discussed in the computer science field and the psychological field. In both, a heuristic is a quick strategy for decision making when solving a problem which gives up the accuracy of the solution in exchange for obtaining an answer faster. Given that humans tend to use heuristics naturally, an analysis of their behavior can be done to identify these heuristics. The JSSP is an optimization problem within the Scheduling domain where different jobs, composed of activities of different types, must be completed by assigning each of them a position in time in any of the different available machines that are of the same type than the activity, with the intention of finalizing all jobs in the minimum time possible. Scheduling problems including the JSSP are seen in many manufacturing processes and supply chain systems, making them of high interest for companies. Computationally, the JSSP is a hard problem of non-deterministic polynomial time (problems that in order to find the optimal answer, a large amount of computational time is required, making it unfeasible to find a solution when these problems are large) which is why techniques like heuristics are needed to solve big versions of this problem. If people are given the JSSP, in many cases, they will naturally start using heuristics to try to solve it. With this in mind, an analysis designed for identifying heuristics was applied on the behavior of 21 individuals when solving the JSSP. Crowdsourcing is the use of external people to a project in which the intention is to obtain goods or, in our case, data from a group of participants. In this thesis, the generation of data from humans solving JSSPs was crowdsourced using a video game by making people to solve JSSPs inside the game. For this, different JSS problems were gamified, which means to add typical elements of a game like points and score to an activity, so these could be presented as features in a video game. The gamification of activities has been proven to motivate and increase engagement from the participants, being an important factor that influenced the use of a video game for this thesis. A video game that included the gamified JSSPs was given to play to 21 students. The movements that the players used to solve the JSSPs while playing were collected to be later analyzed. This research shows how video games can be used to gather data from humans when solving JSSPs, and then transforming those solution processes into heuristics by using Machine Learning (ML) algorithms. Machine Learning algorithms use experience to create models that simulate a behavior and make decisions, which means that analyzing the movements of human players will create methods intended to emulate their behavior. In order to complete this project, first, experimentation was made on ML algorithms to test their capacity to replicate the behavior of existing heuristics in the literature by training ML models with data generated using those heuristics and under different scenarios. ML algorithms used to create the models were: Decision Trees, Multi-Layer Perceptron, K-neighbors Algorithm, Support Vector Machines and Random Forest Algorithm. After knowing the effectiveness of training and testing these ML algorithms, Decision Trees, Support Vector Machines and Random Forest Algorithms were selected as the better algorithms for continuing the research, and then used to train models with the data collected from the players of the video game. An analysis was conducted on the accuracy on the ML algorithms emulating the players, and aiming at understanding their behavior and strategies for solving problems that can be used as heuristics. The behavior of the ML models were visualized and interesting patterns on how humans solved the JSSP were detected, and promising results were obtained. Some of the heuristics obtained from humans were compared against common heuristics, and interesting conclusions were drawn. This thesis provides the initial steps for generating heuristics from humans when solving difficult computational problems, and leaves and open space for future research to enhance and produce new solution models.
- Analyzing factors that impact alumni income with a machine learning approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-05) Gómez Cravioto, Daniela Alejandra; Hernández Gress, Neil; puelquio; Ceballos Cancino, Héctor; López Guajardo, Rafael; Ceballos Cancino, Héctor Gibrán; School of Engineering and Sciences; Campus Monterrey; Preciado Arreola, José LuisThis thesis presents an exploration of different machine-learning algorithms and different approaches for predicting alumni income. The aim is to obtain insights regarding the strongest predictors for income and a ``high" earners class. The study examines the alumni sample data obtained from a survey from Tec de Monterrey, a multi-campus Mexican private university. Survey results encompass 17,898 observations before cleaning and preprocessing and 12,275 observations after this. The dataset includes values for income and a large set of independent variables, including demographic and occupational attributes of the former students and academic attributes from the institution's history. For the problem of income prediction, there have been several attempts in both social science and econometric studies. However, this study investigates whether the accuracy of conventional algorithms in econometric research to predict income can be improved with a data science approach. Furthermore, we present insights obtained with explainable AI techniques. The results show that the Gradient Boosting Model outperformed the parametric models, Linear Regression and Logistic Regression, in predicting the current income of alumni with statistically significant results (p<0.05) in three different approaches: OLS regression, Multi-class Classification, and Binary Classification. The study also identified that for predicting the alum's first income after graduation, the Linear and Logistic Regression models were the most accurate methods, as the non-parametric models did not show a significant improvement. Succinctly, we identified that age, gender, working hours per week, their first income after graduation, and those factors related to their job position and their firm contributed to explaining their income. Simultaneously, post-graduation education and family background had an insignificant contribution to the model. In addition, the results, which showed a gender wage gap indicate that further work is required to enable equality in Mexico.
- Emotion recognition based on physiological signals for Virtual Reality applications(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06-13) Oceguera Cuevas, Daniela; FUENTES AGUILAR, RITA QUETZIQUEL; 229297; Fuentes Aguilar, Rita Quetziquel; puemcuervo; Antelis Ortíz, Javier Mauricio; Fernández Cervantes, Victor; School of Engineering and Sciences; Campus Monterrey; Hernández Melgarejo, GustavoVirtual Reality (VR) Systems have been used in the last years with an increasing frequency because they can be implemented for multiple applications in various fields. Some of these include aerospace, military, psychology, education, and entertainment. A way to increase the sense of presence is to induce emotions through the VE, and since one of the main purposes of VR Systems is to evoke the same emotions as a real experience would, the induction of emotions and emotion recognition could be used to enhance the experience. The emotion of a user can be recognized through the analysis and processing of physiological signals such as Electrocardiogram (ECG) and Electrodermal Activity (EDA) signals. However, very few systems that present online feedback regarding the subject’s emotional state and the possibility of adapting the VE during user experience have been developed. This thesis proposes the development of a Virtual Reality video game that can be dynamically modified according to the physiological signals of a user to regulate his emotional state. The first experiment served for the creation of a database. Previous studies have shown that specific features from these signals, can be used to develop algorithms capable of classifying the emotional states of the subjects into multiple classes or the two emotional dimensions: valence and arousal. Thus, this experiment helped to develop an appropriate Virtual Reality video game for stress induction, a signal acquisition, and conditioning system, a signal processing model and to extract time-domain signal features offline. A statistical analysis was performed to find significant differences between game stages and machine learning algorithms were trained and tested to perform classification offline. A second experiment was performed for the Proof of Concept Validation. For this, a model was created to extract features online and the classification algorithms were re-fitted with the online extracted features. Additionally, to facilitate a completely online process, the signal processing and feature extraction models were embedded on an STM32F446 Nucleo board, a strategy was implemented to dynamically modify the VE of the Virtual Reality video game according to the detected class, and the complete system was tested.
- Machine learning to predict rework time for CNC router(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-30) González Giacoman, Daniel Alejandro; URBINA CORONADO, PEDRO DANIEL; 298324; Urbina Coronado, Pedro Daniel; puemcuervo; Orta Castañón, Pedro Antonio; Ahuett Garza, Horacio; School of Engineering and Sciences; Campus MonterreyThe industry is always in constant change and looking for ways to gain an advantage over its competitors. The fourth industrial revolution has brought massive change to the way things are done in the industry. The fourth industrial revolution brought Big Data, the Internet of things and Artificial intelligence, which gives us new ways to gather a lot of information from different sources and use it for our benefit. The present work develops a methodology to create a new machine learning algorithm to predict rework time for pieces that come out of a CNC router, using python and prove that for this case the created algorithm is better than a statistical model. To validate the methodology and prove the hypothesis of the thesis an experiment will be made to obtain 2 results: the best set of cutting parameters for the selected material and which is the best machine learning algorithm for this problem. To make the experiment the parameters must be set, a database needs to be created to train and test the ML algorithms and the code and libraries to be used should be created to fit the problem to be solved. This will be done by giving a background into databases, artificial intelligence, and how to know by the given results which type of artificial intelligence method is the best for the proposed problem.
- Antimicrobial resistance prediction by bacterial genome-wide-association-study in non-fermenting bacilli with critical priority (Pseudomonas aeruginosa and acinetobacter baumannii).(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-07) Barlandas Quintana, Erick Alan; MARTINEZ LEDESMA, JUAN EMMANUEL; 200096; Martinez Ledesma, Juan Emmanuel; puemcuervo; González Mendoza, Miguel; Garza González, Elvira; School of Engineering and Sciences; Campus Estado de México; Cuevas Díaz Durán, RaquelAntimicrobial resistance (AMR) (or drug resistance) is a natural phenomenon where microor- ganisms change their molecular, physical, or chemical structures to resist the drugs created by infections. The World Health Organization (WHO) had released for the first time a list of Multidrug-Resistant Bacteria (MRB) that pose the greatest threat to human health and for which new antibiotics are desperately needed. Acinetobacter baumannii and Pseudomonas aeruginosa resistant to carbapenems are part of the Gram-negative non-fermenting bacilli group with critical priority according to the WHO. For this, the final research purpose was to create and train a bioinformatic study capable of finding critical k-mers that could differentiate those strains of P. aeruginosa and A. baumannii resistant to carbapenems. Four k-mers sizes were performed for each bacterium (12, 14, 16, and 18), and two training and testing (70:30 and 80:20) schemas were used over seven different machine learning algorithms: Random Forrest, Adaboost, Xgboost, Decision Trees, Bagging Classifier, Support Vector Machine, and KNN. For both bacteria, the best models were obtained when using a k-mer length of 12. In the case of Acinetobacter baumannii, the best models obtained an accuracy of 0.99 for testing. Moreover, for Pseudomonas aeruginosa, the best accuracy obtained was 0.93 when us- ing Bagging Classifier. To investigate the sequences of the k-mers obtained, the National Cen- ter for Biotechnology Information (NCBI) Basic Local Alignment Search Tool BLAST was used. Ten to twenty sequences built with the k-mers were investigated for each model. When using a k-mer length of 12 for A. baumannii, 18 out of 20 sequences represented a crucial sequence in carbapenems (meropenem and imipenem) resistance. In the case of P. aerugi- nosa, 16 out of 20 sequences represented a key sequence. To complement this research, a Dynamic Programming algorithm was used to find changes over the reference genome that could explain the carbapenems resistance within the resistant genomes. Not all the resistant k-mer sequences were found over the reference genome, as some of them could be acquired by horizontal transference (Conjugation, Transformation, or Transduction inheritance). Fur- ther investigation over these sequences can be applied in creating new directed antibiotics or detecting easily resistant strains of Pseudomonas aeruginosa or Acinetobacter baumannii resistant to carbapenems.
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