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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- 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.
- Caption generation with transformer models across multiple medical imaging modalities(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06) Vela Jarquin, Daniel; Santos Díaz, Alejandro; dnbsrp; Soenksen, Luis Ruben; Montesinos Silva, Luis Arturo; Ochoa Ruiz, Gilberto; School of Engineering and Sciences; Campus Monterrey; Tamez Peña, José GerardoCaption generation is the process of automatically providing text excerpts that describe relevant features of an image. This process is applicable to very diverse domains, including healthcare. The field of medicine is characterized by the vast amount of visual information in the form of X-Rays, Magnetic Resonances, Ultrasound and CT-scans among others. Descriptive texts generated to represent this kind of visual information can aid medical professionals to achieve a better understanding of the pathologies and cases presented to them and could ultimately allow them to make more informed decisions. In this work, I explore the use of deep learning to face the problem of caption generation in medicine. I propose the use of a Transformer model architecture for caption generation and evaluate its performance on a dataset comprised of medical images that range across multiple modalities and represented anatomies. Deep learning models, particularly encoder-decoder architectures have shown increasingly favorable results in the translation from one information modality to another. Usually, the encoder extracts features from the visual data and then these features are used by the decoder to iteratively generate a sequence in natural language that describes the image. In the past, various deep learning architectures have been proposed for caption generation. The most popular architectures in the last years involved recurrent neural networks (RNNs), Long short-term memory (LSTM) networks and only recently, the use of Transformer type architectures. The Transformer architecture has shown state-of-the art performance in many natural language processing tasks such as machine translation, question answering, summarizing and not long ago, caption generation. The use of attention mechanisms allows Transformers to better grasp the meaning of words in a sentence in a particular context. All these characteristics make Transformers ideal for caption generation. In this thesis I present the development of a deep learning model based on the Transformer architecture that generates captions for medical images of different modalities and anatomies with the ultimate goal to aid professionals improve medical diagnosis and treatment. The model is tested on the MedPix online database, a compendium of medical imaging cases and the results are reported. In summary, this work provides a valuable contribution to the field of automated medical image analysis
- Design and implementation of a Chatbot for answering questions on scientometric indicators(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-05-23) López Rodríguez, Víctor Iván; HERNANDEZ GRESS, NEIL; 21847; Ceballos Cancino, Héctor; puemcuervo; Hernández Gress, Neil; Alvarado Uribe, Joanna; Juárez Ibarra, Erika Alejandra; Garza Villarreal, Sara Elena; School of Engineering and Sciences; Campus MonterreyScientometrics is the field of study and evaluation of scientific measures such as the impact of research papers and academic journals. It is an essential field because nowadays, different rankings use key indicators for university rankings, and universities themselves use them as Key Performance Indicators (KPI). The first objective of this research work is to propose a semantic model of scientometric indicators by generating a statistical ontology that extends Statistical Data and Metadata Exchange (SDMX). We develop a case study at Tecnologico de Monterrey following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. We evaluate the benefits of storing and querying scientometric indicators using linked data in Neo4j to provide flexible and quick access knowledge representation that supports indicator retrieval, discovery, and composition based on a self-knowledge strategy. The semantic representation can answer a simple query using dimensions, query returning values with time intervals, aggregation functions such as average and standard deviation, and calculate a new scientometric indicator with data stored in the ontology. The second objective of this research work is to integrate the proposed statistical ontology model of scientometric indicators in a chatbot. Building a chatbot requires the use of Natural Language Processing (NLP) as a capability for recognizing users' intent and extracting entities from users' questions. We proposed a method for recognizing the requested indicator and transforming the question expressed in natural language into a query to the semantic model. The chatbot and the ontology model represent a novel framework that can answer questions from the Research Office about scientometric indicators. The chatbot is evaluated in terms of Goal Completion Rate (GCR). It measures how many questions the chatbot answered correctly and correctly identifies intent and entity extraction. The second evaluation approach of the chatbot is a survey that focuses on usability, the strictness of language variations, chatbot comprehension, correlation in chatbot responses, and user satisfaction. The main contribution of this research is the structural representation of the type of question that can be performed over the indicators modeled with SDMX. We simplify the model training and interpretation of questions by defining complexity levels and extracting entities from the question. We demonstrate how a chatbot can answer questions about any indicator modeled with SDMX. The chatbot can be trained to recognize another way to formulate questions without impacting the semantic representation of the indicators. The model is scalable because we can add more indicators using RDF, and the chatbot will only require minor changes (e.g., adding new dimensions).
- Detecting empathy on textual communication(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11) Montiel Vázquez, Edwin Carlos; RAMIREZ URESTI, JORGE ADOLFO; 21998; Ramírez Uresti, Jorge Adolfo; emijzarate/puemcuervo; Monroy Borja, Raúl; González Mendoza, Miguel; Montes y Gómez, Manuel; School of Engineering and Sciences; Campus Estado de México; Loyola González, OctavioEmpathy is a necessary component of human communication. The ability to understand and relate to others provides depth to any conversation between people, and is the basis for any exchange that deals with highly emotional topics. Current technological developments have raised interest in human-like behavior from computer systems regarding communication. This has led to the development of the area known as Affective computing, which is based on the study and processing of concepts related to emotions through artificial intelligence. However, in this area, empathy has been largely ignored in favor of other concepts such as emotion and feeling. This can be attributed to the complexity inherent of the concept. Nevertheless, there are now several methods that can be used to finally study and take advantage of empathy in computer applications. We provide a comprehensive study on the nature of empathy and a method for detecting it in textual communication. Thanks to this research, we present a database of conversations with their respective measurement of empathy. This metric, the Empathy score, is the first method for measuring empathy on texts based on psychological research. In order to detect the value of empathy on conversations, we apply machine learning classification. A pattern-based classification approach was taken in order to predict the Empathy score of utterances in our database, which allowed us to explore the advantages presented by these algorithms in psychologically-adjacent computing research. We were able to use methods found in computer science for the study and detection of empathy, and prove the viability of contrast pattern-based classification for measuring empathy levels on textual conversations.
- Contrast pattern-based classification on sentiment features for detecting people with mental disorders on social media(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-22) Gallegos Salazar, Leslie Marjorie; LOYOLA GONZALEZ, OCTAVIO; 553351; Loyola-González, Octavio; emipsanchez; School of Engineering and Science; Campus Estado de México; Medina-Pérez, Miguel AngelMental disorders are a global problem that widely affects different segments of the population. Mental disorders present consequences in the life of those suffering from them as they can have difficulties performing daily tasks normally. However, consequences in the economy, society, human rights, and cultural scope are also present as it is a problem that has been growing for a long time. Diagnosis and treatment are difficult to obtain as there are not enough specialists on the matter, and mental health is not yet a common topic among the population. Specialists in varied areas have proposed multiple solutions for the detection of the risk of depression; the computer science field has proposed some, based on language use and the data obtained through social media. Those solutions are mainly focused on objective features like n-grams and lexicons. We propose a contrast pattern-based classifier for detecting depression by using a new data representation based only on sentiment and emotion analysis extracted from post on social networks. The representation contains 28 different features which include information on sentiment, emotion, polarity, sarcasm, and other subjective information of the text. We then used a classifier that has not been used before in the state-of-the-art and obtained an AUC between 0.71 and 0.72. Finally we reproduced state-of-the-art models and statistically compared them with the result of the proposed model. The results show no significant statistical difference with a reproduction of the models found in the state-of-the-art. Furthermore, with the classifier used we were able to provide an explanation close to the language of an expert on the decision of the classifier.
