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.
- The role of capitalization and character repetition in identifying depression on social Media: a bilingual approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-24) Burgueño Paz, Luis Humberto; Zareei, Mahdi; emipsanchez; Roshan Biswal, Rajesh; School of Engineering and Sciences; Campus Monterrey; García Ceja, Enrique AlejandroDepression is a mental disorder that affects millions of people worldwide, but a significant portion of the affected people don’t receive adequate treatment. There has been an increasing interest from researchers to detect this condition through social media posts in order to prompt for early treatment. However, most of the research has been focused on the Caucasian Western English-speaking population, limiting the applicability of their findings across diverse cultural contexts. While research has shown the use of nonverbal cues to convey sentiment, their role on depression detection remains under-explored. This thesis aims to assess the effect of nonverbal cues, specifically capitalization and character repetition, on depression detection using datasets both in English and Spanish. This effect was explored through three existing datasets. The first dataset included a collection of Reddit posts and comments in the English language and was selected to assess the effect on a dataset coming from one of the most reputable mental health competitions in Natural Language Processing. The second dataset consisted of a collection of Spanish- language messages from Telegram to verify whether findings in the English language would hold for Spanish. The third dataset, also built from Reddit posts, was used to analyze the impact of these features when classifying by depression severity levels rather than binary labels. Four classifiers were used throughout this research: Logistic Regression, Random Forest, Support Vector Machine, and Neural Network. Overall, the impact of capitalization and character repetition for depression detection was found to be minimal. These features had the most effect on English Reddit data with binary labels, while showing limited impact on Spanish data or when classifying by severity levels. Additionally, models using only character repetition outperformed those relying on capitalization features.
- Machine learning model for road asphalt monitoring system: vibration-based approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-12) López Castañeda, Carlos Alonzo; ZAREEI, MAHDI; 822705; Zareei, Mahdi; puemcuervo, emipsanchez; Roshan Biswal, Rajesh; Meza Ruiz, Ivan Vladimir; School of Engineering and Sciences; Campus MonterreyTo achieve safe and correct driving, it is necessary to have a surveillance plan and the maintenance of highways and roads, in order to maintain a good infrastructure. Mexico has a paved and unpaved network of 780, 511 km, of which is paved 174, 779 km. According to statistics from the INEGI, in 2019, there were 9,318 accidents due to poor road conditions. There are several types of breakdowns on any paved surface, and they may differ depending on the country. For example, potholes, cracks, and patches are some road surface damages essential to assess in Mexico. In 2020, INEGI presents that 96.8\% of the population identified the issue of potholes in streets and avenues, as the problem with the highest frequency nationwide, above crime. Thus, the conditions of our roads are of deep concern for the population. Different forms of road condition monitoring are proposed in the last years by specially designed instruments, using cameras, lasers, which require time and money and can only cover a limited proportion of the road network. Analogous to a video feed visually inspecting the asphalt's surface, a vibration-based system measures the ground conditions based on mechanical feedback from a vehicle. Different road anomalies, including potholes, cracks and ruts in the surface, create forces on the car, the frequency and magnitude of the forces will depend a lot on the type of anomaly. After we investigated different related works, this thesis is going to build on some of their aspects, and make a mix of others. The idea of dividing into three different categories for the classification of the roads, and the usage of supervised learning for road surface quality and anomaly detection. Regarding data collection, it was done through a phone with an Android system and an application created specifically for this job. This thesis proposes a pothole detection model using a vibration base method, using built-in vibration sensors in smartphones. We collected road condition data in Mexico City using a dedicated vehicle and smartphones with a purpose-built mobile application designed for this study, splitting the data into: bump, bump, normal. A processing method was applied to the collected data, and features were extracted, then classified with a neural network. The results indicated that using only the subset of two of the three selected event types, together with their six characteristics, they outperformed other subsets in identifying potholes. Our neural network classifier showed classification performance, with an accuracy of 98\%.
