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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- Digital violence against women: a phenomenon exploration to understand and counteract from a Data Science perspective(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-02) Reyes González, Gregorio Arturo; Cantú Ortiz, Francisco Javier; puemcuervo/tolmquevedo; Galeano Sánchez, Nathalíe María; Ceballos Cancino, Héctor Gibrán; Serrano Estrada, Leticia; School of Engineering and Sciences; Campus Monterrey; Gabarrot Arenas, MarianaInvestigations have shown that Violence Against Women is a pervasive problem that has been increasing over the last years. Until a few years ago, it took place both in public and private spaces, but it has now broken into Digital Space adopting more symbolic expressions. Mexican cyberfeminists have fought to put this social problem on public agenda, achieving this past June to legally typify Violence Against Women in Digital Space at the federal level. There have been some important related work from Data Science approaches but mainly on cyberbullying and in the detection of language patterns through supervised algorithms, through social network features, through profile information, a few works on unsupervised learning, and on violence against women. However, it is important to tackle Digital Violence Against Women as a phenomenon with its particularities and separated from cyberbullying. Moreover, it is necessary to study this phenomenon from a gender perspective since all crimes against women are contained by a gender symbolic structure. The hypothesis of this Thesis Project is that Data Science approaches such as Text Mining, Supervised Learning, Time Series Analysis, Natural Language Processing, and Network Analysis can find associations between proposed variables of Spanish-language text data from microblogging social network, Twitter, datasets. The goal of this thesis is to implement Data Science techniques to analyze the Digital Violence Against Women phenomenon in order to achieve the identification of major associations that will let us understand and counteract violent social discourses and structural violence in digital space. The proposed model is composed of several techniques such as Time Series Analysis, Natural Language Processing, and Network Analysis, that are fed by the outcomes of the ensemble between Supervised Classifiers and an Ontological Matcher. Results indicate a higher presence of Digital Violence Against Women for the predicted tweets under the ensemble algorithm in comparison with just the Supervised Learning Algorithms or just the Ontological Matcher. Time Series Analysis shows peaks in Digital Violence Against Women in dates that correspond to days in which the fight for Women’s Rights was positioned. Natural Language Processing confirms the existence of a violent semantic discourse under this phenomenon. And, Network Analysis exhibits generalized individual attacks connected to a structural and systemic problem. Finally, there were four strategies proposed to counteract Digital Violence Against Women, which are based on detection, prevention, and specificity of the phenomenon.
- Discovering if mexican compensatory duties should be extended beyond China via a data science approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12) Martínez Gutiérrez, Juan Raúl; CANTU ORTIZ, FRANCISCO JAVIER; 9804; Cantú Ortiz, Francisco Javier; puemcuervo, emipsanchez; Brena, Ramón; Barrón Cano, Olivia; School of Engineering and Sciences; Campus Monterrey; Ceballos Cancino, Héctor GibránIn international trade, when suspicion exist of dumping or subsidy, countries may draw upon compensatory duties. Such duties are intended to alleviate the harm caused to domestic economy. In Mexican case, compensatory duties are aimed at products imported from specific nations since their prices are too low that are considered as menacing for local producers. In most of the cases, compensatory duties are aimed at China. Although Mexican authorities set these duties after a thoughtful process, this research tries to prove that these taxes fall short of scope and are focused on Chinese products only. Every year in Mexico take place around 10 millions of imports and exports, and Mexican government, via the Tax Administration Service (SAT), make the information regarding these operations available to be consulted. That information is analyzed following a data science approach to suggest whether compensatory duties should be extended to other countries or not. By relying on statistical methods and machine learning classification algorithms, the import unitary prices of Chinese products and other countries are studied. If a statistical difference can be estimated or the algorithms can distinguish from charged and non-charged prices, then the data would support the existence of a compensatory duty fixed on China. On the contrary, then the justification of a compensatory duty fixed on China would be at least questionable. It can be advanced that statistical approach, for some products, found counter-intuitive price behaviors supported by classification algorithms, and that not all algorithms are suitable for this matter. Such findings suggest that, for products with counter-intuitive price behaviors, compensatory duties fixed on China are questionable.
- Evaluating teaching performance of teaching-only and teaching-and-research professors in higher education through data analysis(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06) Chávez López, Mario Daniel; Cantú Ortiz, Francisco Javier; emipsanchez/tolmquevedo; Torres Delgado, Gabriela; Hernández Gress, Neil; Barrón Cano, Olivia Maricela; Escuela de Ingeniería y Ciencias; Campus Monterrey; Ceballos Cancino, Héctor GibránWe present a study that compares the teaching performance of Teaching-only versus Teaching-and-research professors at higher education institutions. It is a common belief that, generally, Teaching-only professors outperform Teaching-and-Research professors in teaching and research universities according to student perception reflected in student surveys. We present a case study which demonstrates that, in the vast majority of the cases, it is not necessarily true. Our work analyzes these two type of professors at their ability to function as an intellectual challenger, learning guide and their tendency to be recommended to other students. The case study takes place at Tecnológico de Monterrey (Tec), a teaching and research private university in Mexico that has developed a research profile during the last two decades with a mix of teaching-only and teaching and research faculty members and shows a growing accomplishment on world university rankings. We use five datasets from a student survey called ECOA which accounts observations from 2016 to 2019. We present the results of statistical and machine learning methods applied when the taught courses of more than nine thousand professors are taken into account. Methods include Analysis of Variance, Logistic Regression, Recursive Feature Elimination, Coarsened Exact Matching and Panel Data. Contrary to common belief we show that, for the case presented, teaching and research professors perform better or at least the same as teaching-only professors. We also document the differences found on teaching with respect to attributes related to courses and professors.
- A data analytics approach for university competitiveness: the QS rankings(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06) Estrada Real, Ana Carmen; ESTRADA REAL, ANA CARMEN; 791773; Cantú Ortiz, Francisco Javier; emipsanchez/puemcuervo; Sucar Succar, Luis Enrique; Galeano Sánchez, Natalíe María; Hernández Gress, Neil; Monroy Borja, Raúl; School of Engineering and Sciences; Campus Estado de México; Ceballos Cancino, Héctor GibránIn recent years, higher education has been facing the entrance to the internationalmarket due to globalization, this has developed a highly competitive environment, in whichmany institutions have used university rankings as a tool to attract the best academic andstudent talent from all over the world. In this work we take as a base the ranking of QSWord University Rankings and QS Best Student Cities, to apply data science techniques.Extract information on the performance of the most attractive institutions and cities forstudents worldwide, and develop a methodology that allows the stakeholders of the insti-tutions and cities to improve their services for the benefit of students interested in receivingan education of global quality. We accumulated ten years of university rankings (2011-2020) and six years of city rankings (2014-2019), we carried out an exploratory analysisof the indicators and their influence with the final score, later we trained a multiple regres-sion model and panel data to make predictions in the score. Finally, in order to predictthe position, we carry out groupings and train various machine learning algorithms. Withthis work we show a methodology that allows administrators to plan long-term institutionalimprovements to offer a better education and improve their performance in world rankings.
- Un ambiente de trabajo para sistemas de información basados en razonamiento bayesiano(Instituto Tecnológico y de Estudios Superiores de Monterrey, 1905-06-25) Robles Pompa, Armando; Cantú Ortiz, Francisco Javier; Garza Castañón, Luis Eduardo; Morales Menéndez, Rubén; ITESM; Garza Salazar, DavidEn esta tesis se implementan dos algoritmos de razonamiento Bayesiano, y se propone el ambiente de trabajo BIframework (Business Intelligence framework) para utilizarlos en sistemas de información que operan en línea. Se plantea el diseño conceptual de BIframework y se construye un prototipo mostrando su funcionalidad en un caso de estudio, donde se utiliza mediante BIframework el razonamiento Bayesiano para la asignación de asesores en la operación en línea de un sistema de servicio a clientes en una empresa de software.