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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- Deep learning framework to predict and generate new fluorescent molecules from experimental data(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-07) Azizi, Mina; Aguirre Soto, Héctor Alán; emipsanchez; Ray, Mallar; Bernal Neira, David Esteban; Mendoza Cortés, José Luis; School of Engineering and Sciences; Campus Monterrey; Flores Tlacuahuac, AntonioFluorescent molecules play important roles in biological imaging, diagnostics, and materials science. However, identifying efficient and effective fluorophores remains challenging, as traditional trial-and-error experimentation and in silico computations are both costly and time-consuming. To address this, this thesis presents a deep learn- ing approach to streamline the discovery process by predicting optical properties and generating novel fluorescent molecules directly from experimental data. The study is based on FluoDB, a publicly available dataset collected from the literature, containing over 55,000 fluorophore–solvent pairs with experimentally measured optical prop- erties. Graph Convolutional Network (GCN) models were trained to predict four key optical properties and effec- tively captured complex structure–property relationships, achieving R² values ranging from 0.49 to 0.87 across the different targets. A Conditional Variational Autoencoder (CVAE) was also implemented to generate novel fluores- cent molecules based on solvent identity and target absorption range. In total, 2573 valid and structurally diverse molecules were generated, with a variety of predicted optical behaviors. Together, the predictive model and genera- tive models provide a useful and data-driven approach to accelerate exploration and design of functional fluorescent materials.
- Machine learning detection of severity level of maladaptive plasticity in tinnitus and neuropathic pain(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023) González Sánchez, Andrea; Alonso Valerdi, Luz María; emimmayorquin; Ibarra Zarate, David Isaac; Román Godínez, Israel; Tamez Peña, José Gerardo; Montemayor Zolezzi, Daniela; Escuela de Ingeniería y Ciencias; Campus MonterreyTinnitus and NP datasets were analyzed separately and in conjunction, under the hypothesis that they share an underlying mechanism of maladaptive plasticity. Linear and non-linear features were extracted from the EEG signal data, including power spectral density, Shannon entropy and imaginary coherence between channel data. Feature selection with BorutaSHAP wrapper method was applied as part of the model construction process. Classification was performed using both traditional ML algorithms, Random Forest, Support Vector Machine, and k-Nearest Neighbors, and then implementing a prediction voting mechanism, as well as the deep learning neural network EEGNet. In general, SVM had the best performance across experiments. For the tinnitus dataset, the ensemble classifier had the highest accuracy of 50.08%. For the NP dataset, SVM had the best performance measured through an accuracy of 58.43%. For the BC dataset, the highest accuracy score of 42.46% was obtained by SVM. For the EEGNet implementation, the average accuracy obtained in the tinnitus dataset was 51%, the NP dataset was 97.92%, and the BC dataset was 74.31%. EEGNet was the best performing model, particularly for the NP dataset. The top selected features of the feature selection algorithm suggests gamma as a potential biomarker for the detection of maladaptive plasticity.
- Real time distraction detection by facial attributes recognition(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-09) López Esquivel, Andrés Alberto; González Mendoza, Miguel; puemcuervo; Gutiérrez Rodríguez, Andrés Eduardo; Marín Hernández, Antonio; School of Engineering and Sciences; Campus Monterrey; Chang Fernández, LeonardoThe deficit of attention on any critical activity has been a principal source of accidents leading to injuries and fatalities. Therefore the fast detection of it has to be a priority in order to achieve the safe completion of any task and also to ensure the display of the maximum capabilities of the user when achieving the respective activity. While multiple methods has been developed, a new trend of non-intrusive vision based methodologies has been strongly picked by both the research and industrial communities as one with the most potential effectiveness and usability on real life scenarios. In this thesis research, a new attention deficit detection system is presented. Low-weight Machine Learning algorithms will allow the use in remote applications and a variety of goal devices to avoid accidents caused by the lack of attention in complex activities. This research describes its impact, its functioning and previous work. In addition, the system is broken down into its most basic components and its results in various evaluation stages. Finally, its results in semi-real environments are presented and possible applications in real life are discussed, while being compared to state of the art implementations such as CNN’s, Deep learning and other ML implementations
- Mining contrast patterns from multivariate decision trees(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2018) Cañete Sifuentes, Leonardo Mauricio; Monroy, Raúl; emimmayorquin; Jair Escalante, Hugo; Conant Pablos, Santiago Enrique; Loyola González, Octavio; Escuela de Ingeniería y Ciencias; Campus Estado de México; Medina Pérez, Miguel AngelCurrently, there is a growing interest in the development of classifiers based on contrast patterns (CPs); this is partly due to the advantage of them being able to explain a classification result in a language that is easy to understand for an expert. Thorough experiments show that CP- based classifiers, when using contrast patterns extracted by miners based on decision trees, attain accuracies comparable with state-of-the-art classifiers like SVM, k-NN, C4.5, Bagging and Boosting. Existing decision tree-based miners use Univariate Decision Trees (UDTs) to extract CPs. For tree-based classification classifiers based on Multivariate Decision Trees (MDTs) achieve better accuracy than those based on UDTs. This result might be attributable to that MDTs use multivariate relations (e.g., 2height + 3weight > 40) which, in some cases, separate better the classes than the univariate relations (e.g., age > 40) that UDTs use. Our hypothesis runs parallel, but for CP-based classification: using CPs extracted from MDT-based miners, which we call multivariate contrast patterns, a CP-based classifier shall significantly improve on the performance of others based on UDTs. We propose an algorithm to extract, simplify and filter multivariate CPs. We make an empirical study of our proposed algorithm. We use 112 datasets, taking half of the datasets for tuning the parameters of our algorithm. To validate our hypotheses, we use the other half of the datasets as a testing set to compare our algorithm against other state-of-the-art CP miners in terms of quality, and against other state-of-the-art classifiers, in terms of classification performance. The results obtained in the testing set show that the quality of multivariate CPs, in terms of Jaccard, is significantly higher than that of CPs extracted through UDTs (univariate CPs). We also show that the classification results for CP-based classifiers are significantly better when using multivariate CPs than when using univariate CPs; which could be explained by the higher quality of multivariate CPs. The classification results for multivariate CP-based classifiers are also competitive with non-pattern-based state-of-the-art classifiers. Yet, the plus is that multivariate CP-based classifiers provide contrast patterns, which are abstract-level explanations that could help an expert to gain insights in the problem under investigation.