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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- Unsupervised learning to profile emerging researchers in LATAM with Elsevier’s data(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Figueroa Castillo, Jesús Manuel; Hernández Gress, Neil; mtyahinojosa, emimmayorquin; Ceballos Cancino, Héctor Gibrán; Estévez Bretón, Carlos Manuel; School of Engineering and Sciences; Campus Monterrey; Hervert Escobar, LauraThis proposal is being presented in Computer Science. High-impact researchers possess several key features based on their expertise; never theless, it takes time to establish themselves as leaders in their area. The objective of this research is to develop a model that can identify those outstanding researchers by discipline using indicators from the last five years of research and acknowledged databases such as Sco pus and Web of Science. Additionally, it will compare similarities across various disciplines to determine whether it is possible to predict researchers from one or more disciplines using the same model. The main objective of this research is to discover the characteristics that define a ”rising star” based on the concept of an early career researcher as a initial time window. It is important to mention that current metrics measure researchers’ performance through indicators known as H-index and its variants. However, these metrics often do not consider characteristics that differentiate one group from another. Through this unsupervised approach, we aim to f ind different groups that exist in LATAM to measure their characteristics more precisely and fairly, and to identify those high-impact researchers who may not be immediately apparent through indicators like the H-index. This thesis will demonstrate the process from data mining to the statistical analysis of the different groups.
- Design and implementation of a quantum multilayer neural network framewori(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12) Gamboa Vázquez, Ariel Arturo Goubiah; HERNANDEZ GRESS, NEIL; 21847; Hernández Gress, Neil; puemcuervo; Aspuru Guzik, Alan; González Mendoza, Miguel; School of Engineering and Sciences; Campus MonterreyArtificial Neurons are biologically inspired algorithms that form the building blocks for Artificial Neural Networks (ANNs) and Multilayer Neural Networks (MNN), which have been recently studied and implemented to solve important ptoblems. Advances in Learning theory and the availability of powerful computational systems has resulted in the creation of many real-world applications. Practically every industry has already adopted Multilayer Learning powered technologies in some part of their processes, as state of the art MNNpowered algorithms can outperform other algorithms and even human accuracy for a wide number of tasks. However, their performance relies heavily on the budget of data available as well as its format, as the most popular applications require a copious amount of training examples. Another limitation to build large scale MNN applications is the vast computational resources needed to build these systems. MNN based algorithms usage is widespread and also getting more complex, this phenomenon creates an ever-growing demand for computational power, which may no longer be satisfied at some point in the new future, thanks to the deceleration in state of the art monolithic processors’ performance. Quantum information theory, is a field that has had success in the last couple of decades, thanks to the creation of algorithms that are in theory able to outperform classical computers. The ability of quantum computers of working with inherently different physical systems than the ones used by classical computers, opens an exciting opportunity for scientists and companies to explore the performance of quantum computers for machine learning tasks, being multilayer learning a focus point, thanks to its importance in classical computing. Although a considerable amount of resources have been allocated to the development of MNN powered algorithms in quantum computers, there are still challenges left to overcome before Quantum Multilayer Neural Networks come to be a technology that can compete with state of the art MNN powered algorithms. This research explores the properties of multilayer neural network algorithms running on quantum computers. The first contribution of the research work reported in this document is the analysis and implementation of a perceptron algorithm running on a quantum computer. The second contribution is the proposal, implementation and analysis of two different information encoding methods for quantum computers. The final, and most important contribution of this work, is the development of a framework that allows training multilayer neural networks for Supervised Learning.