http://hdl.handle.net/11422/337 Mon, 20 Jul 2026 21:08:25 GMT 2026-07-20T21:08:25Z http://hdl.handle.net/11422/29727 Title: Impactos de eventos extremos de calor sobre a prematuridade: uma abordagem estatística aplicada à saúde pública Author(s)/Inventor(s): André, João Francisco Fortes Advisor: Landim, Flávia Maria Pinto Ferreira Publisher: Universidade Federal do Rio de Janeiro Type: Trabalho de conclusão de graduação Tue, 26 May 2026 00:00:00 GMT http://hdl.handle.net/11422/29727 2026-05-26T00:00:00Z http://hdl.handle.net/11422/29685 Title: Machine learning methods in music emotion recognition Author(s)/Inventor(s): Dessabato, Karolayne Pereira Advisor: Carvalho, Hugo Tremonte de Abstract: Music Emotion Recognition (MER), an area within Musical Information Retrieval (MIR), studies the emotions evoked in listeners by music. We address MER as a re gression task, with the objective of predicting the emotional content of music (encoded in arousal and valence) from acoustic features extracted from the waveform. We apply an interpretable machine learning technique, investigating the role of these features in predicting the target variables. Initially, a random forest model is trained on the DEAM dataset (MediaEval Database for Emotional Analysis of Music). Then, we use the con cept of Shapley values to interpret the role of each variable in the predictions made by this model. Finally, we extract the most significant features from the DEAM dataset to predict arousal and valence, thus enhancing the interpretability of the model employed. Additionally, we explore a dynamic linear model approach to gain further insights into the relationships between features and response variables. This method allows for a potentially “less black-box” and more interpretable representation of the problem. Prin cipal Component Analysis (PCA) is also utilized to analyze the structure of features in the dataset, providing a more comprehensive understanding of the key variables influ encing MER predictions. By integrating these approaches, we aim to enhance both the predictive performance and interpretability of the models, offering meaningful insights into the most relevant features that drive emotional responses in music. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11422/29685 2025-01-01T00:00:00Z http://hdl.handle.net/11422/29636 Title: Algoritmos para Multi-Armed Bandits: teoria e aplicação à precificação dinâmica Author(s)/Inventor(s): Bastos, Ismael Sampaio Advisor: Iacobelli, Giulio Abstract: This work addresses the problem of sequential decision-making, focusing specifically on the multi armed bandit (MAB) framework. In its classical formulation, the MAB problem involves an agent facing a row of slot machines (bandits), with a limited number of pulls (arms) available. The agent’s goal is to determine a sequence of actions that maximizes the total reward. The core challenge lies in balancing the trade-off between choosing the action that currently appears to yield the highest reward and exploring lesser-known alternatives (a dilemma known as exploration versus exploitation). In this study, we explore several algorithms designed to support decision-making within the multi armed bandit setting. We also examine an application of this theory to the problem of dynamic pricing, i.e., determining optimal selling prices for products and services. In this context, the seller takes the role of the agent who aims to sell a product by selecting from a finite set of possible prices, without prior knowledge of demand or consumer behavior. The seller must therefore adopt a strategy that enables the identification of the optimal price over time. Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11422/29636 2025-01-01T00:00:00Z http://hdl.handle.net/11422/27647 Title: Inferência variacional aplicada a modelos de mistura Author(s)/Inventor(s): Soares, Iran Cruz Advisor: Carvalho, Hugo Tremonte de Publisher: Universidade Federal do Rio de Janeiro Type: Trabalho de conclusão de graduação Fri, 01 Aug 2025 00:00:00 GMT http://hdl.handle.net/11422/27647 2025-08-01T00:00:00Z