TCC - Bacharelado em Sistemas da Informação (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/427
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Resultados da Pesquisa
Item Utilização de filtragem colaborativa no auxílio de recomendação personalizada para leitores de mangá(2024-03-04) Brochardt, Rodrigo Nativo do Brasil; Garrozi, Cícero; http://lattes.cnpq.br/0488054917286587This study investigated, developed, and compared two approaches for generating manga recommendations: the Singular Value Decomposition (SVD) model and the Pearson Correlation Coefficient. The methodology involved data preparation through the development and execution of a web scraper to extract manga information and reviews from a highly active internet forum. Challenges arising in the applicability of these data extraction methods were addressed, along with alternatives for handling source blocking situations, model training, and performance evaluation, focusing on collaborative filtering and personalized recommendations for user profiles and manga works. In the implementation of SVD, latent patterns in user review data were identified, enabling personalized recommendations based on individual preferences through the sharing of experiences with similar profiles. However, metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) revealed the need for model refinement to improve its accuracy, as well as alternative implementations for conducting comparisons and metrics related to the specific data mass used in the study. Conversely, the approach based on the Pearson Correlation Coefficient prioritized similarity between manga reviews to generate item-focused recommendations, although it significantly relied on the number of available reviews. This methodology offered a direct and valid logic for personalized recommendations based on relationships derived from reviews. The conclusion highlighted the future possibility of exploring hybrid methods combining the advantages of SVD and the Pearson Correlation Coefficient to achieve more precise and comprehensive recommendations, as well as validating techniques that bring different recommendation approaches for tangible comparison. The utilization of additional data gathered in the generated data mass to enrich the quality of recommendations was suggested, aiming to use more detailed parameters in recommendations, along with the employment of indirect approaches, such as using LLMs to aid in the recommendation process. Finally, the study emphasizes the importance of advancing these recommendation technologies to facilitate readers' lives by assisting in filtering the vast content offered by the industry and the internet.Item Processo de Renovação Generalizado baseado na distribuição Gumbel como modelo de estimativas de ocorrências de ondas de calor para auxiliar no processo de tomada de decisão do cultivo de manga no Sertão de Pernambuco(2023-05-08) Ferraz, Kimbelly Emanuelle Avelino; Cristino, Cláudio Tadeu; http://lattes.cnpq.br/0295290151219369; http://lattes.cnpq.br/2320958356149704Several types of events can harm the planting, harvesting or handling of plants and fruits in agricultural areas, one of them including the event called heat waves, which is characterized as a prolonged and relatively uncommon meteorological phenomenon with extremely high temperatures for the region and persistent for several days or even weeks. Given the importance of agriculture, this work seeks, through the analysis of the maximum temperature data in the Petrolina region, the study of the mango plantation, the Heat Wave event through the 90th percentile, optimization algorithms and the processes of generalized renewal and Gumbel, estimating this event contributing to the farmer’s decision making and optimization of Mango production. The proposed model uses the generalized renewal process based on the Gumbel distribution (GuGRP) to model the time intervals between heat waves, considering that consecutive events are conditionally independent. This model proved to be adherent to model events with a significance level of 0.05 and a P −V alue of 0.28 through the Kolmogorov-Smirnov adherence test on the adequacy data adapted to the GuGRP. The model parameters were estimated by Log-Likelihood using optimization algorithms, also specifically testing the Particle Swarm algorithm.Item Explainable Artificial Intelligence - uma análise dos trade-offs entre desempenho e explicabilidade(2023-08-18) Assis, André Carlos Santos de; Andrade, Ermeson Carneiro de; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/3963132175829207Explainability is essential for users to efficiently understand, trust, and manage computer systems that use artificial intelligence. Thus, as well as assertiveness, understanding how the decision-making process of the models occurred is fundamental. While there are studies that focus on the explainability of artificial intelligence algorithms, it is important to highlight that, as far as we know, none of them have comprehensively analyzed the trade-offs between performance and explainability. In this sense, this research aims to fill this gap by investigating both transparent algorithms, such as Decision Tree and Logistic Regression, and opaque algorithms, such as Random Forest and Support Vector Machine, in order to evaluate the trade-offs between performance and explainability. The results reveal that opaque algorithms have a low explanability and do not perform well regarding response time due to their complexity, but are more assertive. On the other hand, transparent algorithms have a more effective explainability and better performance regarding response time, but in our experiments, we observed that accuracy obtained was lower than the accuracy of opaque models.Item Recomendação de psicólogos por meio de algoritmos de filtragem colaborativa, conteúdo e híbrida(2023-09-14) Gomes Júnior, Augusto Rosário; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863There is a rising number of people diagnosed with mental health disorders such as depression and anxiety, disorders that have been long neglected by science and society. Even so, more and more advances are being made in the ways of treating these people, such as platforms that offer psychological care remotely. However, choosing a psychologist or therapist is not always an easy task, given the large amount of information involved in the choosing process. Based on that, the goal of this article was to develop a psychologist recommender system based on a hybrid model, which should be able to recommend psychologists with expertise that meet the needs of different types of patients. The model showed promising results, where the similarity between the recommended psychologists was consistent and good results were achieved in the evaluation metrics MAE (<0.5) and RMSE (<0.75). It was also possible to mitigate weaknesses from both content and collaborative recommendations.Item Sugestão de livros baseada em algoritmo híbrido de recomendação e grau de interesse recente(2023-05-26) Tavares, Eduardo Brandão; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863With a vast and growing range of books available, choose your next reading can become a complex job amid so many options. In the context of Brazil, where most readers have to choose well which book to buy, due to the low purchasing power of our population, an assertive recommendation has become more valuable. This article presents a book recommendation algorithm based on a hybrid model, which consists of using both techniques related to association rules and techniques that are based on the content of books, aiming to present unknown books that follow the recent interest of the reader. The model managed to reach an accuracy comparable to other models in the RMSE and MAE metrics and delivers recommendations closely related to the last readings of each reader.