https://repositorio.ufu.br/handle/123456789/21344 2026-04-06T03:10:49Z 2026-04-06T03:10:49Z https://repositorio.ufu.br/handle/123456789/45984 2025-05-30T06:17:36Z 2025-05-26T00:00:00Z

Title: Cálculo de estadiamento, preditores de risco por meio da comparação de algoritmos de machine learning como estratégia de rastreio à doença renal crônica Abstract: One of the biggest challenges of Modern Medicine is working with the quantity and availability of data, as the decision for a certain treatment or confirmation of diagnosis is supported by various methodological approaches. Among the various existing pathologies that are subject to and relevant to an approach through the use of generative Artificial Intelligence (AI) are Chronic Kidney Diseases (CKD), with multiple causes in the organ or in the Urinary System combined with multiple risk factors previously existing in the patient. Faster and more accurate testing and evaluation of results are extremely necessary. That said, the objective of this research was to apply three supervised Machine Learning (ML) techniques - Decision Tree (DT), Multilayer Perceptron (MLP), Kolmogorov-Arnold Network (KAN) and compare their performances (accuracy, precision, recall, F1 score and ROC-AUC) of predisposing variables and correlated with CKD staging, using the Explainable Artificial Intelligence (XAI) metric. Using 24 variables available in The UCI Machine Learning Repository (2015); with 400 white men, through exploratory and transversal analysis. As a result, it was obtained that the MLP model outperformed the others, achieving the highest average precision (87.2%) and ROC-AUC score (0.98), indicating strong predictive capacity for this effectively studied population, however KAN provided a balance between strong performance and moderate explainability, showing promise for further development as an interpretable model for CKD prediction. This research highlights the importance of considering accuracy and interpretability when selecting models for healthcare applications and positions KAN as a potential XAI model for predicting CKD stages. Future research could focus on improving the interpretability of KAN and exploring its use in other medical datasets.

2025-05-26T00:00:00Z https://repositorio.ufu.br/handle/123456789/44391 2024-12-20T06:20:56Z 2024-12-09T00:00:00Z

Title: Multiplicação de matrizes comprimidas Abstract: This work explores the field of Compressed Linear Algebra, which investigates methods for compressing matrices, enabling algebraic operations to be performed directly on the compressed representations efficiently. An algorithm for matrix compression was implemented, along with a method for multiplying compressed matrices by a right-hand vector, both based on the solution presented in Ferragina et al. (2022). The compression process was carried out in a partitioned manner, dividing the matrices into blocks processed sequentially. The experiments evaluated the impact of the number of blocks on reducing RAM memory usage. The results indicated a significant reduction in memory consumption during both compression and multiplication as the number of blocks used for matrix segmentation increased. Execution time improved during compression and remained nearly constant during multiplication. However, the compression rate experienced a moderate degradation as the number of blocks used to divide each matrix increased. We conclude that the proposed approach is promising for scenarios with limited computational resources, such as embedded devices and IoT.

2024-12-09T00:00:00Z https://repositorio.ufu.br/handle/123456789/33228 2021-11-09T06:18:41Z 2021-11-01T00:00:00Z

Title: Desenvolvimento de uma aplicação web com linguagens funcionais puras Abstract: This work presents the development of a game, called “The Pawn Game”, in Web format, fully developed in pure functional languages. For its implementation, several technologies were used, in order to put their effectiveness to the test. For the creation of the “Front-End” the functional language Elm was used and for the “Back-End”, the Haskell language, also belonging to the group of functional languages. The “Back-End” encompasses the server and an artificial intelligence designed to perform movements for one of the game’s players. For the server, it was necessary to define all the routes and requests it can accepts, using a Haskell framework called Servant. The artificial intelligence was implemented to perform the white player’s moves, using the minimax algorithm.

2021-11-01T00:00:00Z https://repositorio.ufu.br/handle/123456789/31109 2021-01-21T06:18:46Z 2020-12-21T00:00:00Z

Title: Plataforma web para auxílio de treinamento do uso de próteses para indivíduos com amputação de membros superiores Abstract: Amputation of an upper limb is a traumatic process. The rehabilitation period can take months, due to the difficulty of the patient to adapt with the prosthesis. This time, for the patient, is tiring and discouraging, as he will be accompanied by an occupational therapist daily and other occasional professionals. Based on this fact, this work offers a web platform to assist the patient and occupational therapist, who will be reported at work only as "therapist", in the use of the prosthesis during the training period. The web platform has as main functionality, information provided for the therapist to closely monitor the training of several patients, without having to be present for consultation. Both patient and therapist can access the system from any location, whether via smartphone or computer with internet access. An interface is directly used to collect data and send results, data collection is done by technologies adapted to both web and mobile mode. The work classes the tools used, the construction of the system, its mode of use and characteristics.

2020-12-21T00:00:00Z