DSpace Collection:https://repositorio.ufu.br/handle/123456789/187922026-04-08T03:28:45Z2026-04-08T03:28:45ZO uso de contraste na Tomografia Computadorizadahttps://repositorio.ufu.br/handle/123456789/482902026-02-14T06:21:25Z2025-12-17T00:00:00ZTitle: O uso de contraste na Tomografia Computadorizada2025-12-17T00:00:00ZEstudo de séries temporais e suas aplicações em econofísica usando redes neurais artificiaishttps://repositorio.ufu.br/handle/123456789/477972025-12-06T06:26:22Z2025-09-22T00:00:00ZTitle: Estudo de séries temporais e suas aplicações em econofísica usando redes neurais artificiais
Abstract: This work falls within the field of Econophysics and aimed to create and evaluate the
effectiveness of an Artificial Neural Network (ANN) with a Long Short-Term Memory
(LSTM) architecture in forecasting time series of stock prices in the Brazilian capital
market. For this purpose, historical daily data were collected for the assets Petrobras
(PETR4.SA), Vale (VALE3.SA), and Itaú (ITUB4.SA) over the period from 2005 to 2025.
The methodology involved the use of advanced computational models to analyze complex
financial systems and included, as part of the data preprocessing, normalization using
the MinMaxScaler function and structuring the data into 60-day time windows to feed
a deep LSTM model. This model consists of four layers and was regularized using the
Dropout technique (with a rate of 0.2) to prevent overfitting. The results on the test set
showed varied performance: high accuracy was achieved for the banking sector asset (Itaú),
good trend forecasting ability for the oil sector asset (Petrobras), and a quantitatively
significant failure for the mining sector asset (Vale), evidenced by a notable error and a
"cluster"at the end of the training epoch, occurring only for the Vale asset. It is concluded
that, although LSTM networks show great potential for modeling financial time series,
their effectiveness strongly depends on the particular characteristics and stability of each
asset. This reinforces the idea that the financial market is a complex and non-universal
system.2025-09-22T00:00:00ZMétodos biofísicos utilizados para isolamento de toxinas botrópicas com potencial antitumoralhttps://repositorio.ufu.br/handle/123456789/477702025-12-02T06:21:44Z2025-09-23T00:00:00ZTitle: Métodos biofísicos utilizados para isolamento de toxinas botrópicas com potencial antitumoral2025-09-23T00:00:00ZAplicação da técnica de imagens via ressonância magnética no diagnóstico de câncer: uma revisão bibliográficahttps://repositorio.ufu.br/handle/123456789/477562025-11-28T06:19:12Z2025-09-17T00:00:00ZTitle: Aplicação da técnica de imagens via ressonância magnética no diagnóstico de câncer: uma revisão bibliográfica
Abstract: The use of magnetic resonance imaging (MRI) has grown significantly, mainly due to its
effectiveness in diagnosing various pathologies, resulting from its high sensitivity in detecting
morphological and functional alterations in tissues. Unlike other modalities, such as computed
tomography and radiography, MRI does not use ionizing radiation, making it a safe and effective
option, especially for repetitive or follow-up examinations. MRI is particularly useful in the
evaluation of tumors in soft tissues, such as the brain, liver, breast, and prostate, enabling early
detection and accurate disease staging. Furthermore, new techniques, such as functional MRI,
spectroscopy, and diffusion imaging, have expanded its role in oncology. The examination is based
on the use of an extremely strong magnetic field combined with the application of radiofrequency
pulses to generate images with high resolution and detail. During the process, the patient’s body
interacts with this field and the radio waves, producing specific signals that are captured by highly
sensitive coils. These signals are then transmitted to a specialized computerized system, where
they undergo a conversion process: first, they are transformed into electrical signals, then digitized,
and finally processed by advanced software. The result of this complex procedure is the formation
of clear and precise images, which are displayed on the equipment monitor and can be analyzed
by healthcare professionals, significantly assisting in medical diagnosis. Based on the analysis of
scientific studies, this work highlights the relevance of MRI in clinical oncology practice,
demonstrating that its use contributes significantly to therapeutic planning and monitoring of
treatment response. Therefore, MRI is an essential tool in the diagnosis and follow-up of cancer,
with growing potential as new technologies and applications are developed.2025-09-17T00:00:00Z