DSpace Collection:http://repositoriosenaiba.fieb.org.br/handle/fieb/432024-01-21T11:38:05Z2024-01-21T11:38:05ZModelagem computacional aplicada a análise de similaridade e filogenia das proteínasSouza, Luryane Ferreira dehttp://repositoriosenaiba.fieb.org.br/handle/fieb/18292023-10-16T22:45:55Z2023-03-08T00:00:00ZTitle: Modelagem computacional aplicada a análise de similaridade e filogenia das proteínas
Authors: Souza, Luryane Ferreira de
Abstract: With the increasing number of protein sequencing data, the first step in identifying this
macromolecule is to compare proteins to identify their similarities with known proteins.
This similarity analysis can identify evolutionary relationships between the compared spe cies. Similar sequences imply species that share a recent common ancestor. However, the
sequences are not always conserved in evolution, so in this case, a comparison with the
more conserved structure can be an alternative to searching for evolutionary relationships
between proteins with the same function. This work analyzes the similarities of protein
sequences from different species to classify them according to their evolutionary charac teristics using one-dimensional cellular automata to represent each protein as an image.
We calculated the distances between the automata images using the Hamming distance.
This distance measures the similarities between the cellular automata images, and we
use it to analyze species evolutionary relationships. We also propose modeling using the
hydropathy profile difference to analyze the differences that occurred in the structures of
the SARS-CoV-2 variants throughout the COVID-19 pandemic. Our method efficiently
approached species of the same animal class and variants of SARS-CoV-2 that share the
N501Y mutation. Furthermore, using the difference in the hydropathy profile, we can see
that the Omicron variant underwent a significant change in the RBD region that may be
related to the cases of reinfection for this variant.2023-03-08T00:00:00ZAprendizagem profunda para o suporte ao diagnóstico da pneumonia causada por Covid-19 em exames de raio x e tomografia computadorizadaFurtado, Adhvan Novaishttp://repositoriosenaiba.fieb.org.br/handle/fieb/18082023-09-22T23:07:50Z2022-09-23T00:00:00ZTitle: Aprendizagem profunda para o suporte ao diagnóstico da pneumonia causada por Covid-19 em exames de raio x e tomografia computadorizada
Authors: Furtado, Adhvan Novais
Abstract: The exponential spread of COVID-19 around the world has brought important
challenges to public health systems. We learned that in pandemic situations, the high
volume of patients overwhelms the care capacity of primary health care services. It
became clear that in this COVID-19 pandemic, to control the morbidity and mortality of
the disease, it is necessary to quickly identify as many patients as possible with
suspected pneumonia. Imaging tests have been successfully used to identify and
confirm suspected pneumonia associated with COVID-19. COVID-19 patients usually
have abnormal situations on chest images obtained by computed tomography (CT)
and X-ray exams. Although the radiological findings are similar to those found in other
lung diseases, specialists are able to identify images of ground-glass features and
suggest the diagnostic possibility of COVID-19 pneumonia even in its initial phase,
especially in pandemic or epidemic situations. In this context, this work presents the
use of a deep learning algorithm on the images associated with the diagnosis of
COVID-19. Two open source systems were developed, with convolutional neural
networks, capable of performing the identification of images suggestive of COVID-19,
present in chest´s X-ray and CT scans. For the training of neural networks, data were
collected from international public databases as well as data obtained through
partnerships with Santa Izabel hospitals in Salvador, Bahia, Hospital das Clínicas, in
São Paulo, capital, and Medsenior hospital, in Vitória, Espírito Santo. For the
development of the X-ray image classification algorithm, 44,031 exams were used
during training and validation.
The X-ray model obtained a sensitivity of 0.85, specificity of 0.82 and ROC AUC
of 0.93 when tested on a set of 1,158 chest radiographs from a referral hospital. The
computed tomography classification algorithm used a base of 3,000 exams and
innovatively selected the 16 most representative images per exam for training. The
algorithm achieved a sensitivity of 0.89, specificity of 0.90, ROC AUC of 0.97 over a
test base of 414 samples. The two algorithms presented themselves as good options
for evaluating the chest images of COVID-19 patients, making it possible to separate
those with alterations from those with no abnormalities. This is relevant in supporting
the targeting of suspected patients in unassisted regions that lacks more efficient
diagnostic models. We conclude that the use of intelligent algorithms can help in the
identification of abnormal images in X-ray and chest tomography, especially in the
absence of a specialist to report the exam, allowing a quick triage of those patients
with suspected pneumonia who need immediate attention in the healthcare service.2022-09-23T00:00:00ZMétodo para analisar autoria de textos baseado em regras de associação e redes de palavrasSouza Junior, Cleônidas Tavares dehttp://repositoriosenaiba.fieb.org.br/handle/fieb/18072023-09-22T23:07:04Z2022-10-25T00:00:00ZTitle: Método para analisar autoria de textos baseado em regras de associação e redes de palavras
Authors: Souza Junior, Cleônidas Tavares de
Abstract: This thesis deals with methods for authorship analysis and verification. More specifi cally, authorship analysis (AA) of digital texts, in particular, literary works in Portuguese
from Brazil and Portugal. The AA investigates how similar is a work by an unknown
author to a set of works by a known author (LAGUTINA et al., 2019; ROCHA et al.,
2017; VENCKAUSKAS et al., 2015; BOUANANI; KASSOU, 2014; TAMBOLI; PRA SAD, 2013; STAMATATOS, 2009; HOLMES, 1985). Authenticating the real author of
a work is important, it prevents false authorship attributions and prevents, for exam ple, that the notoriety of a writer is used to spread ideas that, originally, are not his.
To analyze the similarities between the works, the AA extracts, organizes and compares
structures that appear in the texts, such as number of letters, length of sentences, re petition of words, etc. (ROCHA, 2019; JAMIL; MUSTAFA, 2018; REXHA et al., 2007;
MARKOV; BAPTISTA; PICHARDO-LAGUNAS, 2017; VOROBEVA, 2016). In AA, we
identified a lack of studies that verify the authorship of a text from combinations of words
(i.e. sets of words that recurrently appear among the sentences of a writer and that do
not appear in the sets of words of other writers). In this sense, this thesis aims to present
a new method for authorship verification. Word combinations are assumed not to happen
randomly; they occur in accordance with the syntactic and semantic knowledge that the
authors have and evidence of their language (CHOMSKY, 2018; CHOMSKY, 1994; MI OTO; SILVA; LOPES, 2007; FRANCHI; NEGRAO; MULLER, 1998). The advantage of
analyzing word combinations lies in discovering patterns related to each author and the
contexts of production and publication of each work. The method proposed in this thesis
extracts word combinations through association rules, consolidates the combinations into
word networks and, from sixteen network metrics, analyzes and infers, in literary works,
the periods of editions, the varieties of language Portuguese used, the literary schools and
the authors. In this sense, this thesis contributes to AA with a working method that, in
addition to verifying authorship, highlights the contexts that, supposedly, an unpublished
text by an author should present.2022-10-25T00:00:00ZAplicação das equações diferenciais fracionárias em problemas de geociências: soluções analíticas usando o método da decomposiçãoPrates, José Humberto de Souzahttp://repositoriosenaiba.fieb.org.br/handle/fieb/18062023-09-22T23:06:21Z2022-02-18T00:00:00ZTitle: Aplicação das equações diferenciais fracionárias em problemas de geociências: soluções analíticas usando o método da decomposição
Authors: Prates, José Humberto de Souza
Abstract: In this work, analytical solutions of fractional differential equations are obtained for three
traditional problems in geophysics: (i) the case of a point source located on the surface injecting
electric current into the ground; (ii) the case of a plane electromagnetic wave, and iii) the case of a
point source releasing atmospheric pollutants. To obtain the analytical solutions of the proposed
problems, the method of decomposition by Laplace (MDL) was used, which provides a solution in
fast convergence series. In particular, for the problem of dispersion of pollutants in the planetary
boundary layer, a solution of the three-dimensional diffusion-advection equation was obtained
using the CDM method in two variables, considering the Caputo derivative (non-local) and the
conformable derivative (local ). The proposed mathematical modeling represents an important
advance in the area, considering that the fractional calculus is the generalization of the traditional
whole order calculus. The 2D transient difusion-advection equation was also semi-analytically
solved. For this solution, the double Laplace transform and the numerical inverse Fixed-Talbot
were used.2022-02-18T00:00:00Z