Bacharelado em Sistemas de Informação (Sede)

URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/12

Siglas das Coleções:

APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso

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Agora exibindo 1 - 20 de 104

Uma abordagem baseada em aprendizado de máquina para dimensionamento de requisitos de software
(2016-12-13) Fernandes Neto, Eça da Rocha; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/6325583065151828
This work proposes to perform the automatic sizing of software requirements using a machine learning approach. The database used is real and was obtained from a company that works with Scrum-based development process and Planning Poker es- timation. During the studies, data pre-processing, classification and selection of best attributes were used along with the term frequency–inverse document frequency algo- rithm (tf-idf) and principal component analysis (PCA). Machine learning and automatic sorting occurred with the use of Support Vector Machines (SVM) based on available data history. The final tests were performed with and without attribute selection by PCA. It is demonstrated that the assertiveness is greater when the best attributes are selected. The final tool can estimate the size of user stories with a generalization of up to 91 %. The results were considered likely to be used in the production environment without any problems to the development team.
Uma abordagem de Game Learning Analytics para identificação de habilidades de leitura e escrita no ensino infantil
(2018) Oliveira Neto, José Rodrigues de; Rodrigues, Rodrigo Lins; Amorim, Américo Nobre Gonçalves Ferreira; http://lattes.cnpq.br/7962263612352589; http://lattes.cnpq.br/5512849006877767; http://lattes.cnpq.br/3879751025550218
The power that video games have to capture their players’ attention has brought with it the idea of using them with the main objective of reinforcing learning in educational context. Recent studies demonstrate that it is possible to analyze the interactions of players in such games, called Serious Games, to conclude and measure the learning obtained during interaction in those games. Given this context, this work aims to develop an analysis of data obtained from the interaction of players in one game, out of 20, applied during a research that proved their positive impact on the development of reading and writing skills of 4-years-old children. Three classifiers were selected: Naive Bayes, Support Vector Machines (SVM) and Logistic Regression, which were trained with the data resulting from the interaction of these players with the game and demonstrated the hit rate of each of the classifiers. In addition, this work also makes an analysis of the interactions considered more relevant by one of the models, finding relationships between the words proposed as challenge in the test and those present in the game, raising reflections that can be taken into account during the development of a educational game that aims to improve children’s reading and writing skills in early childhood education.
Uma abordagem para o suporte ao diagnóstico de melanoma por imagem via comitês de autoencoders
(2021-12-17) Silva, Evele Kelle Lemos; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964
Skin cancer is the most common type of cancer in Brazil, representing about 33% of cases of malignant neoplasms in the country. Melanoma is a type of skin cancer that represents only 3% of cancer cases in the organ, but it is considered the most offensive due to high possibility of metastasis, which is the spread of cancer to other organs. Although melanoma is considered the main fatal skin disease, the introduction of new drugs combined with the detection of the tumor in early stages have contributed to positive prognosis. Through the ABCDEs rule of melanoma, it is possible to identify melanoma by watching some characteristics present in the lesion, however, the identification of melanoma through observation is often a failure, especially when it comes from an inexperienced doctor. Therefore, this work aims to select and use Machine Learning techniques to propose a model that can help dermatologists to identify skin lesions through dermoscopic images, serving as a second opinion to say if it is or it is not melanoma. The proposed model was compared with techniques widely used in the literature for solving complex problems, with the objective of presenting superior performance. Using Precision and Recall, the proposed model proved to be comparable to the others, although it had access to only 0,1% of the dimensions of the image, which indicates that the model worked well on finding the characteristics that discriminate skin lesions.
Ações para diminuição do número de cancelamentos de uma empresa do setor fitness (ESF)
(2024-02-08) Macena, Jean Karlos Clemente de; Monteiro, Cleviton Vinicius Fonsêca; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/1965699603282309
Acompanhamento preventivo e agendamento de pacientes com câncer utilizando programação inteira e matroides
(2023-04-26) Oliveira, Estéfane Paula Bezerra de; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/7916017096302322
Within the context of the growing number of cancer cases globally, optimization models can help physicians and health professionals to ensure the best use of available resources. This paper presents two constrained optimization models, one for the prioritization of patients for preventive follow-up of cervical cancer and the other for the scheduling of oncology patients in treatment, both using integer programming and AMPL. In addition, a performance analysis of a patient scheduling algorithm using matroids is presented. The models were tested on randomly generated instances, resulting in a list of hypothetical priority patients for performing tests, as well as a list of patients scheduled in their respective shifts.
Alagamentos e Inundações - Uso de visualização geométrica para análise de risco associados ao volume de chuva e altura das marés na cidade do Recife
(2022-06-06) Gomes, Igor de Melo Laurentino; Bocanegra, Silvana; Albuquerque, Jones Oliveira de; http://lattes.cnpq.br/1220553574304474; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/3909324432072958
The purpose of this paper is to present a model of a risk diagram related to the occurrence of floods in the city of Recife. Visual artifacts that could be used as information sources and support decisions, disaster prevention and specific actions to mitigate adverse effects caused by rains are the expected results of this paper. To achieve the main goal, the calculating model for COVID-19 propagation risk created by IRRD in association with the Polytechnic University of Catalunya was adapted to the climatology’s theme. The presented results were collected in a day with big rain precipitation in the metropolitan region of Recife, and this was good for evidence capture and to prove the proposed model is applicable indeed.
Um algoritmo para geração de Navigation Meshes em mapas bidimensionais homogêneos: uma aplicação no jogo Dragon Age: Origins
(2019) Costa, Ingrid Danielle Vilela; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/6113606913639280
In the field of electronic gaming and more recently in robotics, autonomous agent soften need to repeatedly solve the problem of searching for the smallest path. This need can eventually consume a lot of resources and demands optimizations to make these searches more efficient. Such optimizations may include improvements in search algorithms, map representation, data structures used. This work presents an optimization for search algorithms based on the reduction of the search space by means of an automatic Navigation Meshes generation algorithm which are networks of walka blemap areas implying in a reduction of the search space and consequently improving the search processing time. The generation of Navigation Meshes is a problem with no consolidated solution. To prove the heuristic, path finding problems were solved on 156 benchmark maps. The path findings were performmed by the A* algorithm and the solutions were compared between the original maps and the optimized ones. An average search space reduction of 97.42% was achieved, with a standard deviation of 0.026and the search had an average marginal reduction in execution time of 46.76%.
Alocação otimizada de horários acadêmicos com disponibilidade restrita de professores usando algoritmos genéticos
(2022-06-01) Demiro, Matheus Paulo dos Santos; Garrozi, Cícero; http://lattes.cnpq.br/0488054917286587; http://lattes.cnpq.br/8926398361586659
The generation of academic timetables is one of the most complex and arduous activities faced by educational institutions at the beginning of each academic period. In most cases, the solution found for this problem, commonly called “timetabling” in the literature, is performed manually, which makes the process very tiring and time-consuming for institutions. This problem is considered a great challenge in combinatorial optimization, due to the wide set of variables and constraints involved, being considered an NP-Complete problem, where there is no possibility of solution through conventional programming methods. This article deals with the use of genetic algorithm techniques to find an optimal solution to the problem of scheduling academic schedules that takes into account the restrictions of the student and the faculty, in order to favor the academic performance of students and adapt to availability from the students . teachers. For this work, it is expected to develop a genetic algorithm that is able to obtain valid results that meet the constraints of the problem in a reasonably considerable time. Technically, it is expected that the algorithm, from a set of input data, processes and returns a solution that has the highest fitness value - the lowest number of infractions committed - between generations of individuals (solutions). This article uses data from the Information Systems course grid at the Federal University of Rio Grande Norte as a base. After modifications in the base and the experiments were carried out, the genetic algorithm proved to be efficient and managed to achieve the objectives, generating adequate academic schedules and compatible with the established restrictions.
Um ambiente para modelagem integrada de energia, custo e disponibilidade de Data Centers
(2021-07-20) Leonardo, Wenderson de Souza; Callou, Gustavo Rau de Almeida; http://lattes.cnpq.br/3146558967986940; http://lattes.cnpq.br/0100793252761254
Sustainability has received increasing attention from the scientific community, with a strong focus on reducing energy consumption and maintaining nonrenewable resources for future generations. In parallel, the expansion of paradigms such as cloud computing, social networking, and e commerce has increased the demand for data centers. In this context, tools that support the modeling of data center architectures and compute metrics such as availability, cost, and energy consumption are extremely important. This project proposes developing a tool with a high level vision for modeling data Center architectures to compute energy consumption, availability, and cost. This tool development uses a graph library to represent the architecture’s components, and from which it will be internally converted to scripts compatible with another tool, called Mercury. The proposed tool will convert the model of this high level view, and through the communication with Mercury, the metrics of interest will be computed by formalism such as RBD(Reliability Block Diagram), EFM (Energy Flow Model) e SPN (Stochastic Petri Nets). Furthermore, optimization algorithms were integrated into the proposed tool. In order to find a combination of components for a given data center architecture in a reduced fraction of time, compared to the brute force algorithm (algorithm that tests all cases), from a preestablished list of components.
An implementation of a mathematical-computational method for the detection and treatment of financial outliers in higher education
(2023-09-06) Freitas, Nathan Cavalcante; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/1613649528791400
The Higher Education Census occurs annually, collecting data from public and private Higher Educational Institutions (HEI) in Brazil. Different factors can lead to anomalies or outliers in some of these collected data. This work proposes a mathematical-computational method to detect and treat atypical HEI’s financial values. Both univariate and bivariate analysis to that end. We analyzed the expenses and incomes of HEI in the census from 2016 to 2019. This analysis revealed that 204 out of 2,224 HEI, approximately 10%, reported some atypical data.
Análise comparativa de ferramentas de testes automatizados de ponta a ponta em ambientes de aplicações web
(2024-03-11) Farias, Guilherme Carneiro de; Monteiro, Cleviton Vinicius Fonsêca; http://lattes.cnpq.br/9362573782715504
In a context where software occupies an increasingly relevant and complex space in society, it is extremely important to enable means for it to be developed with quality. One of these means is automated testing, and in the current scenario, we observe the emergence of a variety of tools in this area, each with its own nuances and unique functionalities. Faced with this diversity of options, this research compares the main end-to-end testing automation tools in web application environments, aiming to facilitate the choice of the most suitable for each project. The theoretical foundation includes concepts of Software Quality, Software Testing, and Architecture and Automation Testing Tools. Three tools were identified and evaluated: Selenium WebDriver, Cypress, and Playwright. The research method is exploratory and descriptive, combining qualitative and quantitative approaches. The results indicate that Playwright presents the best combination of features for end-to-end automated testing in web applications.
Análise da utilização de aprendizado de máquina na redução do volume de alertas benignos
(2019) Simião, Augusto Fernando de Melo; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/0529129636604731
To aid in combating cyber attacks, Managed Security Services Providers (MSSPs) use SIEMs (Security Information and Event Management). SIEMs are able to aggregate, process and correlate vast amounts of events from different systems, alerting security analysts of the existence of threats, such as computer viruses and cyber attacks, in computer networks. However, SIEMs are known for the high rates of benign alertas (non-threatening alerts) warnings relative to malign alerts (threatening alerts). Due to the high volumes and prevalence of benign alertas, the analyst ignores alerts as a whole, which includes those that represent potential threats, thereby increasing the risk of a network compromise. This phenomenon is known as alert fatigue and has been a frequent target of applying machine learning techniques to reduce the volume of benign alerts. Modern SIEMs use machine learning, in correlation of events, so that only alerts that actually represent possible threats are reported. However, this correlation does not consider the analyst’s deliberation, thus allowing SIEMs to continue to generate alerts previously identified as benign. This paper investigates the use of the algorithms Naïve Bayesian Learning, Decision Tree and Random Forest, to reduce the volume of benign alerts using alerts previously identified by analysts, rather than the chain of events that generate such alerts. In this way, it was possible to show, through experiments, that supervised machine learning techniques can be applied in the identification of alerts previously identified as benign.
Análise das dinâmicas de transmissão da Mpox em Pernambuco através do uso de Modelo SEIQR com otimização de parâmetros
(2022-11-23) Pessoa, Wagner Palacio; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/0525335441263931
In recent years, as a result of the COVID-19 pandemic, the importance of the accuracy of the results of studies related to the evolution and propagation of diseases has become evident, so that scientific authorities have enough inputs to make quick decisions in the containment and prevention of epidemics and mitigate their effects on society and the economy as soon as possible. At the end of July 2022, the Mpox (Monkeypox) outbreak was declared a global health emergency by the WHO, accelerating a possible return to the state of alert for a new pandemic. This work aims to analyze the transmission dynamics of this virus in Pernambuco using the SEIQR compartmental epidemiological model (Susceptible, Exposed, Infected, Quarantineed and Recovered), with data available from July 12 to November 3, 2022. The simulations were performed with the Wolfram Language. Experiments were performed with manual adjustment of the model parameters by a graphical interface and also considering the dynamic adjustment over time intervals, using a non-linear optimization function. The results suggest a possible regression in the spread of the virus in the state between mid-December 2022 and January 2023.
Análise de dados coletados para a melhoria de uma suite de testes em um site de e-commerce
(2024-03-08) Lubambo, Manoela Timossi; Monteiro, Cleviton Vinicius Fonsêca; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/5743969633597802
Quality is important in Software Engineering so that systems maintain and meet specified requirements, are reliable, efficient and free from defects. This is guaranteed through a series of standards, practices and processes. As an essential part of the quality assurance process, software testing aims to verify the software’s compliance with established functional and non-functional requirements, such as performance, security, usability, reliability, among others. They are conducted by running the software under controlled conditions, using specific techniques and strategies to detect problems and ensure their correction. One of its diverse approaches is through test automation. In this work, a detailed report is made on the process of automating a test suite, highlighting the challenges faced throughout this process. And, a thorough analysis of the data collected regarding automation is carried out and, through this analysis, we seek identify the root of the problems related to the lack of effectiveness of the automation presented, where possible improvements are identified based on the results obtained, aiming to optimize the effectiveness of the test automation process.
Análise de dados de redes Wi-Fi por meio de redes de correlação
(2021-12-17) Ferreira, Anderson dos Santos; Goncalves, Glauco Estacio; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/6157118581200722
The growing use of Wi-Fi networks has generated a large volume of data that allow studies to analyze human behavior. One of the ways to study these data is through a complex network created from correlation coefficients. Data from the UFRPE Wi-Fi network were collected during 6 days, the creation of time series allowed the analysis of the occupation graphs of the headquarters buildings, thus enabling the creation of complex networks based on correlation coefficients. The occupancy graphs showed the times and buildings with the highest occupancy for each day, while the complex network metrics showed a strong correlation of buildings with different degree values.
Análise de performance de algoritmos estocásticos aplicados ao problema do caixeiro viajante
(2024-10-09) Lima, Lucas Gabriel Oliveira Sales; Monteiro, Cleviton Vinícius Fonsêca; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/7636465842833021
Optimization algorithms are increasingly relevant tools in modern companies because they are capable of optimizing processes and resources, ensuring more efficient results and timely processing for decision making. Comparing these algorithms is a common process during their adoption studies. However, the use of complex methodologies can often lead to the choice of an imprecise algorithm, since its result may not reflect the reality of a company seeking to implement applications with limited resources. In view of this problem, the need arises to evaluate these algorithms from a new perspective. The main objective of this work is to propose a reflection on the way experiments in algorithms are conducted. The present study carried out experiments with optimization algorithms using computational resources similar to those found in most companies, comparing them with another work in which optimizations and tunings were used in these same algorithms. For the experiment, the traveling salesman problem was used, through 15 benchmarking divided into 3 categories, according to the size of each article. Finally, statistical metrics were obtained for the performance of each algorithm, which, when compared to the reference article, showed shorter execution times without compromising the accuracy of the results. Probabilistic algorithms are of great financial importance to companies that need to manage resources quickly, such as airports and shipyards. Therefore, the appropriate choice of parameters provides a more accurate view of reality.
Análise de sentimentos dos tweets relacionados ao Superior Tribunal Federal no ano de 2019
(2022-11-10) Cadengue, Guilherme Lapa de Araújo; Andrade, Ermeson Carneiro de; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/8502533221842320
The Social media since its inception has affected all Internet users. Networks such as Twitter provide a new form of communication, interaction and, above all, a way of expressing opinions about the different events of life in society, consequently enabling the generation of content. Knowing the opinions of Brazilians about public institutions is very important for engaging people in society, as agents participating in decisions that affect all individuals, that is, it is a form of social inclusion. The application of Sentiment Analysis is carried out in several areas in order to extract the content of public opinion. The objective of this work is to identify the feelings of the Brazilian population about the Superior Federal Court of Brazil through the content of published tweets between January and December 2019. For this, the tweets in the period were collected, which were pre-processed, classified and then analyzed. The results show highly polarized opinions, but generally negative opinions regarding the STF are predominant (estimate at 51.7%).
Análise de sentimentos em reviews de jogos digitais da Plataforma Steam
(2024-09-26) Albuquerque, Júlia de Melo; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584
Sentiment analysis is an area that investigates the emotional expressions of human language, aiming to understand the underlying needs and opinions expressed in texts. Its complexity lies in the ability to discern not only the textual content but also the implicit emotional matrices. With technological advancements, the ease of publicly expressing opinions is disseminated through various means, with online gaming being a sector that attracts numerous player posts about various available titles. However, this diversity of audiences and topics makes it challenging to understand the expressed sentiment that pervades this universe. The aim of this study is to apply sentiment analysis techniques to digital game reviews, adopting an approach focused on supervised machine learning algorithms and pre-polarized libraries, in order to identify the best classification path capable of discerning the sentiments expressed by users in the reviews. This operation considers an approach with all opinions and another focused on each game’s specific genre. This analysis was conducted by exploring data from an online game distribution company (Steam), followed by data preparation due to the peculiarities present in the records. The results reveal that machine learning models outperform traditional approaches, such as using the VADER library, showing a higher precision by approximately 10% in captures. A difference of 20% more was observed in metrics such as recall and F1-score. This study represents an analytical contribution to the field of sentiment analysis, highlighting the model’s ability to deal with the complexity of human language.
Análise de sentimentos em Tweets relacionados ao desmatamento da Floresta Amazônica
(2021-12-17) Silva, Vinicius José Paes e; Andrade, Ermeson Carneiro de; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/7437953784606274
The Amazon Forest is being devastated at the fastest pace in recent years. In 2021, the Amazon rainforest registers the largest accumulation of deforestation in 5 years, increasing from 13 thousand km2 between August 2020 and July 2021. An increase of 22% compared to the same period in the previous year, the highest number since 2006. Although many works address the issue of deforestation, none of them focus on analyzing the sentiments of the Brazilian population regarding the issue. This work presents an analysis of the sentiments of the Brazilian population related to the deforestation of the Amazon rainforest through the text mining of Twitter and aims to understand how Brazilian users opine and dialogue about the deforestation of the Amazon rainforest. The results reveal that Brazilian users tend to react to events related to deforestation in the Amazon forest on Twiter and that most users have a negative sentiment about the topic, reaching peaks of approximately 60% of tweets in a given time.
Análise de Sentimos de Tweets Relacionados ao Uso de Máscara Durante a Pandemia da Covid-19 no Brasil
(2022-10-07) Oliveira, Felipe de Araújo Morais Vilar; Andrade, Ermeson Carneiro de; http://lattes.cnpq.br/2466077615273972
The world has recently gone through a global crisis. The COVID-19 pandemic began in a Chinese city called Wuhan in mid-December 2019 and spread across the world, infecting more than 596 million people and causing about 6.68 million fatalities. As the COVID-19 virus has much of its proliferation and contagion through the airways, experts and scholars in the health area recommended that the entire population wear masks in an attempt to stop the number of cases by creating a physical barrier to try to contain the respiratory droplets that serve as a means of spreading the virus. The use of masks in Brazil was adopted at the beginning of April of the year 2020, but its mandatory only started around the end of May of the same year. However, the misinformation about the use of the face mask generated great controversy, doubts and discomfort among the Brazilian population. This work aims to analyze the feeling of the Brazilian population regarding the use of masks as PPE (Personal Protective Equipment) through posts (tweets) taken from Twitter. The results reveal that an average of 89.3% of the tweets related to face masks were neutral. Most of these neutral tweets show the Brazilian population’s discomfort in using masks, but at the same time accepting the need to use them in an attempt to stop the spread of COVID-19.