Navegando por Assunto "Algoritmos computacionais"

Agora exibindo 1 - 20 de 38

Agrupamento automático de mensagens em fóruns educacionais
(2022-06-08) Pereira, Fabio Mariano Costa Silva Gomes; Mello, Rafael Ferreira Leite de; http://lattes.cnpq.br/6190254569597745; http://lattes.cnpq.br/8040322704977339
Virtuais de Aprendizagem trazem inúmeras vantagens quando a questão é facilitar o acesso a informação. Porém, um problema comum que dificulta o acompanhamento dos professores e, sobretudo, o envio de feedback, devido a maior quantidade de alunos por turma, quando comparado com o ensino presencial, é a sobrecarga de informações. Com intuito de mitigar isto, este artigo realiza agrupamentos utilizando os algoritmos K-Means, K-Medoids, DB Scan e o Aglomerativo em 1652 postagens de 4 fóruns educacionais diferentes de um curso superior a fim de agrupar as mensagens semelhantes para auxiliar o professor, tendo que lidar com uma quantidade menor de informação. Em cada postagem, extrai características e aplica técnicas de PLN, além de utilizar uma representação vetorial para o texto das postagens. Por fim, avalia a qualidade de cada agrupamento utilizando as métrica: coeficiente de silhueta e Davies-Boulding.
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%.
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 mensagens de Commit com IA: uma nova perspectiva para o algoritmo SZZ
(2025-03-17) Souza, Camila Nunes de Paula; Cabral, George Gomes; http://lattes.cnpq.br/8227256452129177; http://lattes.cnpq.br/8347479672060133
Este trabalho propõe uma abordagem inovadora para aprimorar o algoritmo SZZ utilizado na identificação de commits que introduzem defeitos em sistemas de software. A metodologia proposta envolve o uso do ChatGPT, para realizar uma análise semântica das mensagens de commit, classificando-as em duas categorias: ”introduz bug”e ”não introduz bug”. O objetivo é melhorar a confiabilidade das classificações geradas pelo SZZ, reduzindo falsos positivos e melhorando a qualidade dos dados utilizados para a geração de modelos preditivos de detecção de defeitos. Para validar a abordagem, foram realizados experimentos com duas bases de dados (Neutron e Nova), utilizando os classificadores Random Forest e SVC, além de técnicas de balanceamento como oversampling e undersampling. Os resultados demonstram que a integração do ChatGPT ao SZZ resultou em uma redução significativa de commits erroneamente classificados como introdução de bugs, além de melhorar o desempenho dos classificadores, especialmente o Random Forest. Conclui-se que a utilização de LLMs pode aprimorar a eficácia do SZZ, contribuindo para a melhoria da qualidade de software e a eficiência na detecção de defeitos.
Análise de um sistema de recomendação de restaurantes sensível ao contexto sobre o grau de satisfação dos usuários
(2023-09-01) Melo Filho, Carlos Olimpio Rodrigues de; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/6986499479035317
Popular applications of recommender systems can be found in many areas. In the food business, platforms such as TripAdvisor stand out for suggesting specialized restaurant recommendations based on various types of relevant information, such as reviews from other users for the menu, atmosphere and recommendations for the closest restaurants are some of the specialties of these platforms. With the possibility of using new data sensitive to the user’s context, the main objective of this work is to evaluate the usage of the reason of going to the restaurant to reorganize the final restaurants recommendation through a context-based post-filtering. To achieve the goal, a mobile application was developed, the SR Recife Restaurants, to assess the degree of satisfaction of real users to the recommended restaurants, an online evaluation approach, using questionnaires, was used. When carrying out the experiment with 15 users, it was possible to notice an increase of 26.67% in the degree of satisfaction of the top-5 first recommendations when using the trip type to the restaurant as context data for the post-filtering phase.
Uma análise do impacto da experiência prévia com pensamento computacional no desempenho de estudantes em programação no ensino superior
(2019) Silva, Emanuel Leite Oliveira da; Falcão, Taciana Pontual da Rocha; http://lattes.cnpq.br/5706959249737319; http://lattes.cnpq.br/5886730483799524
This paper aims to study the effect of previous contact with Computational Thinking instudents of higher education courses. Computational Thinking is a skill that aims to de-velop logical thinking and algorithmic thinking on an ongoing and lifelong basis, helpingthem to solve personal and professional life problems using the techniques of computer science. According to research, more than 50% of students in computer courses willdrop out of the course and one of the main reasons is the difficulty in learning and as-similating the basic and advanced concepts of programming, becoming unmotivated.Thus, this work investigated the feasibility of using computational thinking to help thosestudents with programming learning difficulties. Therefore, two student profiles wereidentified, who had contact with Computational Thinking before and after attending Pro-gramming, and questionnaires were applied to evaluate the perspectives they had onthedisciplineanditsbenefit,whethertheuseofComputationalThinkingwasproductiveor not. Two teachers from the UFRPE Computer Degree course were also interviewedto examine their perspective on Computational Thinking on student performance, com-paring students who had contact before and after attending Programming. From thestudents’ perspective, the use of Computational Thinking assists them in cognitive de-velopment, improving logical thinking and algorithmic thinking, and programming learn-ing. Teachers believe that Computational Thinking cognitively prepares students forProgramming, reducing the effort to assimilate the basics and seeing this approach asan improvement for students.
Aplicação de técnicas de mineração de dados educacionais para previsão do desempenho de estudantes a partir de dados do ENADE
(2021-03-04) Lima, Thacyo Eslley Burgo de; Silva, Paulo Mello da; http://lattes.cnpq.br/8976586263200932; http://lattes.cnpq.br/7863661692692517
With the increasing growth in data, it is necessary to apply techniques to extract information and knowledge from the set of data that are available, with that comes data mining. This work aims to predict the performance of students in Information System courses from data from the National Student Performance Exam (Enade) held in the year 2017. For this, machine learning algorithms were used to discover knowledge and assist in decision making. Four algorithms were used for comparison that obtained accuracy greater than 60%, showing that it is feasible to make the forecast
Aspect term extraction in aspect-based sentiment analysis
(2019) Francisco, Alesson Delmiro; Lima, Rinaldo José de; http://lattes.cnpq.br/7645118086647340
The increasing use of the Internet in many directions has created a necessity to analyze alarge quantity of data. A large amount of data is presented as Natural Language Text,which is unstructured, with many ways to express the same information. It is an importanttask to extract information and meaning from those unstructured content, such as opinionson products or services. The need to extract and analyze the large amount of data createdevery day on the Internet surpassed the capabilities of human ability, as a result, manytext mining applications that extract and analyze textual data produced by humans areavailable today, one of such kind of applications is Sentiment Analysis, viewed as a vitaltask both to the academic and commercial fields, so that companies and service providerscan use that knowledge extracted from textual documents to better understand how theircustomers think about them or to know how their products and services are appreciated ornot by their customers. However, the task of analysing unstructured text is a difficult one,that is why it is necessary to provide coherent information and concise summaries to thoserevisions. Sentiment Analysis is the process of computationally identifying and categorizingopinions expressed in a piece of text, especially in order to determine the writer’s attitudetowards a particular topic or product. Aspect-Based Sentiment Analysis is a sub-field ofSentiment Analysis that aims to extract more refined and exact opinions, by breakingdown text into aspects. Most of the current work in the literature does not take profitof either semantic-based resources or NLP-based analysis in the preprocessing stage. Tocountermeasure these limitations, a study on these resources is done aiming to extract thefeatures needed to execute the task, and to make the best combination for ATE. This workhas the main goal of implementing and analysing a method of Aspect Term Extraction(ATE) of users reviews (restaurants and laptops). The proposed method is based on asupervised approach called Conditional Random Fields (CRF) which is able to optimizethe use of features for classification, this choice was justified by previous related work thatdemonstrate the effectiveness of CRF for ATE. Also, we are investigating the existingmethods and features for ABSA, as well as proposing new features and experimentingwith feature combinations in order to find the best features combinations, that are not yetcovered in the state of art. The detailed study is done by experimenting with word features,n-grams and custom made features using an CRF supervised algorithm to accomplish thetask of Aspect Term Extraction with results in terms of Precision, Recall and F-measure,the standard evaluation metrics adopted in the field. Finally, a comparative assessmentbetween the proposal method for ATE against other related work presented in the literaturehas shown that the method presented by this work is competitive.
Avaliação de algoritmos de rastreamento no problema de detecção de pessoas no mar
(2023-09-13) Nascimento, Ramicés Moisés do; Macário Filho, Valmir; http://lattes.cnpq.br/4346898674852080; http://lattes.cnpq.br/0247140467691140
It is known that shark attacks are a constant fear for the population on the beaches of Pernambuco. Many of these attacks are fatal, which calls for some action to be taken, considering that Pernambuco beaches attract thousands of tourists each year. Therefore, researchers from UFRPE initiated a study aiming to develop a system for tracking people in the sea, which would make it possible to alert lifeguards when individuals exceed a designated safe area on the beach, as well as allocate a greater number of these professionals in areas with a higher concentration of people. The system was divided into three stages: image segmentation, detection of beachgoers, and tracking of individuals. This work focuses on the third stage. Tracking people is a complex task with high computational costs. Problems such as changes in lighting conditions, alterations in the direction of targets, and variations in the background are just a few of the difficulties that can be mentioned. Thus, the objective of this research is to evaluate six people tracking algorithms found in the literature using beach images. Firstly, a database of ten videos recorded at Boa Viagem beach in Pernambuco was manually labeled. Then, six algorithms were selected for evaluation. Subsequently, the output of each frame provided by the algorithm was compared with the previously labeled data, and an average was calculated. Overall averages were then obtained to assess the algorithm’s accuracy and execution time. Finally, the best algorithm was chosen for optimization using a genetic algorithm, and any improvements in the results were verified. CSRT was the algorithm that obtained the best result and after optimization with the genetic algorithm, an improvement of 20% in its accuracy was obtained.
Avaliação de algoritmos multi-classe para classificação de solicitações enviadas a Ouvidoria Geral do Estado de Pernambuco
(2021-03-29) Carvalho, Luiz Henrique Teixeira; Ferreira, Jeneffer Cristine; http://lattes.cnpq.br/3000364145302421
The Ombudsman’s Office is a public agency that covers the entire state of Pernambuco and every day receives several requests with the most varied themes involving all other organs of the state, with that in certain times of the year, these requests can come to burden the resources of State. The main objective of this work is to apply the multi-class classification algorithms to the data obtained from the transparency portal, and to try to predict requests sent to the Ombudsman’s Office of the State of Pernambuco To obtain data from the Ombudsman’s Office of the State of Pernambuco, data scraping was carried out on the Pernambuco Transparency Portal of Pernambuco. Data for the years 2017, 2018 and 2019 were obtained. The algorithms Decision Tree, Random Forest, Bagging and kNN were applied to the ombudsman data. The results showed that the automatic data classification algorithms, particularly the Decision Tree, Random Forest, Bagging algorithms achieved 55 percent and 32 percent in the type and organ classes respectively, taking advantage of one hit every two attempts in the type class and one hit every three attempts in the organ class. The algorithms were also evaluated about their performance in time of model creation and training, with the Decision Tree algorithm as the most performative.
Avaliação entre algoritmos de filtragem colaborativa baseada em vizinhança e transferência de conhecimento para CD-CARS
(2019) Silva, Guilherme Melo da; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/7122596102314881
Recommendations in scenarios with the lack of preferences expressed by users is an importantlimitation for Recommendation Systems (RS). Due to this problem, cross-domain RS (CDRS)searches have gained relevance, where collaborative filtering (CF) is one of the most exploitedtechniques in this area. The CD-CARS system shows that the use of contextual information,available in user preferences, can optimize CF neighborhood-based algorithms, a techniquewidely used in multidomain CF. Although they provide accurate recommendations, some neigh-borhood-based algorithms such as the one used in the CD-CARS have the limitation of the useof multi-domains only in the occurrence of user overlap between domains, a non-trivial scenarioin real databases. This work presents a comparative analysis of different recommendation algo-rithms involving collaborative filtering techniques. The CD-CARS’ NNUserNgbr-transClosure(CF neighborhood-based) and Tracer (CF transfer learning-based) algorithms, were used as thebasis for the recommendation algorithms. In the experiments, the CF algorithms were integratedinto the context-aware techniques, addressed in the CD-CARS: Contextual Pre-Filtering andPost-Filtering, being applied on two data sets, formed by two auxiliary domains and one target,with and without overlap between domains. The MAE and RMSE performance metrics wereused to evaluate the algorithms. The results of the experiments showed that the Tracer algorithmpresented better results concerning the NNUserNgbr-transClosure algorithm in all experimentscenarios without user overlap, with and without the use of the Contextual Pre-Filtering or Post-Filtering.
Classificação de banhistas na faixa segura de praia
(2018) Silva, Ricardo Luna da; Macário Filho, Valmir; http://lattes.cnpq.br/4346898674852080; http://lattes.cnpq.br/3088880066515750
In order to avoid risks in aquatic environments, drownings and shark attack, beach areas should be constantly monitored. When necessary, rescue workers must respond quickly to the case. This work aims to propose a classification algorithm for people as part of a system for automatic monitoring in beach areas.Certain environmental factors are quitech allenging, such as varying brightness on cloudy days,the position of the sun at different times of the day, difficulty in segmenting images, submerged people, and position away from the camera. For this type of problem in the literature is commonly found, for people detection, the use of image descriptors in conjunction with a classifier. This work performs a beach image study using the following image descriptors and their combinations in pairs: Hu Moments, Zernike Moments, Gabor Filter, Guided Gradient Histogram(HOG),Local Binary Patterns(LBP) and Haar.Inaddition,a dimensionality reduction technique (PCA) is applied for feature selection. The detection rate is evaluated with the following classifiers: text it Random Forest, casca de classifier and textit Support Vector Machine (SVM) with linear and radial textit kernel. The experiments demonstrated that the SVM classifier with radial kernel using the HOG and LBP descriptors applying the PCA technique showed promising results, obtaining 90.31% accuracy
Classificação de imagens de textura geradas por gráficos de recorrências no problema de pessoas sofrendo ataques epiléticos
(2019) Queiroz, Danielly de Moura Borba; Macário Filho, Valmir; http://lattes.cnpq.br/4346898674852080; http://lattes.cnpq.br/7461629772562910
Epilepsy is a neurological condition characterized by the occurrence of epileptic seizuresthat recur in variations. These seizures are clinical manifestations of an abnormal dis-charge of neurons, which are cells that make up the brain. Some features make earlydiagnosis of epilepsy a major challenge, even for the most experienced clinicians. Asmedical aid, there are tests such as electroencephalogram (EEG) represented by timeseries widely used in the diagnosis of epilepsy. Time series are present in various areasof study, such as medicine, biology, economics, among others. Your graphics exposehidden patterns and alter data such as texture patterns as well as those that can beused by texture extraction methods. In addition, there are several tools for extractingtime series information, one of which is the hit image, which is currently used to verifythe change of an unsigned pattern. This paper presents a study of texture descriptorsand classifiers in images of healthy and epileptic people generated by recurrence im-ages. The texture descriptors using this study were: Local Binary Models (LBP), LocalPhase Quantification (LPQ) and Gabor Filter Bank. To the best of our knowledge, nostudy has yet been performed, applying these descriptors to base recurrence imagesused in this work. The evaluation is performed through the average hit, precision, recalland f-measure rate resulting from the following classifiers: textit Random Forest, andtextit Support Vector Machine (SVM). The experiments showed that the SVM classi-fier using the LPQ descriptor showed promising results, obtaining 92.1% hit, recall andf-measure mean and for accuracy obtained 92.26%.
Comparison of recommendation algorithms for user groups: a food-based case study
(2023-04-24) Vasconcelos, Caio Giovanni Pereira; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/4775036700843482
Um currículo de aprendizado por reforço para o cenário “Run to Score with Keeper” do Google Research Football Environment
(2019-12-10) Silva, Jonatan Washington Pereira da; Sampaio, Pablo Azevedo; Macário Filho, Valmir; http://lattes.cnpq.br/4346898674852080; http://lattes.cnpq.br/8865836949700771; http://lattes.cnpq.br/6846637095187550
Reinforcement learning is a group of techniques that allow an agent to interact with a particular environment. Agents observe the state of the environment and perform an action, the action is evaluated through a reward obtained. The agent objective is to maximize this reward. Various issues such as three-dimensional locomotion and electronic games have been addressed by reinforcement learning (KURACH et al., 2019). The Trainament of agents for a soccer game usually has sparse rewards, what slows learning (MATIISEN et al., 2019). One technique that can solve this obstacle is the curriculum learning proposed in (BENGIO et al., 2009). This technique use simplest tasks of the main task and the increase difficult level with the time. In This work we present two curriculum, identified as 5-15-30-50 e 3-10-20-67, for the scenario Run To Score With Keeper of Football Academy. We have shown that curriculums on average achieved better results compared to training only in the main scenario, without curriculum. Curriculum 3-10-20-67 achieved a better result even considering the pattern deviation.
Um currículo de aprendizagem por reforço para recompensas modeladas no Lunar Lander
(2021-07-19) Albuquerque, Renilson da Silva; Sampaio, Pablo Azevedo; http://lattes.cnpq.br/8865836949700771; http://lattes.cnpq.br/3364503614448061
Reinforcement learning is a machine learning paradigm where the agent learns to solve problems interacting with an environment, executing actions in a trial and error sequence. For each action performed, the agent receives a reward from the environment indicating how effective it was in solving the whole problem. The agent’s objective is to maximize the total reward received. However, in some reinforcement learning problems, the agent needs to learn complex tasks receiving uninformative rewards, leading to the credit assignment problem that slows the agent’s training process. Reward shaping and curriculum learning are techniques that can speed up agent training time by separating the problem into smaller tasks to be solved sequentially, applying smaller and informative rewards for each action performed. Lunar Lander is a simplified 2D simulator used as a benchmark for reinforcement learning solutions to the optimization problem on landing control of a lunar module. However, its standard rewards system assigns much more punitive rewards for the use of the engines, not being very constructive for the agent, which can lead to the credit assignment problem. Hence, this work proposes a curriculum using two additional shaped reward models and runs experiments that aim to minimize the Lunar Lander learning time. This work found that both the new models and the curriculum were more effective in training the Lunar Lander agent compared to the standard rewards model.
Desenvolvimento de um algoritmo baseado em lógica fuzzy para segmentação de lesões em imagens de mamografia digital
(2018) Bezerra, Kallebe Felipe Pereira; Cordeiro, Filipe Rolim; http://lattes.cnpq.br/4807739914511076; http://lattes.cnpq.br/3067789764865525
Breast cancer has been a growing problem for women around the world. According to the World Health Organization (WHO), it is the most common type of cancer among women, with increasing participation, making it one of the most fatal types of cancer worldwide. In Brazil, it is the leading cause of cancer death among women, with 59.000 new cases of cancer in 2018, with an incidence of about 59,70 cases per 100,000 women. Several methods of prevention have been developed, but one of the most effective methods for the detection of lesions is the diagnosis through digital mammography. However, the interpretation of mammography can be a difficult task even for a specialist, since the analysis is affected by several factors, such as image quality, radiologist experience and type of lesion. 12% to 30% of breast cancer cases are not detected because of bad mammography interpretation. The main objective of this work is the study and development of a tumor segmentation technique in mammography images using Fuzzy logic. It aims to insert the Fuzzy approach in the algorithm Random Walker, in order to propose a new solution for lesion segmentation. It aims to insert the Fuzzy approach in the algorithm Random Walker, in order to propose a new solution for segmentation of tumors. Finally, this work compare the results with state of the art techniques. The database has 322 mammography images obtained from 161 patients. However, only 57 of the images contain masses. Results showed that the proposed approach of the Random Walker with Fuzzy logic, used for mass segmentation, obtained better results when compared with the classic Walker Random algorithm, besides decreasing the user effort in the algorithm initialization step.
Detecção de anomalias em dados meteorológicos do sertão de Pernambuco utilizando Isolation Forest e DBSCAN
(2022-06-02) Cavalcante, Anderson Rodrigues; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/0155290293799371
Anomalous values are one of the problems present in the Big Data age. Robust techniques are required to manipulate correct and incorrect information that is generated at each time. Using non-supervised machine learning algorithms gives the confidence of good performance in the final results. This research will use meteorological data on air temperature and relative humidity from the Instituto Nacional de Meteorologia, of Petrolina, with DBSCAN (Density Based Spatial Clustering of Application with Noise) and IF (Isolation Forest) implemented to detect anomalies present in the data, since weathering meteorological anomalies may appear through defects, bad sensor configuration and even extreme climate effects.
Estudo de técnicas preditivas para o auxílio a gestores na pandemia de COVID-19
(2022-05-27) França, Eliana Maria Silva de; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/2782168150783950
The main objective of this work is to propose an alternative to exploratory statistical surveys, to support the decision-making of managers, during the confrontation of the COVID-19 pandemic. To this end, a methodology was created, using machine learning to provide a new tool for predicting deaths caused by COVID-19, from open data that contain sanitary, demographic and population characteristics. In such a way that, from this study, an artificial intelligence model can be developed capable of helping to face the COVID-19 pandemic. Of the 3 artificial intelligence algorithms used (Decision Tree, Support Vector Machine and Multilayer Perceptron), the model based on Support Vector Machine showed the best performance, because it has the lowest Mean Absolute Error, a metric used to measure the quality of regression-based artificial intelligence models.
Evaluation of dimensionality reduction and truncation techniques forword embeddings
(2021-03-03) Aoun, Paulo Henrique Calado; Nascimento, André Câmara Alves do; Silva, Adenilton José da; http://lattes.cnpq.br/0314035098884256; http://lattes.cnpq.br/0622594061462533; http://lattes.cnpq.br/1048218441267310
The use of word embeddings is becoming very common in many Natural Language Processing tasks. Most of the time, these require computacional resources that can not be found in most part of the current mobile devices. In this work, we evaluate a combination of numeric truncation and dimensionality reduction strategies in order to obtain smaller vectorial representations without substancial losses in performance.