Mestrado em Ciência da Computação (INF)

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Navegando Mestrado em Ciência da Computação (INF) por Por Orientador "Camilo Junior, Celso Gonçalves"

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    Uma proposta de representação e operadores genéticos para algoritmos evolucionários aplicados no reparo automatizado de software
    (Universidade Federal de Goiás, 2017-08-14) Oliveira, Vinícius Paulo Lopes de; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Soares, Telma Woerle de Lima; Vincezi, Auri Marcelo Rizzo
    Maintenance and software repair are responsible for most of the cost of a software in the course of its life. Software repair through genetic evolution may repair errors and improve software, reducing its high cost. GenProg is a technique that uses this approach and through patches evolution it is capable to fix errors in large and small softwares. A patch composed by low-granularity operations compromise the manipulation of these operations. These operations consist of three subspaces: operation, location of application of the operation and what the operation will apply at the location of the fault (operator, fault and fix, respectively). The recombination and mutation operators applied to a low granulation representation limits the ability of the technique to navigate in search space efficiently. It is proposed the reformulation of the representation, in order to allow greater search capability. Theoretical analysis of the representation showed that the new representation has a greater locality than the original one. Through experimentation, validation and genotypic analysis it is shown that the proposed changes have led to a better performance with respect to the original operators and parameters in terms of efficiency, in the first experiments the operator UnifSingle with memorization was 48.88% more effective than the Original operator and then the operator OPSingle_V2 was 26% more effective than the operator UnifSingle with memorization. Some characteristics of these cross-operators were observed through a genotype distance analysis and their influence on the automatic software reapair problem. The proposed mutation operator shown superior results if compared to original. Combination between operator UniSingle with memorization showed the best efficacy among all combinations of operators and parameters (28.29% superior to the best result of the original GenProg).
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    Um método social-evolucionário para geração de rankings que apoiem a recomendação de eventos
    (Universidade Federal de Goiás, 2014-08-22) Pascoal, Luiz Mário Lustosa; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior, Celso Gonçalves; Rosa, Thierson Couto; Castro, Leandro Nunes de
    With the development of web 2.0, social networks have achieved great space on the internet, with that many users provide information and interests about themselves. There are expert systems that make use of the user’s interests to recommend different products, these systems are known as Recommender Systems. One of the main techniques of a Recommender Systems is the Collaborative Filtering (User-based) which recommends products to users based on what other similar people liked in the past. Therefore, this work presents model approximation of functions that generates rankings, that through a Genetic Algorithm, is able to learn an approximation function composed by different social variables, customized for each Facebook user. The learned function must be able to reproduce a ranking of people (friends) originally created with user’s information, that apply some influence in the user’s decision. As a case study, this work discusses the context of events through information regarding the frequency of participation of some users at several distinct events. Two different approaches on learning and applying the approximation function have been developed. The first approach provides a general model that learns a function in advance and then applies it in a set of test data and the second approach presents an specialist model that learns a specific function for each test scenario. Two proposals for evaluating the ordering created by the learned function, called objective functions A and B, where the results for both objective functions show that it is possible to obtain good solutions with the generalist and the specialist approaches of the proposed method.
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    SentiHealth-Cancer: uma ferramenta de análise de sentimento para ajudar a detectar o humor de pacientes de câncer em uma rede social online
    (Universidade Federal de Goiás, 2016-04-26) Rodrigues, Ramon Gouveia; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior, Celso Gonçalves; Pappa, Gisele Lobo; Rosa, Thierson Couto
    Cancer is a critical disease that affects millions of people and families around the world. In 2012 about 14.1 million new cases of cancer occurred globally. Because of many reasons like the severity of some cases, the side effects of some treatments and death of other patients, cancer patients tend to be affected by serious emotional disorders, like depression. Thus, the use of a behavioral tool that assists the detection of the people mood can contribute to the monitoring of patients and family members during treatment. Therefore, the objective of this work is to develop a Sentiment Analysis tool, named SentiHealth-Cancer (SHC), to assist the detection of the emotional state of people members of Brazilian virtual communities for support cancer patients. We conducted a comparative study of the proposed method and a set of general-purpose Sentiment Analysis tools. For this, we collected 789 messages of 8 Facebook communities and considered 2.574 reviews of volunteers about the real sentiments expressed in these messages. Thus, the performance of the tools were tested in each community, with psychologists and non psychologists reviews and, where possible, with texts in Portuguese and translated into English. The results showed that, overall, the proposed method performance in this work is superior to other tools, both analyzing texts in Portuguese and English. For example, its accuracy (56.64%) analyzing all messages shows a significant increase of 11.78% compared to the greater accuracy (50.67%) presented by other tools.
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    Uma abordagem multi-objetivo do método de fertilização in vitro para os algoritmos NSGA-II e GDE3
    (Universidade Federal de Goiás, 2020-01-28) Sampaio, Sávio Menezes; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior, Celso Gonçalves; Soares, Telma Woerle de Lima; Lima Neto, Fernando Buarque de
    objective problems, especially for complex and multimodal. Due to the balance between its exploration and exploitation capabilities, and its ability to avoid local optimal, we speculate that this method can also improve Multi-Objective Evolutionary Algorithms. In this way, this work proposes the adaptation of the In Vitro Fertilization method to the Multi-Objective approach, with new collection and transfer criteria, as well as its coupling to the Multi-Objective Evolutionary Algorithms NSGA-II, based on Genetic Algorithms, and GDE3, based on Differential Evolution, to create new Multi-Objective Memetic Algorithms: IVF/NSGA-II and IVF/GDE3. We evaluated the efficacy of the proposals by comparing canonic NSGA-II with memetic IVF/NSGA-II, as well as GDE3 with memetic IVF/GDE3, applied to Multi-Objective benchmark ZDT, and to the Multi-Objective problem MOTSP-VENDOR. The results show that the In Vitro Fertilization method adapted to the Multi-Objective approach contributed to the fact that the memetic versions exceeded the canonical versions. The results also indicate that this approach is promising to support MOEAs.
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    Invenire: um método evolucionário para combinar resultados das técnicas de sistemas de recomendação baseado em filtragem colaborativa
    (Universidade Federal de Goiás, 2014-08-20) Silva, Edjalma Queiroz da; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Júnior, Celso Gonçalves; Rosa, Thierson Couto; Yamanaka, Keiji
    Recommendation systems function as a guide, helping users to discover products of interest. There are various techniques and approaches in the literature that enable the generationofrecommendations.Thisisinterestingbecauseitemphasizesthediversityof options;ontheotherhand,itcancausedoubtthesystemdesigneraboutwhichisthebest techniquetouse.Eachoftheseapproacheshasparticularitiesanddependsonthecontext to be applied. Therefore, the decision to choose between the techniques is complex to be done manually. This work proposes an evolutionary approach for combining results of recommendation techniques (Invenire) in order to automate the choice of techniques and get fewer errors in recommendations. To evaluate the proposal, experiments were performed with a dataset from MovieLens and some Collaborative Filtering techniques. The results show that the combining methodology proposed in this paper performs better than any one collaborative filtering technique separately in the context addressed. The improvement varies from 3,6% to 118,99% depending on the technique and the experiment executed.