Bacharelado em Sistemas de Informação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/12
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Item Aprendizagem de máquina para a identificação de clientes propensos à compra em Inbound marketing(2019-07-12) Silva, Bruno Roberto Florentino da; Monteiro, Cleviton Vinicius Fonsêca; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/9362573782715504The most important point for a company should always be the customer and getting new customers is not always an easy strategy. Digital marketing techniques study how to attract new customers to businesses using digital platforms. By virtue of the popularization of these means, the strategies had to be shaped to the new possibilities. With just one click you can reach thousands of individuals, which means many new leads for the company. However, filtering out which of these individuals are really interested in the product or service offered by the company demands a lot of effort from the sales team. This overhead is detrimental in the sense that the company can lose revenue by not targeting the real opportunities. With the aim to minimize this problem, the present work offers a proposal whose objective is the automatic identification of the client achieved through digital marketing strategies. It is proposed the usage of Machine Learning techniques, in particular supervised classification algorithms, namely Decision Tree and Naive Bayes. It was used the Scikit-learn library available for the Python programming language. In addition, it was necessary to apply the SMOTE oversampling algorithm, due to the unbalance of the dataset. In addition, in order to optimize the classification, we used the techniques of attribute selection and model selection with hyperparameters adjustment. Finally, to evaluate the results, we used the confusion matrix, the precision and coverage metrics, and the accuracy and coverage curve. Due to the imbalance of the data, the precision metric did not report good indexes results, with averages of 5.5% of correctness. In addition, the coverage was around 83%. Even with such divergent results among the applied metrics, the present work reached its goal, identifying most of the real opportunities and reporting that using this approach, it would be possible to obtain a reduction of up to 85% in the effort applied by the sales team if they had to call for all the leads. As a consequence, the company may have a cost reduction with the resources applied to obtain new customers, allowing the sales team to find new customers with greater efficiency.Item Técnicas de comitês para a estimação de esforço na correção de software(2019-12-10) Guimarães, Ariana Lima; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/2605671850587343A well-defined planning of a software project, since the early stages, is indispensable to its success, whether the development refers to product’s creation or maintenance. Accordingly to the software life cycle, maintenance is continuously executed after the product’s building and delivery, in parallel to the tests execution by engineers and/or users. In this stage, User Stories and Issue Reports are the first documents to be presented. These documents describe, in natural language, business requirements, error scenarios found, expected corrections and enhancements for the system. Its objectives are, among other things, ranking the activities needed to be accomplish during the project. Therefore, in line with the available resources – human, financial and temporal -, it is possible to estimate the effort that will be necessary in the activities development and generate essential information for an effective and efficient planning. As these documents are written in natural texts, it raises the opportunity to use Natural Language Processing and Machine Learning (ML) to predict software effort. In practice, in the daily life of software factories, it is common to use experts’ and project staff’s opinion to judge the effort required by an activity during Planning Poker sessions. Usually, in this technique, the effort is measured in Story Points, which follow Fibonacci sequence. But this planning model requires the scaling of more resources to be executed. The application of ML causes in a system, after the learning phase, the ability to seize the team experience and replicate it quickly and automatically to estimate the activities effort. Thus, this work covers the ML field, proposing a PV-DM Ensemble approach to extract features of Issue Reports to estimate Story Points, the effort indicator. Compared to the two other approaches of BoW and simple PV-DM, the proposed technique has presented good results, about 80% of f-measure, in a supervised learning SVM classifier. The experiments results proved to be a starting point for further study of PV-DM Ensemble approach and its improvement.Item 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/6325583065151828This 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.Item Comparação de técnicas de classificação para predição de esforço no desenvolvimento de software(2019-01-31) Uehara, Matheus Pitancó de Lima; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/2761038597182432A goal of activity development is critical in software development, and it is critical for the software to be delivered with quality without estimated timeframe. Estimates were taken from the project houses because they were planned in the one-year forecast, although they were facilitated by not being more stringent in the time of development of the activity, while those involving development time tended to be the more assertive in the semester demand more time and more the whole external forecasting process. The work was presented as the learning of auxiliary machines in an automated way in the times in which the improvement of movement diminished the time necessary for its accomplishment. Through the experiments were obtained results that validated the feasibility of the technique used for the extraction of characteristics and classifications in the effort estimate of the textual description of the activities. The values of the classifers range from 31% to 33% of the F-measure.Item Uso da ciência de dados para estudo de falhas e fraudes dos abastecimentos de postos de gasolina(2019-12-19) Arruda, Luiz Felipe Ribeiro de; Albuquerque Júnior, Gabriel Alves de; Roullier, Ana; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/1825682578554550Item Programinó: um jogo para auxílio ao aprendizado do assunto de tipos de dados na programação(2019-12-13) Nascimento, Gabriele Pessoa do; Falcão, Taciana Pontual da Rocha; Sampaio, Pablo Azevedo; http://lattes.cnpq.br/8865836949700771; http://lattes.cnpq.br/5706959249737319; http://lattes.cnpq.br/9978319013197863The digital age we live in means that we are always immersed in ever more ubiquitous technologies. For this contact with technology to remain healthy, it is necessary to learn to consume it consciously, and to learn to develop it in different contexts; therefore, we will have increasingly inclusive solutions. Regarding solution development, as much as we have several facilitating artifacts, the process of programming teaching and learning is still a challenge, especially for beginning students. Dealing with so many competing and constant stimuli and still having the ability to abstract and assimilate programming concepts that is not trivial and has not been worked on since childhood, so playful artifacts such as digital games are essential to facilitate first contacts with the schedule. In this context, this work brings to society a digital educational game that deals with the subject of data types in programming, Programinó, so that beginning students can practice and consolidate the content through a playful tool. The game was developed with three levels of difficulty, one easy, one medium and one hard. The hard one applies the adapted minimax algorithm, while the easy one uses the same minimax adapted in an inverted way. The middle level uses a random algorithm. As a way to validate the difficulty levels, comparative experiments were performed that showed that the minimax lost only 5.6% of the time; winning at 49.7% or drawing in the remaining matches.Item Comparação de algoritmos de reconhecimento de gestos aplicados à sinais estáticos de Libras(2019-07-12) Cruz, Lisandra Sousa da; Cordeiro, Filipe Rolim; Macário Filho, Valmir; http://lattes.cnpq.br/4346898674852080; http://lattes.cnpq.br/4807739914511076; http://lattes.cnpq.br/2111589326272463Brazilian Sign Language (BSL) has been created in order to cope with a necessity of a non-verbal communication for the deafs, which during a long time were indoctrinated to learn the Brazilian Portuguese as their first language. Nowadays, the BSL is the Brazil’s second official language and first deaf’s language, as well as the Portuguese for the listener. Nevertheless, even with large recognition, the Brazil’s second official language is not known by the majority of the Brazilian population. The inclusion process aims to allow equality for the impaired, such that the deficiency does not become an impediment factor for living together in society. With the technology arrival and the Artificial Inteligence (AI) advances, it was created technologic artifices to allow inclusion. In the AI, the pattern recognition is one of more approached subthemes in the present, and it is widely applied for the gesture classification of many sign languages in literature. This research has, as key task, the identification of the hands that form a certain BSL gesture and, thus, the recognition of the class it belongs to. Based on American Sign Language (ASL) classification, the Feature Fusion-based Convolutional Neural Network (FFCNN), an extended network from Convolutional Neural Network (CNN), obtained the best accuracy in comparison to other networks, such as Visual Geometry Group (VGG). Therefore, based on this scenario, this work applies the FFCNN to BSL static gestures to verify whether the FFCNN obtain the best accuracy as well as obtained in ASL or not. In order to achieve the goal, this work compares three classifiers: the Visual Geometry Group (VGG), a CNN with variation of 13 and 16 layers, the FFCNN, and a Multi Layer Perceptron network used in recognition of BSL static gestures in literature. The algorithms were applied in a BSL dataset with 9,600 images of 40 signals. The results demonstrate that VGG with 16 layers obtained the best accuracy regarding the described models in this work, corresponding to 99,45%.Item Identificação de Outliers para detectar riscos de gestão(2018-08-17) Brizeno, Raissa Costa; Monteiro, Cleviton Vinicius Fonsêca; Lima, Rinaldo José de; http://lattes.cnpq.br/7645118086647340; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/1672154276438369Outliers are values that doesn’t converge with the rest of the data series. These values when they arise in financial context can represent problems that have a direct influence on the health of an enterprise and the decision-making by the managers. In view of this, it was intended with this work identify anomalies in financial launches arising from the accounts of real companies. For this, statistical analyzes of the launches were fulfilled in order that outliers detection techniques could be chosen and then compared with the outliers detection of evaluators . Among the great variety of techniques were chosen the methods of Boxplot, Boxplot adjusted, MAD and standard deviation. The results show that most of the series didn’t follow a normal distribution, and the experimental results of the comparisons between the automatic methods and the evaluators showed substantial differences.Item Serviço computacional para interpolação espacial de dados meteorológicos(2019) Antonio, Wellington Luiz; Gonçalves, Glauco Estácio; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/6157118581200722; http://lattes.cnpq.br/6454060359445906The spatial interpolation is an essential technique for several fields, such as meteorology, hydrology, agricultural zoning, characterization of health risk areas, sociodemo-graphic, among others. Through interpolation, it is possible to model a surface of a spatially distributed variable from a finite set of known data points. In the case of weather data for agriculture, for instance, interpolation allows us to observe how weather variables behave on a given rural property, which could serve as input for irrigation management on this property. Due to the increasing demand for the use of spatial interpolation,this work proposed the development of a scalable service based on technologies and standards of state-of-the-art in distributed systems, for spatial interpolation of meteorological data associated with agriculture. In order to achieve this goal, we developed a web service based on three different reference evapotranspiration interpolation algorithms, namely: Inverse distance weighted (IDW), Ordinary Kriging (OK) and RandomForest (RF). The first two are widely used algorithms in the spatialization of reference evapotranspiration and they are known to produce low interpolation errors. The third algorithm is originated from Machine Learning. It has been used in recent studies as an alternative for spatial interpolation of environmental variables. This last algorithmhas also been obtaining promising results in the estimation of evapotranspiration. The spatial interpolation web service proposed was developed and its performace was evaluated through measurement. This service was deployed on a production enviromentusing Docker container and a mobile application was developed to integrate and show the main functionalities of the web service. The developed service can be applied inseveral areas. However, in this work more attention was paid to the agricultural sector,as this one is the sector to which this study is focused on. The main beneficiaries of the web service are researchers and developers, which, in turn, are able to develop studies that will benefit the farmer from the application of the service. During this work,we also sought to evaluate how the developed service could be useful for the promotion of the performance and the scalability with respect to spatial interpolation calculus and generation of spatial models. We also highlighted the importance of this software as a support tool for other researches or even for other software, such as Aquaprev, which uses, among other parameters, evapotranspiration and spatial interpolation to estimate the irrigation time of a given crop.Item Estudo comparativo de algoritmos de classificação supervisionada para classificação de polaridade em análise de sentimentos(2019) Albuquerque, Rotsen Diego Rodrigues de; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6441716676783585The huge increase of data on the Internet, it is a rich source for public opinion assessment of a specific subject. Consequently, the number of opinions available makes decision-making impossible if it is necessary to read and analyze all opinions. Since the use of Machine Learning has been widely used, I will present a comparative study of two algorithms for classifying movie comments using techniques of natural language processing and Sentiment Analysis. The data obtained were obtained manually where through the competition site called Kaggle where we have about 50,000 comments on various films. The purpose of this study is also to use the concepts of data science and Machine Learning, natural language processing and sentiment analysis to add more information about the entertainment and film industry. That is why these algorithms were created so that it is possible to show the results for this domain in the of movies comments registered in one big site/platform of movie industry, the famous IMDB. After training and testing, the machine had an accuracy of 86 % on predicting sentiments on commented text from movies.
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