01.1 - Graduação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/2
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Item Predição do consumo energético de dispositivos LoRa usando aprendizagem de máquina(2024-12-10) Pimentel, Henrique Pablo Pinheiro dos Santos; Araújo, Danilo Ricardo Barbosa de; http://lattes.cnpq.br/2708354422178489; http://lattes.cnpq.br/0078523045227122A Internet das Coisas (IoT) é um conceito em constante evolução que tem conquistado destaque tanto na comunidade acadêmica quanto na indústria. Dentro dela, o consumo energético é um fator fundamental para determinar o tempo de funcionamento dos dispositivos e a frequência necessária para realizar a manutenção deles. Este artigo investiga a aplicação de algoritmos de aprendizado de máquina para predição do consumo energético de dispositivos IoT-LoRa, permitindo estimar a duração da bateria dos dispositivos e sua autonomia. A metodologia considerou a criação de um conjunto de dados a partir de experimentos com placas de desenvolvimento Event stream processing (ESP32), capturando métricas como tempo de hibernação, tipo de conexão e consumo energético. Técnicas de Inteligência Artificial (IA) são então aplicadas para prever o consumo energético com base nessas variáveis. De acordo com os resultados obtidos, a melhor técnica para prever o consumo energético é a Decision Tree, com um coeficiente de determinação superior a 96%. O estudo contribui para processos decisórios que visam selecionar dispositivos IoT considerando a autonomia projetada para as baterias de tais dispositivos.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 Implementação de um sistema mobile colaborativo para acompanhamento do quadro de pacientes com esclerose múltipla por meio de análise de sentimento(2024-10-02) Araujo, Paula Priscila da Cruz; Gouveia, Roberta Macêdo Marques; Tschá, Elizabeth Regina; http://lattes.cnpq.br/9598413463162759; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/0280090820230057The study aims to develop a mobile system to facilitate the monitoring of patients with Multiple Sclerosis (MS), based on the Human-Centered Design (HCD) Toolkit to meet patient needs. The app allows patients to record and track emotions, symptoms, and treatments, offering monthly reports and personalized alerts. For sentiment analysis, the machine learning algorithms XGBoost and Naive Bayes were used, with XGBoost showing better performance, achieving 87.56% accuracy and an F1-Score of 0.876, while Naive Bayes obtained 62.25% accuracy and an F1-Score of 0.524. The results indicate the tool’s effectiveness in emotional and medical monitoring, contributing to an improved quality of life.Item Técnicas preditivas para auxílio no diagnóstico de melanomas via imagens(2024-10-02) Silva Júnior, José Carlos Monte; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964Skin cancer is the most common type of cancer worldwide, divided into two main types: melanoma and non-melanoma. Although rarer, melanoma is the most lethal due to its potential to cause metastasis. Non-invasive methods, such as dermoscopy and the ABCDE rule, have been used to avoid unnecessary surgical procedures and have helped in the identification of lesions, contributing to faster diagnoses. With advances in technology, Artificial Intelligence (AI) has gained prominence, proving to be a promising solution for medical data analysis, especially with the use of Convolutional Neural Networks (CNNs), which can recognize patterns in dermoscopic images and help classify lesions as melanoma or non-melanoma in an automated manner. This project proposes an ensemble of classifiers based on Convolutional Neural Networks to classify dermoscopic images as melanoma or non-melanoma, comparing its performance with validated architectures, such as AlexNet and VGG-16, using Transfer Learning techniques The analyses of Precision, Recall, and F1 Score showed that the ensemble of Convolutional Neural Networks outperformed the models using Transfer Learning techniques, with AlexNet showing better performance than VGG-16. The ensemble of Convolutional Neural Networks demonstrated a greater generalization capability, proving to be promising in capturing relevant features from the images, revealing potential for medical applications, although it still needs refinement to meet clinical standards.Item 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/1399502815770584Sentiment 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.Item Sistema de suporte à criação de modelos de classificação para a previsão de evasão no ensino superior(2024-03-08) Costa, Tarcísio Barbosa da; Alencar, Andrêza Leite de; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6060587704569605; http://lattes.cnpq.br/6560255346406064Student dropout is one of the greatest challenges faced by university degree institutions. In order to mitigate it, those institutions develop moitoring and analysis tools regarding this phenomenon. One of many existing methodologies to do so is the recognition of student characteristics that leads to dropout, and ond of many existing tools is SABIA: a virtual dashboard responsible for supporting evidence-based management allied to concepts like Learning/Academic Analytics and Business Intelligence. This work expands SABIA through a new page able to create user-customizable supervisioned learning models, offering feature analysis from students and predicting their final status based on those features. Information obtained through those models enables the recognition of risk features on student profiles and assists managers on providin guidelines for applying countermeasures against dropout.Item Análise da evasão no ensino superior: predição e prevenção por meio da mineração de dados educacionais(2024-03-05) Ferreira, Rodolfo André Barbosa; Mello, Rafael Ferreira Leite de; http://lattes.cnpq.br/6190254569597745; http://lattes.cnpq.br/2982020271806247Considering that dropout occurs due to abandonment, transfer, or withdrawal from the course; when the student disengages from the institution they are enrolled in or when the student definitively abandons or does not complete higher education, this article seeks to identify methods and automated techniques to assist managers in preventing dropout cases through predictions. To conduct the study, Educational Data Mining (EDM) was used, which applies data mining techniques such as database, statistics, and machine learning in education. Data from 5144 students with characteristics related to course, semester, and demographics were used from the database provided by the Academic Information and Management System (SIGA) of the Federal Rural University of Pernambuco (UFRPE) for the courses of Animal Science, Fisheries Engineering, and Agronomy. The data, except for those containing personal, restricted, and sensitive information, were separated into Academic Characteristics per Semester, General Academic Characteristics, Course-related, Demographic, and Target Characteristics. The study employs the LSTM machine learning algorithm and the SGD and Adam optimizers, exploring different values for the parameters of learning rate, momentum, batch size, and number of epochs.Item Desenvolvimento de um sistema auxiliar para controle de acesso de veículos para a Universidade Federal Rural de Pernambuco(2024-03-08) Izidio, Stefany Vitória da Conceição; Garrozi, Cícero; http://lattes.cnpq.br/0488054917286587; http://lattes.cnpq.br/0642557485551355Currently, vehicle access control to the Federal Rural University of Pernambuco is done manually on paper by university employees. There is also direct release for vehicles that register with the university and receive a specific sticker to use on the windshield. This type of control is not very safe, as it can be easily cloned and used by vehicles without real authorization. Furthermore, there is a short diversion of the employee's attention when he performs the manual work of writing down the sign on paper. This work aims to make the vehicle control process more reliable and safe through the development of a prototype of a system that assists in access control. This work proposes a solution by capturing an image of the license plate, identifying the vehicle plate and checking in a database whether the plate is previously registered or not. And, the system produces a light signal to indicate to the employee whether the license plate is registered or not. To achieve this, a hardware product was assembled and embedded software was developed. The hardware consists of a set of electronic devices such as LEDs, camera, processing device, etc. The software is a set of libraries that were, for the most part, developed in Python. For the embedded software, a set of images with photos of Brazilian car license plates was used to train an object detection model to detect the license plates. Finally, an optical character recognition service was used to extract the content of the plate, thus making it possible to register and emit the light signal to the user.Item Uma metodologia para a avaliação de desempenho e custos do treinamento de redes neurais em ambientes de nuvem(2024-03-07) Moura Filho, Cláudio Márcio de Araújo; Sousa, Érica Teixeira Gomes de; http://lattes.cnpq.br/9899077867723655; http://lattes.cnpq.br/8143173691280119Deep neural networks are solutions to problems involving pattern recognition and several works try to find ways to optimize the performance of these networks. This optimization requires suitable hardware to be implemented, hardware that can be very expensive for small and medium-sized organizations. The objective of this work is to propose a methodology to evaluate the performance and cost of training neural networks, considering the factors that most impact training time and evaluate the total financial cost of the environment for this task. In this sense, it was observed that factors such as the size of the input image and the network architecture have a great impact on the training time metric and consequently on the total cost.Item Prototipação de um sistema de localização utilizando Redes LoRaWAN(2024-03-05) Maia, Pedro Lopes; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/2161981667043569With the proliferation of the use of IoT technologies, efficient solutions in terms of battery usage and applicability for device positioning have become increasingly necessary due to the demand for location-based services. In this context, signal-based localization techniques, such as fingerprinting, represent a very appropriate solution as they meet the requirements of these applications. In this study, a public dataset containing RSSI values from a LoRaWAN network was used to create machine learning models to evaluate their effectiveness in positioning LoRa devices, offering an alternative to GPS, which due to the high power consumption of device batteries, in many cases, is not viable for IoT systems. After evaluating hyperparameters and applying appropriate methodologies for each algorithm studied, a model was obtained capable of making predictions with an average error of 301.34 meters and a median of 164.26 meters.