Brazilian Journal of Applied Computing
type: channel
description: The Brazilian Journal of Applied Computing (RBCA) is edited under the responsibility of the Graduate Program in Applied Computing (PPGCA) at the University of Passo Fundo and published by Editora UPF. It aims to disseminate national and international scientific productions in the areas of computing applied to agriculture, health, engineering, and other fields that require computing as a tool to solve their problems, also serving as an environment that fosters the exchange of experiences and ideas among researchers working in these application areas. We inform you that new submissions to RBCA can be made starting from March 2026, based on new submission guidelines. We emphasize that the submissions already made in 2025 are in the normal evaluation process for the next two editions of RBCA. Upcoming editions: v.18, n.1 (April/2026), v.18, n.2 (July/2026), and v.18, n.3 (November/2026)
publisher: UPF Editora
language: pt-BR
publication name: Brazilian Journal of Applied Computing
issn: 2176-6649
copyright: Todos os artigos estão licenciados com a licença Creative Commons Atribuição-NãoComercial-SemDerivações 4.0 Internacional. Autores que publicam nesta revista concordam com os seguintes termos: a) Autores mantém os direitos autorais e concedem Ã revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista. b) Autores têm autorização para assumir contratos adicionais separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista. c) Autores têm permissão e são estimulados a publicar e distribuir seu trabalho online (ex.: em repositórios institucionais ou na sua página pessoal) a qualquer ponto antes ou durante o processo editorial, já que isso pode gerar alterações produtivas, bem como aumentar o impacto e a citação do trabalho publicado (Veja O Efeito do Acesso Livre).
items: Performance Analysis of Ethernet Networks Through Quality of Service (QoS) Metrics Using Real and Virtual Machines
items: Explaining the black-box or using the black-box to develop better interpretable solutions?
items: Comparative analysis of the Support Vector Machine and Convolutional Neural Network algorithms applied in classifying Acer Palmatum plant subspecies
items: Advances in scientific article recommendation systems: A systematic literature review
items: Estimating Roadside Vegetation Height for Maintenance Decision-Making
items: A methodology for assessing UX dimensions in standardized questionnaires
items: A Systematic Review about Large Language Models (LLMs) applied to Code Generation
items: Automatic Identification of Electrical Appliances in Domestic Settings
items: Performance Analysis of Routing Algorithms based on V2V Topology using NS-3 Simulations
items: Efficient generation of high-quality initial solutions for the job sequencing and tool switching problem
items: Application of machine learning strategies in the detection of invasive plants in pastures
items: CNOPsolution Tester: a web application for result validation in constrained optimization problems
items: AMIPDI: architecture model to identify pests in soybean defoliation images using convolutional neural networks
items: Benchmarking Machine Learning Algorithms in Fake Reviews Detection in Brazilian Portuguese
items: Transfer Learning and Data Augmentation Analysis for Computer Vision in Robot Football using Deep Learning
items: Semiotic Evaluation of the Communicability of a Collaborative Libras Dictionary
items: Multi-queue Round Robin Scheduling for Enhanced Performance in Integration Platforms
items: On the Use of Fireworks Algorithm and Particle Swarm Optimization for Watermarking in Image Vector Quantization
items: Comparative study between neuromorphic implementations using Spiking Neural Network connected by Network-on-Chip
items: A Coloured Petri Nets Model of the 5G Authentication and Key Agreement Protocol
items: Evaluation of Routing Protocols in Mobile Ad-Hoc Networks Applied to Military Scenarios: Challenges and ResearchGuidelines
items: A process for source code unstructured search
items: Comparative analysis of PID control and MPC in CartPole and LunarLander environments
items: Recognition of Brazilian Banknotes Using Artificial Intelligence to Assist Visually Impaired Individuals
items: A systematic literature mapping on the security of 5G and vertical applications
items: Pothole Detection Web App: an approach for detecting potholes in asphalt pavements using YOLO
items: MQTT Poller: an endogenous framework for large-scale MQTT protocol performance evaluation
items: Preventing flow rules injection attacks in SDNs using Blockchains
items: R2SC: RBAC system smart contract-based
items: Software Development Models in Game Development: a Literature Review

Performance Analysis of Ethernet Networks Through Quality of Service (QoS) Metrics Using Real and Virtual Machines
type: item
description: Quality of Service (QoS) encompasses technologies used in computer networks to ensure reliable application performance. Consequently, the main QoS performance metrics are throughput, delay, jitter, and packet loss rate. One problem regarding networks is finding an ideal environment that guarantees the minimum QoS requirements, especially in home office. Therefore, this work analyzed the performance of a computer network in two contexts, with and without virtual machines. Thus, Iperf3 was used to generate traffic in two ways: originating from an Iperf3 client on a real machine; and originating from an Iperf3 client installed on a virtualized OS. The tests used UDP connections to two Iperf3 servers: local (LAN) and remote (WAN). The goal of this was to differentiate services that require high throughput from those that require minimal delay. The QoS metrics were throughput, delay, jitter, and packet loss rate. The results highlighted the importance of minimizing packet loss to ensure efficient communication, especially in services such as e-mail delivery and file transfer.
subject: Original Paper
  topic: section
subject: Iperf3
  topic: keywords
subject: Network Performance Metrics
  topic: keywords
subject: Quality of Service (QoS)
  topic: keywords
subject: Avaliação de Desempenho de Redes de Computadores
  topic: subjects
subject: Redes e Sistemas Distribuídos
  topic: disciplines
subject: Ciências Exatas e da Terra
  topic: disciplines
creator: Diones da Silva Rodrigues
creator: Diógenes Antonio Marques José
rights: Copyright (c) 2025 Diones da Silva Rodrigues, Diógenes José http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 64
ending page: 77
doi: 10.5335/rbca.v17i2.16481

Explaining the black-box or using the black-box to develop better interpretable solutions?
type: item
description: Understanding the decision-making processes behind Artificial Intelligence models became a crucial aspect of AI. This paper describes a study that compares the performance of models produced by both interpretable and black-box algorithms and evaluates if it is possible to use black-box models to assist in interpretable models' training. We verified a significant difference in performance between the two types of models. However, the interpretable model was able to mimic the behavior of the black-box models to a satisfactory degree. The promising initial results obtained from using black-box models to aid in interpretable models' training suggest the potential efficacy of this approach.
subject: Original Paper
  topic: section
subject: Black Box Models
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Interpretable Models
  topic: keywords
subject: Post-hoc Interpretability
  topic: keywords
subject: Artificial Intelligence
  topic: subjects
subject: Explainable AI
  topic: subjects
subject: Computer Science
  topic: disciplines
creator: Vinicius Alves Matias
creator: Julia Machado Lechi
creator: Norton Trevisan Roman
creator: Luciano Antonio Digiampietri
rights: Copyright (c) 2025 Vinicius Alves Matias, Julia Machado Lechi, Norton Trevisan Roman, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 46
ending page: 53
doi: 10.5335/rbca.v17i2.16459

Comparative analysis of the Support Vector Machine and Convolutional Neural Network algorithms applied in classifying Acer Palmatum plant subspecies
type: item
description: Background: Plant identification is an essential task as it provides valuable information about plant characteristics and helps determine the population and distribution of species. This paper presents an artificial intelligence solution developed to automatically classify subspecies of the plant species Acer palmatum based on images. A database containing subspecies of Acer palmatum was created, and supervised learning algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were used to classify them. The experiments included different scenarios, such as original images, feature extraction, and data augmentation techniques. Results: The results showed that the CNN with transfer learning and data augmentation performed best, standing out as the best model tested regardless of the dataset evaluated. Conclusions: These findings suggest that advanced Machine Learning techniques can be highly effective in classifying subspecies of Acer palmatum, providing a valuable biodiversity monitoring and mapping tool.
subject: Original Paper
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: Plant species classificaton
  topic: subjects
subject: Machine Learning
  topic: subjects
subject: Computer Vision
  topic: subjects
subject: Computer Science
  topic: disciplines
subject: Machine Learning
  topic: disciplines
subject: Computer Vision
  topic: disciplines
creator: Patrick de Souza Sagioratto
creator: Rúbia Eliza de Oliveira Schultz Ascari
creator: Dalcimar Casanova
rights: Copyright (c) 2025 Patrick de Souza Sagioratto, Rúbia Eliza de Oliveira Schultz Ascari, Dalcimar Casanova http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 23
ending page: 40
doi: 10.5335/rbca.v17i3.16412

Advances in scientific article recommendation systems: A systematic literature review
type: item
description: The abundance of available articles highlights the growing relevance of recommendation systems. This study conducted a systematic review of the literature with the aim of identifying the state of the art in scientific article recommendation systems. Approaches were identified that use embeddings to capture semantic similarity and probabilistic matrix factorization to integrate relationships between articles and authors. The results indicate a predominance of hybrid models, using LLMs and time-aware modeling. It is concluded that, although there have been significant advances, gaps remain in the standardization of metrics and reproducibility of experiments, as well as opportunities for the development of contextually customized models.
subject: Original Paper
  topic: section
subject: Recommendation Systems
  topic: keywords
subject: Scientific Articles
  topic: keywords
subject: Temporal Context
  topic: keywords
subject: OpenAlex
  topic: keywords
creator: Bruno de Santana Braga Contreras
creator: Luciano Antonio Digiampietri
rights: Copyright (c) 2025 Bruno de Santana Braga Contreras, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-18
publication date: 2025-08-18
starting page: 78
ending page: 86
doi: 10.5335/rbca.v17i2.16376

Estimating Roadside Vegetation Height for Maintenance Decision-Making
type: item
description: The upkeep of roadside vegetation is essential for ensuring the safety of both motorists and pedestrians. However, identifying the necessity for such maintenance is frequently a time-consuming and costly process, with a high potential for errors in annotation. This work therefore proposes the development of a solution capable of estimating the height of vegetation along roadsides in an automated manner. A machine learning model was developed and evaluated using a dataset of manually annotated data. The model employs a convolutional neural network architecture, adapted for the task of classifying vegetation heights. The results demonstrate that the model is able to detect the different vegetation height classes with low error rates, indicating its potential for automating the decision-making process for mowing vegetation. This study contributes to the advancement of road monitoring techniques, providing greater operational efficiency and reducing costs for road maintenance.
subject: Original Paper
  topic: section
subject: Convolutional neural networks
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Road maintenance
  topic: keywords
subject: Road monitoring
  topic: keywords
creator: Mateus Reis Santos
creator: Bruna Rocha Aguiar
creator: Enzo Rigazzo Oliveira
creator: Thiago Meirelles Ventura
creator: Raoni Florentino da Silva Teixeira
rights: Copyright (c) 2025 Mateus Reis Santos, Bruna Rocha Aguiar, Enzo Rigazzo Oliveira , Thiago Meirelles Ventura, Raoni Florentino da Silva Teixeira http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 14
ending page: 22
doi: 10.5335/rbca.v17i3.16358

A methodology for assessing UX dimensions in standardized questionnaires
type: item
description: User Experience (UX) encompasses many and diverse variables, which contributes to making its evaluation a complex task. Self-evaluation through questionnaires is one of the most commonly used ways to evaluate UX, both in isolation and combined with other instruments or techniques, and there are currently many standardized and validated questionnaires that can be readily applied. Our work suggests a set of dimensions to describe different aspects of UX, and then proposes a methodology to quantify and compare how much different questionnaires emphasize each of these dimensions. We then select a set of ten questionnaires to exemplify the use of our methodology by comparing them in a particular case study. The results show that all selected questionnaires focus on only a few of the dimensions. Our research reinforces the importance of knowing different instruments and being able to compare them, choosing the one best suited to the context of use.
subject: Original Paper
  topic: section
subject: Evaluation
  topic: keywords
subject: Hedonic Criteria
  topic: keywords
subject: Questionnaire
  topic: keywords
subject: User Experience
  topic: keywords
creator: Erico Souza Veriscimo
creator: João Luiz Bernardes Júnior
creator: Luciano Antonio Digiampietri
rights: Copyright (c) 2025 Erico Souza Veriscimo, João Luiz Bernardes Júnior, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 34
ending page: 45
doi: 10.5335/rbca.v17i2.16323

A Systematic Review about Large Language Models (LLMs) applied to Code Generation
type: item
description: Large Language Models (LLMs) for code generation represent a significant advancement in software development by boosting productivity, simplifying repetitive tasks, enabling automated testing, and promoting best practices. This paper presents a systematic literature review (SLR) of studies focused on LLMs applied to code generation. The review enhances understanding of the capabilities and limitations of these models, outlining both their benefits and challenges. The review protocol included a search on the Google Scholar database using relevant keywords related to LLMs and code generation. A total of 112 articles were initially retrieved, from which 15 were selected based on relevance and quality criteria. Of these, 8 were analyzed in depth to evaluate various approaches and outcomes, while the remaining 7 provided the theoretical foundation for the study. This work contributes to the growing body of knowledge in the field and supports future research and applications of LLMs in software engineering.
subject: Original Paper
  topic: section
subject: Fuzzing
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: AI in Software Engineering
  topic: keywords
subject: Automatic Code Synthesis
  topic: keywords
subject: Transformer Models
  topic: keywords
subject: Large Language Models (LLMs)
  topic: subjects
subject: Automation of code creation
  topic: subjects
subject: Software development
  topic: subjects
subject: Efficiency and error reduction
  topic: subjects
subject: Technical challenges
  topic: subjects
subject: Study analysis
  topic: subjects
subject: Opportunities for future advancements
  topic: subjects
subject: Artificial Intelligence and Software Engineering
  topic: subjects
subject: Computer Science
  topic: disciplines
subject: Artificial Intelligence
  topic: disciplines
subject: Software Engineering
  topic: disciplines
creator: Fabio Alecsandro Bacin
creator: Braulio Adriano de Mello
creator: Giancarlo Dondoni Salton
creator: Samuel da Silva Feitosa
rights: Copyright (c) 2025 Fabio Alecsandro Bacin, Braulio Adriano de Mello, Giancarlo Dondoni Salton, Samuel da Silva Feitosa http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 1
ending page: 13
doi: 10.5335/rbca.v17i3.16310

Automatic Identification of Electrical Appliances in Domestic Settings
type: item
description: The Internet of Things (IoT) is pivotal for automation, monitoring, and control in modern environments. However, effectively managing numerous electrical devices within these settings presents significant challenges. A critical gap in understanding the operational status and power consumption of these devices often results in energy waste and can shorten their lifespan. This paper focuses on the automatic identification of electrical devices in IoT environments, leveraging features extracted from the frequency domain of electrical current signals. We collected power consumption measurements from household appliances and fed them to machine learning algorithms. Our results highlight the efficacy of the k-Nearest Neighbors classifier, which achieved an impressive F1-score of 99% in device identification.
subject: Original Paper
  topic: section
subject: Machine learning
  topic: keywords
subject: Classification
  topic: keywords
subject: Load signature
  topic: keywords
subject: Internet of things
  topic: keywords
creator: Antonio D. T. Amurim
creator: Elvis M. G. Stancanelli
rights: Copyright (c) 2025 Antonio D. T. Amurim, Elvis M. G. Stancanelli http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-23
publication date: 2025-08-23
starting page: 87
ending page: 102
doi: 10.5335/rbca.v17i2.16305

Performance Analysis of Routing Algorithms based on V2V Topology using NS-3 Simulations
type: item
description: Vehicular Ad-Hoc Network (VANET) has a great potential to provide security and entertainment applications for ITS (Intelligent Transport System), and to improve road traffic safety. Vehicle-to-Vehicle (V2V) communication is essential for the vehicular automation process and is used in places where there is no Road Side Unit (RSU) structure implemented, and also to complement V2X communication, as it helps to reduce the load traffic in V2X with high density of vehicles. Thus, this work proposes the use of the Multi-Table Ad-Hoc On-Demand Distance Vector (MT-AODV) routing protocol on VANET/V2V networks to overcome the recurring obstacle of communication degradation in networks with high vehicle density and communication failures.
subject: Original Paper
  topic: section
subject: MT-AODV
  topic: keywords
subject: routing protocol
  topic: keywords
subject: VANET
  topic: keywords
subject: V2V
  topic: keywords
subject: V2V
  topic: subjects
subject: VANET
  topic: subjects
subject: Redes Ad Hoc
  topic: subjects
subject: Redes sem Fio
  topic: subjects
subject: Redes de Computadores
  topic: disciplines
subject: Redes Móveis
  topic: disciplines
subject: Redes sem Fio
  topic: disciplines
creator: Carlos P. Alves
creator: Natália C. Fernandes
creator: Tadeu N. Ferreira
rights: Copyright (c) 2025 Carlos P. Alves, Natália C. Fernandes, Tadeu N. Ferreira http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 54
ending page: 63
doi: 10.5335/rbca.v17i2.16395

Efficient generation of high-quality initial solutions for the job sequencing and tool switching problem
type: item
description: As an optimization problem, the job sequencing and tool switching problem has been the subject of several studies in operations research on its different variations, emphasizing its academic and industrial relevance. Although current methods approaching this problem yield extremely high-quality solutions, the computational time required has proven prohibitive when considering the practical aspects of the problem. Thus, in this paper, a method is presented for generating valid, high-quality solutions in low computational time, which can be used as initial solutions by more robust methods, aiming to accelerate them and contribute to the final quality of the solutions. The proposed approach consists of a new implementation of the random variable-neighborhood descent method using traditional and tailored local searches. Five traveling salesman problem heuristics were considered to generate the initial exploration point for the proposed method. The results obtained were compared with a recent strategy in the literature for generating initial solutions, which demonstrated significant improvement. Additionally, the proposed method was compared to the current state-of-the-art method for the addressed problem, and an average gap of only 5.36% was reported, evidencing the high quality of the solutions achieved for the proposed objective.
subject: Original Paper
  topic: section
subject: Combinatorial optimization
  topic: keywords
subject: Constructive heuristics
  topic: keywords
subject: Flexible manufacturing
  topic: keywords
subject: Tool switching
  topic: keywords
subject: Random Variable Neighborhood Descent
  topic: keywords
creator: Clark Cerqueira Engelhardt Veronez
creator: Leonardo Cabral da Rocha Soares
rights: Copyright (c) 2025 Clark Cerqueira Engelhardt Veronez, Leonardo Cabral da Rocha Soares http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 64
ending page: 73
doi: 10.5335/rbca.v17i3.16997

Application of machine learning strategies in the detection of invasive plants in pastures
type: item
description: The presence of invasive plants on pastures is a challenge for livestock farmers. Weeds not only compete with forage for nutrients, but can also harm livestock. Chemical control is one of the options to tackle this problem. In this sense, automatic detection is a necessary alternative, as it increases agility and reduces process costs. In this sense, machine learning algorithms emerge as possible alternatives. In this work, different models for the classification of invasive plants were evaluated, using images of the genus Rumex as reference. The evaluated models were based on monolithic strategies and on systems with multiple classifiers. The experiments, performed on two datasets with different proportions of positive and negative classes, showed that KNN was the most competent single model in detecting the invasive plant, achieving an accuracy of more than 95% and a sensitivity of more than 0.90 in both datasets. Among the models based on ensembles, Random Forest stood out with a accuracy of over 0.95 and a sensitivity of about 90% in both scenarios.
subject: Original Paper
  topic: section
subject: Automatic Classification
  topic: keywords
subject: Weeds
  topic: keywords
subject: Forage
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Livestock
  topic: keywords
subject: Detecção de Plantas Invasoras em Pastagens
  topic: subjects
subject: Aprendizado de Máquina
  topic: subjects
subject: Reconhecimento de Padrões
  topic: subjects
subject: Ciência da Computação
  topic: disciplines
subject: Metodologia e Técnicas da Computação
  topic: disciplines
creator: Gabriel Tadioto Oliveira
creator: André Luiz Brun
rights: Copyright (c) 2025 Gabriel Tadioto Oliveira, André Luiz Brun http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 21
ending page: 33
doi: 10.5335/rbca.v17i2.16301

CNOPsolution Tester: a web application for result validation in constrained optimization problems
type: item
description: In numerical optimization, solutions to optimization problems are developed using methods or techniques that generate approximate results for complex problems, such as metaheuristics. When these techniques generate results, it is essential to verify whether these values meet the optimization problem requirements, for example, values of the variables within the minimum and maximum range and the satisfaction of the problem constraints. Doing it by hand can be very difficult, especially considering optimization problems have different characteristics. This paper presents the development of a web application called CNOPsolution Tester, which allows researchers and end-users to validate the values of decision variables based on their ranges and constraints. The application was designed based on diagrams created with the Unified Modeling Language and developed in JavaScript. We tested the web application with multiple users and 24 benchmark optimization problems to evaluate its usability, measuring the number of clicks needed to solve each problem. We also validated the functionality using 20% of the problems. The results demonstrate that the CNOPsolution Tester is an efficient tool and easy to use by any end-user, as shown by the response time and the number of clicks used on the application. We applied the Pearson correlation to identify the relationship between the number of clicks, problem variables, and constraints.
subject: Original Paper
  topic: section
subject: Metaheuristics
  topic: keywords
subject: Optimization Problems
  topic: keywords
subject: Pearson Correlation
  topic: keywords
subject: Tester
  topic: keywords
subject: Programmer
  topic: subjects
subject: tester
  topic: subjects
subject: Artificial intelligence specialist
  topic: subjects
subject: RESEARCHERS IN COMPUTER SCIENCES
  topic: subjects
subject: Computer Science
  topic: disciplines
subject: Information Technology
  topic: disciplines
subject: Software Engineering
  topic: disciplines
subject: Artificial Intelligence
  topic: disciplines
creator: Adrian García-López
creator: Oscar Chávez-Bosquez
creator: Betania Hernández-Ocaña
rights: Copyright (c) 2025 Adrian García-López, Oscar Chávez-Bosquez, Betania Hernández Ocaña http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 103
ending page: 112
doi: 10.5335/rbca.v17i2.16291

AMIPDI: architecture model to identify pests in soybean defoliation images using convolutional neural networks
type: item
description: Pests that attack crops are one of the leading causes of low production and economic losses. Identifying and diagnosing forecasts is one way to reduce losses and maintain quality in production. Defoliation of crops harms production and production quality. Using computational technologies such as Artificial Intelligence andMachine Learning has enabled the identification of pests faster and earlier. In this paper, we propose a Deep Learning-based architecture aimed at identifying and diagnosing insects by analyzing defoliation in crop images. Different Convolutional Neural Network approacheswere considered to evaluate the proposed architecture. The training and testing of themodelswere performed using images collected by drone in a natural environment. The approach that presented the best performance in our scenario was the VGG16. The average accuracy in the validation phase was 0.95, while in the test set, we obtained 0.86.
subject: Original Paper
  topic: section
subject: Soybean
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Pest
  topic: keywords
subject: Defoliates
  topic: keywords
subject: Agriculture
  topic: keywords
subject: Artificial Intelligence
  topic: disciplines
creator: Maria Eloisa Mignoni
creator: Aislan Honorato
creator: Cesar Zagonel
creator: Gabriel de Oliveira Ramos
creator: Rafael Kunst Kunst
rights: Copyright (c) 2025 Maria Eloisa Mignoni, Aislan, Cesar, Gabriel de Oliveira Ramos, Rafael http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 11
ending page: 20
doi: 10.5335/rbca.v17i2.16186

Benchmarking Machine Learning Algorithms in Fake Reviews Detection in Brazilian Portuguese
type: item
description: The proliferation of fake reviews has become a growing concern on e-commerce platforms, as these reviews can mislead consumers and harm the reputation of products and services offered. Automatic detection of fake reviews is a challenging task, as it requires analyzing textual data and identifying subtle patterns that indicate the veracity of reviews. Since fake review datasets in Portuguese are scarce, in this work, we generate and propose a dataset in Brazilian Portuguese for the detection of fake reviews. Then, four machine learning algorithms, combined with three text vectorization methods, are used in a transfer learning scheme for fake review classification. A comparative analysis is carried out using performance metrics such as accuracy, F1-score, and false positives. The results show that, for the proposed dataset, the combination of Logistic Regression and a pre-trained BERT model in Brazilian Portuguese, i.e., BERTimbau, reached the best metric values, reaching 96.61% of accuracy.
subject: Original Paper
  topic: section
subject: Fake reviews
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Classification
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: Eduardo Camargo Ribeiro Borges
creator: Cristiano Mesquita Garcia
creator: Samuel da Silva Feitosa
creator: Carlos Henrique Radavelli
rights: Copyright (c) 2025 Cristiano Mesquita Garcia, Eduardo C. R. Borges, Samuel da S. Feitosa, Carlos H. Radavelli http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-23
publication date: 2025-05-23
starting page: 12
ending page: 22
doi: 10.5335/rbca.v17i1.16183

Transfer Learning and Data Augmentation Analysis for Computer Vision in Robot Football using Deep Learning
type: item
description: Robot Soccer is a promising field for advances in computing and robotics, especially in computer vision with Convolutional Neural Networks (CNN). New models in this area can optimize the intelligence and autonomy of machine learning systems. This study developed databases to create Image Classification and Object Detection models in humanoid robot football, analyzing the impact of Transfer Learning (TL) and Data Augmentation (DA). Using the Edge Impulse platform, experiments with 5 image classes were carried out for Image Classification, while 4 databases with different image sizes were used for Object Detection. The results showed that TL increased the accuracy of the Classification models between 11.25% and 38%, and the combination with DA resulted in variations of up to 3.5% in accuracy in comparison to when applying only TL. In Object Detection, increasing the number of images improved accuracy by 33% and Recall by 37%. These findings highlight the importance of these techniques for the learning and interaction of robotic agents in complex environments. Future studies can focus on tuning hyperparameters and new architectures to optimize these models.
subject: Original Paper
  topic: section
subject: Computer vision
  topic: keywords
subject: Data Augmentation
  topic: keywords
subject: Image Classification
  topic: keywords
subject: Robot soccer
  topic: keywords
subject: Transfer Learning
  topic: keywords
creator: Franklin Andrade de Brito
creator: Rodrigo C. de Barros
creator: André Luiz Carvalho Ottoni
rights: Copyright (c) 2025 Franklin Andrade de Brito, Rodrigo C. de Barros, André Luiz Carvalho Ottoni http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 1
ending page: 10
doi: 10.5335/rbca.v17i2.16154

Semiotic Evaluation of the Communicability of a Collaborative Libras Dictionary
type: item
description: Communicability refers to the ability of a system to communicate effectively and understandably with the user. In this sense, this article investigates the communicability of a system developed to serve as a collaborative dictionary for the Brazilian Sign Language (Libras). The research uses semiotic inspection methodologies to evaluate how the developers' intentions are perceived and interpreted by users. The results point to the need for significant refinements in the tool, to improve the communicability and the user experience - essential aspects for the teaching and broader social inclusion of the deaf community. Such modifications aim to facilitate the use of the dictionary, promoting a clearer understanding of the content and more effective learning of Libras by its users.
subject: Original Paper
  topic: section
subject: Communicability Assessment
  topic: keywords
subject: Libras
  topic: keywords
subject: Semiotic Inspection
  topic: keywords
subject: Avaliação de Comunicabilidade
  topic: subjects
subject: Inspeção Semiótica
  topic: subjects
subject: Libras
  topic: subjects
subject: Sistemas Colaborativos
  topic: disciplines
subject: Interação Humano Computador
  topic: disciplines
creator: Júlia Llorente
creator: Carla Berkenbrock
creator: Fabíola S. F. Sell
rights: Copyright (c) 2025 Carla Berkenbrock http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-27
publication date: 2025-05-27
starting page: 45
ending page: 54
doi: 10.5335/rbca.v17i1.16018

Multi-queue Round Robin Scheduling for Enhanced Performance in Integration Platforms
type: item
description: Contemporaneous environments involve a large amount of information received from many interconnected devices and integrated by many platforms. The Internet of Things and Cloud computing offer services to deal with such big data so that enterprises can play in their business core by outsourcing the development, configuration, and maintenance of those services and their computational resources. Many enterprises use integration Platforms as a Service to maintain their applications and data synchronously. However, current integration platform task scheduling algorithms face some difficulties when tackling large volumes of data. In this article, we propose a scheduling algorithm based on the round-robin heuristic through multiple task queues that presents better performance than the traditional First-in-first-out heuristic used by current platforms. We experimented with our algorithm in the execution of an integration process and validated the results using statistical techniques.
subject: Original Paper
  topic: section
subject: Application integration
  topic: keywords
subject: task scheduling
  topic: keywords
subject: algorithm
  topic: keywords
subject: workflow scheduling
  topic: keywords
subject: integration patterns
  topic: keywords
subject: round robin
  topic: keywords
subject: Application integrationn
  topic: subjects
subject: Software Engineering
  topic: disciplines
creator: Daniela Lopes Freire
creator: Rafael Z. Frantz
creator: Vitor Basto-Fernandes
creator: Gerson Battisti
creator: Sandro Sawicki
creator: Fabricia Roos-Frantz
rights: Copyright (c) 2025 Daniela Lopes Freire, Rafael Z. Frantz, Vitor Basto-Fernandes, Gerson Battisti, Sandro Sawicki, Fabricia Roos-Frantz http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 100
ending page: 113
doi: 10.5335/rbca.v17i3.16747

On the Use of Fireworks Algorithm and Particle Swarm Optimization for Watermarking in Image Vector Quantization
type: item
description: Information hiding has been addressed in several studies. When the information is hidden in digital images, it can be carried out in three domains: spatial domain (original space of image pixels), frequency domain (or transformed domain, such as Discrete Cosine Transform domain) and compressed domain. In the last case, one can cite data embedding in images compressed by vector quantization (VQ). This paper addresses the problem of codebook partition in the scenario of invisible watermark embedding in digital images compressed by VQ. In this paper, two techniques are investigated for partitioning purposes: the PSO (Particle Swarm Optimization) algorithm and the EFA (Enhanced Fireworks Algorithm) as alternatives to the Genetic Algorithm. The performance of the techniques is evaluated with regard to the algorithms execution time. Robustness of the watermark against a variety of attacks is assessed for codebooks partitioned with the aforementioned algorithm.
subject: Original Paper
  topic: section
subject: Fireworks Algorithm
  topic: keywords
subject: Information Hiding
  topic: keywords
subject: Particle Swarm Optimization
  topic: keywords
subject: Vector Quantization
  topic: keywords
subject: Watermarking
  topic: keywords
creator: Marcos José Canêjo Estevão de Azevêdo
creator: Jair Galvão
creator: Waslon T. A. Lopes
creator: Hugerles S. Silva
creator: Francisco Madeiro
rights: Copyright (c) 2025 Marcos José Canêjo Estevão de Azevêdo http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-23
publication date: 2025-05-23
starting page: 33
ending page: 44
doi: 10.5335/rbca.v17i1.16442

Comparative study between neuromorphic implementations using Spiking Neural Network connected by Network-on-Chip
type: item
description: The efficient implementation of neural networks in hardware, especially using networks-on-chip for the interconnection of spiking neural networks in neuromorphic chips, is a significant advancement in computational systems that mimic the human brain. This study conducts a systematic review on the integration of these technologies in neuromorphic chips, using the PRISMA methodology to analyze various studies and compare different hardware approaches. The results highlight the importance of energy-efficient and high-performance solutions for artificial intelligence. Despite technical challenges, neuromorphic computing is rapidly evolving, with the potential to advance several emerging technologies. The challenges include exploring neuron models, learning techniques, and interconnection and routing strategies to improve efficiency and performance.
subject: Original Paper
  topic: section
subject: Hardware
  topic: keywords
subject: Network-on-Chip
  topic: keywords
subject: Neuromorphic Chips
  topic: keywords
subject: PRISMA
  topic: keywords
subject: Spiking Neural Networks
  topic: keywords
subject: Chips Neuromórficos
  topic: subjects
subject: Redes Neurais Pulsantes
  topic: subjects
subject: Redes-em-Chip
  topic: subjects
subject: Artificial Inteligence
  topic: disciplines
subject: Neuromorphic Computation
  topic: disciplines
creator: Victor Benvenutti Cavalheiro
creator: Beatriz Oliveira Câmara
creator: Janaina Gonçalves Guimarães
rights: Copyright (c) 2025 Victor Benvenutti Cavalheiro, Beatriz Oliveira Câmara, Janaina Gonçalves Guimarães http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-23
publication date: 2025-05-23
starting page: 1
ending page: 11
doi: 10.5335/rbca.v17i1.16019

A Coloured Petri Nets Model of the 5G Authentication and Key Agreement Protocol
type: item
description: Background: The security in 5G networks must safeguard network functions and other components responsible for end-to-end communications, intending to provide confidentiality, integrity, availability, authenticity, and non-repudiation. Some security functions that protect 5G networks are part of relevant protocols such as the 5G Authentication and Key Agreement (5G-AKA). However, the 5G-AKA protocol has some limitations, becoming susceptible to attacks such as replay and man-in-the-middle. Results: Aiming to analyze the 5G-AKA protocol regarding security properties and help to identify possible vulnerabilities and threats, we used coloured Petri nets to model the protocol's components, their interactions, and an example of an attack scenario. We validated the model using simulations and the model-checking technique. Conclusions: This model may help improve the security of 5G networks by validating properties and behaviors using simulations and formal verification. The proposed 5G-AKA model can be a relevant tool for identifying vulnerabilities and proposing mitigation measures for different 5G topologies.
subject: Original Paper
  topic: section
subject: 5G-AKA
  topic: keywords
subject: formal modeling
  topic: keywords
subject: security
  topic: keywords
subject: formal verification
  topic: keywords
creator: José Sávio Gama Macêdo
creator: Antônio Carlos de Oliveira Bezerra
creator: Álvaro Sobrinho
creator: Leandro Dias da Silva
creator: Dalton C. G. Valadares
creator: Danilo F. S. Santos
creator: Angelo Perkusich
rights: Copyright (c) 2025 José Sávio Gama Macêdo, Antônio Carlos de Oliveira Bezerra, Álvaro Sobrinho, Leandro Dias da Silva, Dalton C. G. Valadares, Danilo F. S. Santos, Angelo Perkusich http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 52
ending page: 63
doi: 10.5335/rbca.v17i3.16506

Evaluation of Routing Protocols in Mobile Ad-Hoc Networks Applied to Military Scenarios: Challenges and ResearchGuidelines
type: item
description: Ad-hoc networks, widely studied by institutions such as the Internet Engineering Task Force, have gained prominence in military contexts due to their rapid deployment and the use of dynamic and adaptive routing protocols, which are essential for agile operations and training. Simulation-based studies have made it possible to identify the protocols best suited to different operational conditions, providing valuable guidance for their selection and use in military environments. Although the literature extensively addresses Ad-hoc networks, there is a noticeable lack of studies that jointly examine a set of protocols capable of supporting a complete service infrastructure tailored to military communication needs. The distinctive contribution of this work lies precisely in filling this gap: rather than analyzing isolated protocols, it proposes an integrated structure that takes into account the specificities of military networks—distinct from civilian and commercial networks—thus contributing to the development of a more robust and efficient technological framework to support military operations.
subject: Original Paper
  topic: section
subject: Performance
  topic: keywords
subject: MANET
  topic: keywords
subject: Ad-hoc protocols
  topic: keywords
subject: Ad-hoc routing
  topic: keywords
subject: Military network
  topic: keywords
creator: Cláudia Rödel Bosaipo Sales da Silva
creator: Ronaldo Ribeiro Goldschmidt
creator: Ronaldo Moreira Salles
rights: Copyright (c) 2025 Cláudia Rödel Bosaipo Sales da Silva, Ronaldo Ribeiro Goldschmidt, Ronaldo Moreira Salles http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 41
ending page: 51
doi: 10.5335/rbca.v17i3.16484

A process for source code unstructured search
type: item
description: A consulta em código fonte representa um importante recurso para auxiliar desenvolvedores na compreensão de programas, bem como em atividades de refatoração, especialmente em grandes repositórios de código. Este trabalho apresenta um processo que busca facilitar a recuperação de informação, utilizando técnicas de consultas não estruturadas em código fonte. O processo apresenta arquivos classificados por ordem de importância com base nas características da linguagem de programação e nas preferências definidas pelos desenvolvedores. Foram realizadas duas instanciações do processo, nas linguagens de programação Java e AspectJ, demonstrando a aplicação do processo e como ele pode facilitar a identificação das informações recuperadas.
subject: Original Paper
  topic: section
subject: Unstructured search
  topic: keywords
subject: Software Processes
  topic: keywords
subject: AHP
  topic: keywords
creator: Rodrigo de Castro Gil
creator: Eduardo Kesser Piveta
creator: Cristiano De Faveri
creator: Deise de Brum Saccol
creator: Lisandra Manzoni Fontoura
rights: Copyright (c) 2025 Rodrigo de Castro Gil, Eduardo Kesser Piveta, Cristiano De Faveri, Deise de Brum Saccol, Lisandra Manzoni Fontoura http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 84
ending page: 99
doi: 10.5335/rbca.v17i3.16482

Comparative analysis of PID control and MPC in CartPole and LunarLander environments
type: item
description: With technological advancement, increasingly complex systems emerge, driving the relentless pursuit of more effective control methods. In this study, the control of two Gymnasium environments will be explored: LunarLander and CartPole. LunarLander represents a simplification of vehicle autonomy, while CartPole is closely related to robotics. The goal is to apply Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control to these environments, aiming to achieve their specific objectives. The techniques employed have shown success in controlling the environments, highlighting the effectiveness of these methods in complex challenges. Additionally, this study addresses various engineering concepts, from physical fundamentals to system identification, leveraging Python packages to offer an accessible approach for both academia and industry. This multidisciplinary analysis promises to significantly contribute to the advancement of knowledge and practical application in the field of complex system control.
subject: Original Paper
  topic: section
subject: CartPole
  topic: keywords
subject: Model Predictive Control
  topic: keywords
subject: Proportional-Integral-Derivative Control
  topic: keywords
subject: LunarLander
  topic: keywords
subject: Computação Aplicada
  topic: subjects
subject: Modelagem Matemática
  topic: subjects
subject: Controle de Processos
  topic: subjects
subject: Python
  topic: subjects
subject: Engenharia de Controle
  topic: disciplines
subject: Ciência de Dados
  topic: disciplines
creator: Gabriel Bueno Leandro
creator: Samir Angelo Milani Martins
rights: Copyright (c) 2024 Samir Angelo Milani Martins http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-03
publication date: 2024-12-03
starting page: 74
ending page: 88
doi: 10.5335/rbca.v16i3.15852

Recognition of Brazilian Banknotes Using Artificial Intelligence to Assist Visually Impaired Individuals
type: item
description: Recognizing Brazilian Real banknotes remains a challenge for visually impaired individuals. This work presents a proposal that employs a convolutional neural network to assist visually impaired people in recognizing Real banknotes using a smartphone with a camera. The implementation provides an application called Banknotes Recognition, which performs the recognition process swiftly by capturing the banknote image with the smartphone's camera. Tests conducted with a set of 66 images of Real banknotes showed an accuracy of 100%, with all banknotes correctly classified even under unfavorable conditions, such as folded notes, complex backgrounds, various lighting conditions, and worn-out notes.
subject: Original Paper
  topic: section
subject: Accessibility
  topic: keywords
subject: Brazilian Banknote Recognition
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Artificial Intelligence
  topic: subjects
subject: Deep Learning
  topic: subjects
subject: Exatas
  topic: disciplines
creator: Karen de Almeida
creator: Almir Olivette Artero
creator: Danilo Medeiros Eler
creator: Maurício Araújo Dias
creator: Danillo Roberto Pereira
creator: Francisco Assis da Silva
rights: Copyright (c) 2025 Danillo Roberto Pereira, Karen de Almeida, Almir Olivette Artero, Danilo Medeiros Eler, Maurício Araújo Dias, Francisco Assis da Silva http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-05-23
publication date: 2025-05-23
starting page: 23
ending page: 32
doi: 10.5335/rbca.v17i1.15934

A systematic literature mapping on the security of 5G and vertical applications
type: item
description: Background: The deployment of 5G infrastructure is one of the vectors for new application scenarios since it enables enhanced data bandwidth, low latency, and comprehensive signal coverage. This communication system supports various vertical applications such as smart health, smart cities, smart grids, and transportation systems. However, these applications bring new challenges to 5G networks due to specific requirements for such scenarios. Furthermore, as software-based technologies, including network slicing, software-defined networks, network function virtualization, and multi-access edge computing, are a fundamental part of the 5G architecture, the network can expose these applications to new security and privacy concerns. Results: This study summarizes existing literature on 5G vertical applications security. We highlight vulnerabilities, threats, attacks, and solutions for 5G vertical applications. We conducted a systematic literature mapping to discuss security and privacy challenges regarding the 5G vertical applications. We reviewed 389 papers from 2,349 produced by searching with a curated search query and discussed vulnerabilities, threats, attacks, and solutions for 5G vertical applications. Conclusions: Smart cities, Industry 4.0, smart transportation, public services, smart grids, and smart health are vertical applications with relevant security concerns. We observed the need for more research since the 5G and vertical applications continuously evolve.
subject: Original Paper
  topic: section
subject: Vertical Applications
  topic: keywords
subject: 5G
  topic: keywords
subject: Security
  topic: keywords
subject: Privacy
  topic: keywords
creator: Álvaro Sobrinho
creator: Matheus V. P. dos Santos
creator: Amanda B. Silva
creator: Edmar C. Gurjao
creator: Danilo F. S. Santos
rights: Copyright (c) 2024 Álvaro Sobrinho http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-03
publication date: 2024-12-03
starting page: 100
ending page: 115
doi: 10.5335/rbca.v16i3.16005

Pothole Detection Web App: an approach for detecting potholes in asphalt pavements using YOLO
type: item
description: The detection of potholes in asphalt pavements is a crucial task for road safety. This survey requires significant time and financial resources. An automatic method for this task can help in Pavement Management System, speeding up processes of road recovery and maintenance of materials. With this in mind, this work presents the Pothole Detection Web App, a smart web application for detecting potholes in asphalt pavements using the YOLOv7-tiny architecture. The solution allows performing the identification of potholes in asphalt pavements by photos, videos or live broadcasts. The images or videos are sent to a web server, where the detection model is applied and the results are returned to the user. Preliminary tests pointed out good results, with an accuracy of 74% (F1-Score = 66%). The solution proved to be capable of detecting potholes and estimating defect dimensions with a good approximation, in reports with organized information. In addition, it showed good performance for real-time analysis, considering different browsers. The approach can serve both urban roads, using internet, and open roads, using communication with a local area network.
subject: Original Paper
  topic: section
subject: asphalt pavement
  topic: keywords
subject: pothole detection
  topic: keywords
subject: real-time object detection
  topic: keywords
subject: web app
  topic: keywords
subject: YOLOv7-tiny
  topic: keywords
creator: Jean Cássio Peres Barbosa
creator: Francisco Dalla Rosa
creator: Rafael Rieder
rights: Copyright (c) 2024 Jean Cássio Peres Barbosa, Francisco Dalla Rosa, Rafael Rieder http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-03
publication date: 2024-12-03
starting page: 25
ending page: 36
doi: 10.5335/rbca.v16i3.15851

MQTT Poller: an endogenous framework for large-scale MQTT protocol performance evaluation
type: item
description: MQTT is an application layer protocol that supports synchronous and asynchronous client communication. It employs an intermediary server/broker between clients and a topic system to send and receive messages. Thanks to its simplicity and low control cost, it is commonly used in the development of applications for the Internet of Things (IoT), and it is also of interest in several research works, such as those related to the scalability of the protocol. In this context, it is observed that there is a lack of performance evaluation and experiment orchestration instruments. This work presents a framework for evaluating the performance of the MQTT protocol in many scenarios. The development of the framework follows an endogenous communication model (i.e., based on the MQTT topic schema) for control and interaction between the architecture's elements. Starting from a reference performance evaluation tool (MQTTLoader), it is possible to configure an evaluation scenario that makes it feasible to run multiple instances of this tool, under the coordination of an orchestrator, on machines possibly hosted in different administrative domains. A case study demonstrates the functionalities and practicality of using the framework to conduct MQTT performance evaluation experiments.
subject: Original Paper
  topic: section
subject: MQTT
  topic: keywords
subject: Publisher/Subscriber
  topic: keywords
subject: Performance evaluation
  topic: keywords
creator: Diogo Maciel da Cunha
creator: Marco Aurélio Spohn
rights: Copyright (c) 2024 Diogo Maciel da Cunha, Marco Aurélio Spohn http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-03
publication date: 2024-12-03
starting page: 37
ending page: 47
doi: 10.5335/rbca.v16i3.15778

Preventing flow rules injection attacks in SDNs using Blockchains
type: item
description: Due to the programmability feature of Software-Defined Networks (SDNs), both the control software and network applications are susceptible to attacks, such as the injection of false flow rules. Thus, there is a need to investigate alternative solutions that enable the verification of integrity, authenticity, correctness, and agreement on the set of flow rules to be deployed on switches. In this context, the secure distribution of flow rules can be implemented through Distributed Ledgers (DLs), specifically utilizing Blockchain (BC) technology. This technology allows for the maintenance of a distributed and collaborative information registry among participants, who are responsible for the integrity and validation of the inserted data. With this in mind, this article presents an integrated model using BC to support security in the distribution and establishment of flow rules in SDNs. The proposed model was validated through experimental performance analysis and security assessment in an emulated environment. Results show that the proposed model is effective against attacks based on packet injection techniques.
subject: Original Paper
  topic: section
subject: SDN
  topic: keywords
subject: Blockchain;
  topic: keywords
subject: Security;
  topic: keywords
subject: Performace Analysis.
  topic: keywords
creator: Ramon Amorim dos Santos Delgado Ama
creator: Valério Rosset
rights: Copyright (c) 2024 Ramon Amorim dos Santos Delgado Ama, Valério Rosset http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-03
publication date: 2024-12-03
starting page: 63
ending page: 73
doi: 10.5335/rbca.v16i3.15696

R2SC: RBAC system smart contract-based
type: item
description: Due to the popularity of Blockchain technology, there have been many applications of it in many contexts. It is a technology that allows trusted communication between untrusted entities without a centralized third party. Also, Blockchain is a chronological sequence of blocks linked through the hash of each one. This characteristic ensures data integrity and immutability. Therefore, Blockchain is a powerful technology for recording data like access control rules. This work utilizes Blockchain to provide a framework applying the Smart Contract concept for a Role-Based Access Control (RBAC) system. It is the RBAC System Smart Contract-based (R2SC). Also, it uses the framework Truffle to test the Smart Contract functionalities. In counterpart to the related works, R2SC has scalability.
subject: Original Paper
  topic: section
subject: Blockchain
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Access Control
  topic: keywords
creator: Lucas Vargas Dias
creator: Tiago Antonio Rizzetti
creator: Wagner Brignol
creator: Luciane Neves Canha
rights: Copyright (c) 2024 lucas vargas dias, Tiago Rizzetti, Wagner Brignol, Luciane Canha http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-12-03
publication date: 2024-12-03
starting page: 116
ending page: 123
doi: 10.5335/rbca.v16i3.15634

Software Development Models in Game Development: a Literature Review
type: item
description: The present work is a systematic literature review (SLR) based on the PICOC framework (Population, Intervention, Comparison, Outcome, and Context), with the objective of mapping and analyzing studies on the application of software engineering methodologies to game development. We analyzed scientific papers up to 2024, retrievable from the ACM, IEEE, and SOL databases using the search string ("game development model"OR "game development models") OR (("software development model"OR "software development models") AND (games OR game)). As a result, 9 articles that met the inclusion criteria were found, and we present the main methodologies applied, their characteristics, application context, and performance. Overall, the analysis of the selected articles reveals that, although there are significant parallels with traditional software development, game development presents unique challenges and demands that require adapted and specific methodologies. This distinction becomes evident in how software engineering models are applied and modified to meet the specific needs of the gaming industry, which intrinsically combines technical and creative aspects.
subject: Original Paper
  topic: section
subject: Digital games development
  topic: keywords
subject: software development models
  topic: keywords
subject: systematic literature review
  topic: keywords
creator: Gabriel Acassio Correia
creator: Vinícius Carvalho Pereira
creator: Cristiano Maciel
rights: Copyright (c) 2025 Gabriel Acassio Correia, Vinícius Carvalho Pereira, Cristiano Maciel http://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 74
ending page: 83
doi: 10.5335/rbca.v17i3.16407