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 February 2026, based on new submission guidelines and adjustments to the article template. We emphasize that the submissions already made are in the normal evaluation process for the next two editions of RBCA. Upcoming editions: v.17, n.3 (November/2025) and v.18, n.1 (April/2026)
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).
Performance Analysis of Ethernet Networks Through Quality of Service (QoS) Metrics Using Real and Virtual Machines
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.
Avaliação de Desempenho de Redes de Computadores
Copyright (c) 2025 Diones da Silva Rodrigues, Diógenes José http://creativecommons.org/licenses/by-nc-nd/4.0
Explaining the black-box or using the black-box to develop better interpretable solutions?
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.
Copyright (c) 2025 Vinicius Alves Matias, Julia Machado Lechi, Norton Trevisan Roman, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
Advances in scientific article recommendation systems: A systematic literature review
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.
Copyright (c) 2025 Bruno de Santana Braga Contreras, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
A methodology for assessing UX dimensions in standardized questionnaires
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.
Copyright (c) 2025 Erico Souza Veriscimo, João Luiz Bernardes Júnior, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
Automatic Identification of Electrical Appliances in Domestic Settings
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.
Copyright (c) 2025 Antonio D. T. Amurim, Elvis M. G. Stancanelli http://creativecommons.org/licenses/by-nc-nd/4.0
Performance Analysis of Routing Algorithms based on V2V Topology using NS-3 Simulations
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.
Copyright (c) 2025 Carlos P. Alves, Natália C. Fernandes, Tadeu N. Ferreira http://creativecommons.org/licenses/by-nc-nd/4.0
Application of machine learning strategies in the detection of invasive plants in pastures
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.
Detecção de Plantas Invasoras em Pastagens
Copyright (c) 2025 Gabriel Tadioto Oliveira, André Luiz Brun http://creativecommons.org/licenses/by-nc-nd/4.0
CNOPsolution Tester: a web application for result validation in constrained optimization problems
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.
Copyright (c) 2025 Adrian García-López, Oscar Chávez-Bosquez, Betania Hernández Ocaña http://creativecommons.org/licenses/by-nc-nd/4.0
AMIPDI: architecture model to identify pests in soybean defoliation images using convolutional neural networks
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.
Copyright (c) 2025 Maria Eloisa Mignoni, Aislan, Cesar, Gabriel de Oliveira Ramos, Rafael http://creativecommons.org/licenses/by-nc-nd/4.0
Benchmarking Machine Learning Algorithms in Fake Reviews Detection in Brazilian Portuguese
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.
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
Transfer Learning and Data Augmentation Analysis for Computer Vision in Robot Football using Deep Learning
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.
Copyright (c) 2025 Franklin Andrade de Brito, Rodrigo C. de Barros, André Luiz Carvalho Ottoni http://creativecommons.org/licenses/by-nc-nd/4.0
Semiotic Evaluation of the Communicability of a Collaborative Libras Dictionary
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.
Copyright (c) 2025 Carla Berkenbrock http://creativecommons.org/licenses/by-nc-nd/4.0
On the Use of Fireworks Algorithm and Particle Swarm Optimization for Watermarking in Image Vector Quantization
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.
Copyright (c) 2025 Marcos José Canêjo Estevão de Azevêdo http://creativecommons.org/licenses/by-nc-nd/4.0
Comparative study between neuromorphic implementations using Spiking Neural Network connected by Network-on-Chip
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.
Copyright (c) 2025 Victor Benvenutti Cavalheiro, Beatriz Oliveira Câmara, Janaina Gonçalves Guimarães http://creativecommons.org/licenses/by-nc-nd/4.0
Comparative analysis of PID control and MPC in CartPole and LunarLander environments
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.
Proportional-Integral-Derivative Control
Copyright (c) 2024 Samir Angelo Milani Martins http://creativecommons.org/licenses/by-nc-nd/4.0
Recognition of Brazilian Banknotes Using Artificial Intelligence to Assist Visually Impaired Individuals
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.
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
A systematic literature mapping on the security of 5G and vertical applications
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.
Copyright (c) 2024 Álvaro Sobrinho http://creativecommons.org/licenses/by-nc-nd/4.0
Pothole Detection Web App: an approach for detecting potholes in asphalt pavements using YOLO
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.
Copyright (c) 2024 Jean Cássio Peres Barbosa, Francisco Dalla Rosa, Rafael Rieder http://creativecommons.org/licenses/by-nc-nd/4.0
MQTT Poller: an endogenous framework for large-scale MQTT protocol performance evaluation
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.
Copyright (c) 2024 Diogo Maciel da Cunha, Marco Aurélio Spohn http://creativecommons.org/licenses/by-nc-nd/4.0
Preventing flow rules injection attacks in SDNs using Blockchains
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.
Copyright (c) 2024 Ramon Amorim dos Santos Delgado Ama, Valério Rosset http://creativecommons.org/licenses/by-nc-nd/4.0
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.
Copyright (c) 2024 lucas vargas dias, Tiago Rizzetti, Wagner Brignol, Luciane Canha http://creativecommons.org/licenses/by-nc-nd/4.0
Machine learning in indoor agriculture: a systematic mapping and taxonomy
The present article describes a systematic mapping of works related to the application ofMachine Learning in Indoor Agriculture. This research encompasses searches conducted up toMarch 2024 in the IEEE Xplore, ACM Digital Library, Springer Library, Science Direct, Scopus, MDPI,Wiley, and Taylor & Francis databases. The initial search resulted in 10,149 articles, of which 76 studies were selected for full reading after applying the inclusion and exclusion criteria. This analysis led to the selection of 36 articles that were studied with the aim of answering 9 research questions along with a taxonomy proposal. The main results reveal that the selected articles employed 43 different machine learning techniques. Additionally, it was found that no study was published before the year 2018. Twenty-six distinct pieces of information were identified from the crops, with the cultivation of 17 different crops and the use of 4 devices for information collection, among other relevant information. The analysis of the articles evidenced a clear trend in the use of machine learning in Indoor Agriculture.
Copyright (c) 2024 Bruno Guilherme Martini, Jorge Luis Victória Barbosa http://creativecommons.org/licenses/by-nc-nd/4.0
Unraveling user experience and mood: a comparative evaluation and predictive modeling approach
Background User Experience (UX) evaluation is crucial for Information Systems, as it is closely related to their acceptance and post-adoption performance. It often involves the use of one or more standardized questionnaires. This evaluation can be costly, from selecting the appropriate questionnaire to extracting data from a significant number of users, analyzing the data and comparing results, especially with evaluations that use different questionnaires. Thus, Artificial Intelligence is increasingly adopted to aid these tasks. To this end, we explore several machine learning (ML) algorithms to correlate and predict the answers of different UX and mood questionnaires. Results The developed approach successfully predicted questionnaire responses with high accuracy (error less than 1 on a 7-point Likert scale), particularly for the longer questionnaire. Additionally, it revealed that mood significantly contributes to this accuracy. Conclusions Our main contributions include demonstrating how ML can be utilized to reduce UX evaluation costs and exploring the impact of user mood on this evaluation.
Copyright (c) 2024 Gabriela Jie Han, Enrico de Souza Veriscimo, João Luiz Bernardes Júnior, Luciano Antonio Digiampietri http://creativecommons.org/licenses/by-nc-nd/4.0
An investigation about the genetic operators in the Evolutionary Algorithm applied to the Knight’s Tour
Bioinspired Computing is an area of research focused on developing techniques inspired by natural phenomena to solve computational optimization problems, classified as NP problems. In this work, EA (Evolutionary Algorithm) is implemented to solve the Knight’s Tour. This algorithm is strongly based on Darwin’s Evolutionary Theory, in particular, Natural Selection. The Knight’s Tour, in turn, is a combinatorial problem widely used as a basis for improving or developing algorithms and for solving real problems, such as image encryption. Leonhard Euler was the first to formally study it. The Knight’s Tour consists of finding a sequence ofmoves—made by the chess piece corresponding to the knight -- that travels across the entire board without visiting a squaremore than once. Therefore, this work aimed to implement EA for the Knight’s Tour solution, improving the results found in the literature, through the implementation of new operators (selection,mutation) and the central initialization. The first is based on the exploration of a larger search field through the crossing of dissimilar parents, the second is the exchange of genes (pathways) for valid neighbours, and the third, the initialization in the central (or approximate) square of the board was used. The experiments showed that the implemented EA was able to solve Knight’s Tour for nxn boards, with 5 ≤ n ≤ 20.
Copyright (c) 2024 Murielly Oliveira Nascimento, Christiane Regina Soares Brasil http://creativecommons.org/licenses/by-nc-nd/4.0
Systematic literature review on digital note-taking software for learning
As an essential part of the learning process, the practice of note-taking benefits students in understanding and revi- ewing the material. Technological advancements and the ubiquity of electronic devices have revolutionized traditional educational practices, especially regarding note-taking. This work conducts a comprehensive analysis of the state of the art of digital annotation software, acknowledging their functionalities, the context in which they are embedded, and their impacts on the educational process. The study reveals that digital annotation software not only facilitate interaction with study material but also transform education into a more flexible experience, supporting various teaching and learning modalities.
Copyright (c) 2024 Laurindo Britto Neto, Daniel Carvalho http://creativecommons.org/licenses/by-nc-nd/4.0
Analysis of keyword extraction methods for legal document classification
Given the significant increase in judicial demand and the growing scarcity of resources in the Brazilian legal system, it becomes imperative to invest in new technological applications. A critical area for analysis is the processing of legal cases, which still involves several manually conducted stages, including the initial screening. In this context, this research aims to analyze the techniques of keyword extraction and verify which extraction technique can help in the classification of legal documents. To this end, two databases were used, one containing 86 cases and the other with 3,543 jurisprudence, both belonging to the Judiciary Court of Maranhão. The results showed that the Bertopic and YAKE extraction methods obtained better performance for the legal data. It is concluded that these models have the potential to be used to provide greater speed in the procedural distribution.
Copyright (c) 2024 Antonio Fernando Lavareda Jacob Junior, Matheus S. Marinato, Ewaldo E. C. Santana http://creativecommons.org/licenses/by-nc-nd/4.0
Brazilian initiatives for the entry and retention of women in the computing area: a systematic mapping of literature
Despite the growing advancement of initiatives aimed at gender equality in educational and professional circles, women still face obstacles to being accepted and recognized in areas where male activity predominates. This work aims to provide an updated overview of enabling projects that encourage the insertion and retention of women in the area of information technology (IT) in the Brazilian scenario. To this end, a systematic literature mapping (MSL) was carried out, analyzing and classifying 19 (nineteen) articles from a total of 642 (six hundred and forty-two) candidate studies. The results of this study reveal that the most common strategies to promote female insertion and retention focus on training actions, such as offering short courses, lectures and events involving the dissemination of information and qualification. It was observed that the quantitative ones focus on initiatives aimed at academic qualification at undergraduate level. It was also observed the presence of a minority directly focused on the job market. The main factors determining women's withdrawal from the IT area are: (i) influence from third parties (IT); (ii) educational aspects (AE); (iii) market aspects (MA); (iv) sociocultural limitations (LS); and (v) emotional and psychological aspects (AEP).
Initiatives for Admission and Permanence
Copyright (c) 2024 Kely Soares, Paula Vitória, Manuel Gonçalves da Silva Neto http://creativecommons.org/licenses/by-nc-nd/4.0
Simulation of coffee quality using ABIC criteria in discriminating regions with different spatial dependency structures
The paper addresses the application of discriminant analysis in proportion data with spatial dependence, focusing on the discrimination of specialty coffees in simulated microregions. Using Monte Carlo simulations, the study explores scenarios with varying levels of spatial dependence and link functions, comparing the effects of semivariogram models (spherical, exponential, and Gaussian) on the accuracy and precision of the analyses. The results indicate that quadratic discriminant analysis tends to be more effective for smaller samples (n=25), while strong spatial dependence favors the exponential model, improving specificity and reducing the false positive rate as n increases. When employing the complementary log-log link function, similar patterns are observed, with the Gaussian model performing best in larger samples. The study highlights the importance of selecting the appropriate model based on factors such as spatial dependence, sample size, and link function to ensure accurate results. The balance between specificity and sensitivity is crucial in the final decision, emphasizing the need for a careful approach to the analysis of proportion data with spatial dependence.
Copyright (c) 2024 Laryssa Ribeiro Calcagnoto http://creativecommons.org/licenses/by-nc-nd/4.0
A systematic review on the recognition of counterfeit banknotes using computer vision
Counterfeiting of banknotes is a real problem that affects the entire world, especially people with visual impairments and those without access to the financial system. In this study, a systematic literature review was conducted on the recognition of counterfeit banknotes with the aim of assessing the current state of the art, addressing the following research question: ``What computer vision approaches have been used to recognize counterfeit banknotes?''. As a result, this literature review selected 25 articles that underwent inclusion and exclusion criteria and were summarized based on the year of publication, the currency used, and the approach employed. Finally, an analysis of these results was performed, presenting perspectives for future research. Additionally, this article describes some datasets reported in the literature containing images of counterfeit and genuine banknotes available to the public.
Copyright (c) 2024 Kenad Wanderson Araujo Silva, Kelson Romulo Teixeira Aires, Laurindo de Sousa Britto Neto http://creativecommons.org/licenses/by-nc-nd/4.0
Identifying ISP’s customers profiles prone to Churn by employing TR-069 protocol data into ML algorithms
Internet Service Providers (ISPs) offer an essential communication infrastructure to support people everyday's tasks over the Internet and also the advent of new networking technologies. However, a challenge to ISPs is to reduce churn rate, also known as low customer retention rate. Despite efforts in the literature, ISPs remain short of tools to identify customers' churns. This paper proposes ChurnSense, a process to identify ISP customers profiles by employing machine learning techniques, contributing to the understanding of the churn problem. The processes comprises three steps: Collect, Pre-processing, and Analysis. Through it, Collect gathers data from TR-069 protocol, Pre-processing treats these data and Analysis finds clusters that define customers profiles, providing useful information to decision making about churn. A case study was conducted by employing real data from a regional ISP. The obtained results show 20.61% of customers devices with connection quality below the expected, being at risk of churn.
Copyright (c) 2024 Ricardo Tombesi Macedo http://creativecommons.org/licenses/by-nc-nd/4.0