Iberoamerican Journal of Applied Computing
The Iberoamerican Journal of Applied Computing is an academic and scientific journal, with an interdisciplinary character and with submission and publication of articles in continuous flow. The purpose of the journal is to disseminate the scientific production inherent to Applied Computing in the various areas of knowledge, valuing original research and reflections. ISSN 2237-4523
PARALLEL-PLATE CAPACITIVE SENSOR FOR SCREENING METHANOL ADULTERATION IN CACHAÇA
This paper reports the design, construction, and characterization of a low-cost parallel-plate capacitive sensor intended for screening gross methanol adulteration in cachaça. The transducer, built from copper-clad boards held by a 3D-printed PLA frame, operates without direct fluid–electrode contact: samples are sealed in low-density polyethylene pouches and inserted between the plates. Coupled to an NE555 timer in a stable configuration, the sensor converts changes in the effective permittivity of the medium into oscillation frequency, which is read by a microcontroller through pulse counting. Twenty-eight samples covering 0–30% (v/v) methanol in four commercial cachaças were measured between 15 °C and 26 °C, yielding 2,014 readings. Methanol addition consistently increased the oscillation frequency, consistent with the partial replacement of the high-permittivity aqueous fraction of the beverage by methanol. Univariate linear calibration, however, proved insufficient (best R² = 0.49): the signal is confounded by a thermal drift of up to −273 counts/°C and by baseline shifts of about 22% caused by dissolved sugars. The results support the hardware as a viable, low-cost data-acquisition platform for adulteration screening and characterize the interferences that any quantification model must compensate, indicating multivariate approaches as the natural next step. The system's selectivity limits and the gap between the tested concentrations and regulatory limits are explicitly discussed.
Direitos autorais (c) 2026 Iberoamerican Journal of Applied Computing
BIOMETRIC RECOGNITION OF COWS THROUGH MUZZLE IMAGES APPLIED TO ANIMAL MANAGEMENT
Individual cattle identification plays an important role in animal traceability, health management, productivity monitoring, and precision livestock farming. Conventional identification methods, such as ear tags and electronic devices, present limitations related to identifier loss and operational failures. In this context, bovine muzzle biometrics emerges as a promising alternative by exploring potentially unique anatomical patterns for each animal. This work investigates the feasibility of recognizing cows through biometric analysis of muzzle images using a computer vision and deep learning pipeline. The methodology includes image preprocessing, feature extraction with a convolutional neural network, generation of normalized embeddings, and similarity comparison using cosine distance. A preliminary experimental evaluation was conducted using a dataset of 150 images from 30 cows, with five images per individual. The proposed approach achieved a global accuracy of 92.7\% using a 1-nearest neighbor leave-one-out validation strategy. Dimensionality reduction analyses with PCA, t-SNE, and UMAP demonstrated clustering tendencies by individual, indicating the potential of bovine muzzle biometrics for automatic cattle identification and future applications in livestock management and animal traceability.
A CHARACTERIZATION OF ADVERSARIAL VULNERABILITIES IN NEURAL NETWORK ARCHITECTURES FOR IMAGE RECOGNITION
As Redes Neurais Convolucionais (CNNs) são fundamentais para a visão computacional moderna, particularmente em sistemas de reconhecimento de imagem implantados em cenários críticos de segurança, como a gestão de tráfego e estacionamento. Apesar do seu elevado desempenho preditivo sob condições nominais, estes modelos podem ser severamente afetados por perturbações adversárias que permanecem quase impercetíveis ao olho humano. Este artigo investiga a vulnerabilidade adversária de duas arquiteturas amplamente utilizadas, DenseNet121 e GoogLeNet (InceptionV3), na tarefa de reconhecimento de ocupação de lugares de estacionamento. Ambos os modelos foram treinados e avaliados numa amostra do conjunto de dados PKLot, que compreende imagens de três parques de estacionamento distintos sob diferentes condições meteorológicas. Exemplos adversários foram gerados utilizando o Método de Sinal de Gradiente Rápido (FGSM) e o ataque de Carlini & Wagner (C&W), sendo posteriormente apresentados aos modelos treinados. Os resultados experimentais mostram que, embora as redes originais alcancem elevada exatidão, precisão e revocação em dados limpos, as suas previsões degradam-se substancialmente sob perturbações adversárias, com uma redução acentuada nas estimativas corretas de ocupação. O ataque C&W induz consistentemente uma degradação de desempenho mais forte do que o FGSM, destacando fraquezas críticas em arquiteturas CNN de última geração quando expostas a entradas adversárias cuidadosamente elaboradas.
BRIDGING EFFICIENCY AND INTERPRETABILITY: EXPLAINABLE EDGE AI FOR CLINICAL DECISION-MAKING IN MEDICAL TELEMETRY
A crescente adoção da telemetria médica e do monitoramento remoto de pacientes tem transformado a assistência à saúde ao possibilitar a observação contínua de sinais fisiológicos em ambientes reais. Nesse contexto, a Edge AI emerge como uma abordagem promissora para o processamento local de dados, reduzindo a latência e a dependência de infraestruturas em nuvem, especialmente em cenários com recursos limitados. No entanto, a maioria dos modelos baseados em edge opera como “caixas-pretas”, limitando sua transparência e dificultando a adoção clínica. Este estudo apresenta uma revisão sistemática, seguindo as diretrizes PRISMA, sobre métodos de XAI aplicados a sistemas de Edge AI em telemetria médica. A análise evidencia um trade-off persistente entre eficiência computacional e interpretabilidade, com a maioria das abordagens privilegiando desempenho em detrimento da explicabilidade. Do ponto de vista clínico, os resultados ressaltam que dados de telemetria isolados são insuficientes para apoiar decisões médicas, sendo necessária a integração com raciocínio clínico e informações contextuais do paciente. Além disso, o estudo discute os riscos associados a vieses algorítmicos e cognitivos, bem como os desafios impostos por infraestrutura limitada e capacitação profissional, particularmente em sistemas de saúde em desenvolvimento. Os resultados revelam uma lacuna crítica no desenvolvimento de modelos integrados, leves e interpretáveis, adequados para implementação em ambientes reais. A superação dessa lacuna é essencial para avançar na criação de inteligência artificial confiável, escalável e clinicamente relevante na saúde.
A COMPUTATIONAL FRAMEWORK FOR EXPLAINABLE ASSISTED SIGNATURE SCREENING IN FORENSIC DOCUMENT ANALYSIS
This paper refactors a previously forensic-oriented methodological manuscript into an applied-computing article suitable for the Iberoamerican Journal of Applied Computing. The proposed contribution is an explainable computational framework for assisted screening of questioned signatures from static images, with emphasis on feature transparency, multisample comparison, and decision support rather than automated attribution. The framework organizes the workflow into image quality assessment, pre-processing, extraction of geometric and texture descriptors, multisample normalization, divergence-aware scoring, and structured reporting. A case-based demonstration is presented using one questioned signature and four reference signatures, for which the original technical execution indicated mixed evidence: broad-form compatibility coexisted with divergence in slant, ink density, baseline behavior, projection entropy, and anti-remake indicators. Instead of overclaiming authorship, the framework treats intermediate outputs as explainable evidence layers and supports an inconclusive result when the observed pattern does not justify strong attribution. The article contributes a computational perspective that aligns signature examination with current concerns in applied machine learning and forensic decision support, namely explainability, calibrated uncertainty, and reproducible reporting. The manuscript also adds a real bibliography grounded in signature verification, forensic handwriting comparison, and explainable decision support, addressing a limitation identified in the previous submission history.
CONSTRUCTION OF AN ETL PIPELINE FOR TRAFFIC DATA ANALYSIS USING PUBLIC SOURCES
O crescimento do fluxo de veículos nas cidades intensificou desafios relacionados a mobilidade urbana, exigindo soluções baseadas em dados para subsidiar o planejamento e a gestão do trânsito. Este trabalho apresenta o desenvolvimento de um pipeline de Extração, Transformação e Carregamento (ETL) para o processamento automatizado de dados públicos de trânsito da cidade de Curitiba. A solução foi implementada com tecnologias open-source — Python, Apache Airflow e MySQL — visando garantir escalabilidade, automação e integração entre diferentes fontes de dados. Os resultados demonstraram a eficácia do pipeline na coleta, transformação e armazenamento das informações, permitindo a geração de visualizações analíticas em Power BI que evidenciam padrões espaciais e temporais de ocorrências de trânsito. O estudo reforça a importância da Engenharia de Dados como instrumento de apoio a tomada de decisão e a otimização ao da mobilidade urbana.
Direitos autorais (c) 2025 Iberoamerican Journal of Applied Computing
A MACHINE LEARNING-BASED FRAMEWORK FOR DETECTING FINANCIAL FRAUDULENT TRANSACTIONS
O objetivo deste estudo foi desenvolver e avaliar modelos de aprendizado de máquina para a detecção de fraudes financeiras em transações, visando mitigar perdas econômicas e apoiar a tomada de decisões em instituições bancárias. A metodologia envolveu o uso do conjunto de dados público Credit Card Fraud Detection, composto por 284.807 transações, das quais apenas 492 foram fraudulentas (≈ 0,172%). Os algoritmos Random Forest e XGBoost foram testados, com e sem a aplicação da técnica de balanceamento SMOTE. A avaliação foi conduzida utilizando métricas como precisão, recall, F1-score, MCC, além das curvas ROC e de Precisão-Recall. Complementarmente, uma validação qualitativa foi realizada por meio de entrevistas com quatro especialistas do setor financeiro, a fim de analisar a aplicabilidade prática dos modelos. Os resultados mostraram que todos os modelos apresentaram alto desempenho geral, com áreas sob a curva ROC acima de 0,96. O XGBoost com SMOTE alcançou maior sensibilidade, com recall de 85% e 15 falsos negativos, porém com um aumento de falsos positivos (22). Por outro lado, o Random Forest sem SMOTE obteve melhor precisão (0,94) e a maior pontuação F1 (0,87), mas falhou na detecção de 18 casos de fraude. O Random Forest com SMOTE apresentou desempenho intermediário. A validação qualitativa confirmou a relevância dos modelos, com 75% dos especialistas priorizando a máxima detecção de fraudes, mesmo com um maior número de falsos alarmes, e 25% valorizando a redução de falsos alarmes, mesmo que isso signifique menor sensibilidade. Conclui-se que a escolha do modelo deve considerar o equilíbrio entre recall e precisão, alinhado às prioridades institucionais entre reduzir perdas financeiras e minimizar a sobrecarga operacional. O estudo também destaca limitações, como o uso de um conjunto de dados temporal específico e a ausência de otimização avançada de hiperparâmetros. Para trabalhos futuros, sugere-se explorar o ajuste de parâmetros, o aprendizado incremental e a validação em conjuntos de dados contemporâneos, visando maior robustez e aplicabilidade prática dos modelos.
AUTOMATED BREAST CANCER DIAGNOSIS WITH YOLO AND GENERATIVE AI
Breast cancer is a leading cause of mortality among women in Brazil, and human interpretation of mammograms still has limitations. This study evaluated a BI-RADS (Breast Imaging Reporting and Data System) classification pipeline that uses the YOLO (You Only Look Once) model for lesion segmentation and the ChatGPT language model for interpretation and assisted diagnosis. 120 images classified with the following distribution were used to evaluate the results: BI-RADS 3 (68 images), 4 (44 images), and 5 (images). Three approaches were given: (i) direct AI diagnosis; (ii) a pre-trained model without preprocessing getting used in the ChatGPT; and (iii) the same model with detailed morphological descriptions and enhancement filters (CLAHE and Sharpen), with ChatGPT producing the classification. The overall accuracies were 30%, 42%, and 70%, respectively. The methodology improved the accuracy of the results, although it still presents limitations in the model's ability to differentiate specific cases, or even creating a confusion in the interpretability, indicating space for future improvements.
PHP CALANGO FRAMEWORK AND DIGITAL TRANSFORMATION: AN INTRODUCTORY GUIDE FOR BEGINNERS
Calango is a lightweight PHP framework released in 2012 as an open-source project under the LGPL-3.0 license. It suits program language teaching, research applications, small or mid-size business websites, APIs, microservices, and prototypes. Calango works with the MVC structure, and its simplicity allows the developer greater freedom in their applications. This paper presents a tutorial introduction to help potential framework users get started through a step-by-step guideline.
SPECTRAL BAND SELECTION SUPPORTED BY THE PSO METAHEURISTIC FOR PREDICTION OF ALUMINUM CONTENT OF SOIL SAMPLES
Near-infrared (NIR) diffuse reflectance spectroscopy (DRS) is a technique that can be employed to make a model of soil nutrient prediction by correlating sample data to the respective reference value obtained by chemical analysis. Such data are organized as attributes of a set of high-dimensional records and making a model from these data involves difficulties that impair its performance. To overcome such difficulties, the use of evolutionary algorithms for Feature Selection has shown to be promising. The aim of this article is to identify, with Particle Swarm Optimization meta heuristic, the relevant wavelengths for predicting the aluminum content of soil samples from the Campos Gerais, PR, Brazil region. Results suggest that, for this scenario, few iterations and small swarm size provide the best subsets.
USE OF UAV AS A TOOL FOR DAM AUSCULTATION
This study explores the application of Unmanned Aerial Vehicles (UAVs) integrated with digital aerophotogrammetry as a tool for the auscultation of earth dams, focusing on the Chasqueiro Dam, located in southern Rio Grande do Sul. Using a rotary-wing UAV equipped with a high-resolution camera, cartographic products such as orthomosaics and Digital Surface Models (DSM) were generated, enabling the identification of critical elements such as piezometers and gullies on the downstream slope that were not detected during visual inspections. The data analysis allowed for the evaluation of the variability of slope inclinations and the conditions of the dam crest, confirming that its width and elevation are adequate for structural stability. The use of techniques such as georeferencing through materialized control points and processing with specialized software demonstrated high precision and efficiency in monitoring. This method proved to be a safe, fast, and cost-effective alternative compared to conventional approaches. It is concluded that employing advanced technologies in dams can mitigate the risks of structural failures and significantly improve monitoring and maintenance practices. Continued research is recommended to integrate additional sensors to expand and refine structural and environmental analyses.
Direitos autorais (c) 2024 Iberoamerican Journal of Applied Computing
USE OF COMPUTER VISION FOR INSECT RECOGNITION
The use of technologies such as computer vision has been a trend in agriculture. This technology can reduce the effort and time to obtain data about crops, improving production, crop health and bringing more sustainable practices. This article presents a literature review, among the topics covered, the article includes algorithms and computational techniques, including Convolutional Neural Networks (CNNs), YOLO (You Only Look Once) and Support Vector Machines (SVMs). These techniques are used in conjunction with various image processing techniques to detect and classify insects. Machine learning techniques have been shown to be effective in classifying insects based on characteristics such as color, size and position. Different datasets, such as IP102, Pest24, Xie1, Xie2 and Wang, were used to train and evaluate these algorithms. This article concludes the effectiveness of deep learning algorithms, particularly YOLOv5 and Faster R-CNN, in insect detection and classification, suggesting a promising future for automated insect monitoring.
INTEGRATION OF RFID TECHNOLOGY IN AGRICULTURAL TRACEABILITY TO OPTIMIZE CONTROL AND QUALITY OF PRODUCTS
Traceability in the agricultural chain is essential to guarantee the safety and quality of food products. The use of technologies such as Radio Frequency Identification (RFID) presents itself as a solution to improve this process, providing accurate and efficient tracking of products throughout the entire production chain. This study aimed to develop and validate an integrated structure using RFID in the traceability process in the soybean grain production chain. The results of this work included the detailed specification of the stages of the production process, using RFID for tracking and monitoring. The integration of RFID proved to be effective, allowing more accurate and efficient traceability, ensuring compliance with quality standards and industry regulations.
ASPHALT PAVEMENT CRACKING CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS
Asphalt pavements on roads are subject to cracking due mainly to overload and weather over time. Detecting the presence of cracking is an essential part of road maintenance systems. Traditional road defect detection methods are time-consuming, dangerous, labor-intensive, and subjective. An alternative is to use digital images of roads, collected by cars or unmanned aerial vehicles, to feed automated asphalt damage identification models. Thus, automated defect detection systems could quantify the quality of road surfaces and help prioritize and plan road network maintenance, thereby preserving and extending the useful life of roads. This work aims to investigate the use of machine learning for detecting and classifying cracking in road pavements using convolutional neural networks. Transfer learning and data augmentation techniques were used to build classification models. The results indicated that the model was able to identify cracking in the images of roads with an accuracy of 99%. When using the model to classify the cracking subtypes, the model presented an overall accuracy of 78%.
PLANT SPECIES RECOGNITION USING LEAF IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
The identification of plant species is essential in botany and has attracted the interest of researchers in the field of computer science. Such identification requires the assistance of botany experts and, due to the substantial number of species and the similarity among them, it can be time-consuming and subjective. To automate the process of plant identification, computer systems that capture and process plant images have been considered. These systems use machine learning and therefore require image samples for training and model construction. Among the techniques that can be used for machine learning, convolutional neural networks have shown promise due to their ability to use images without prior preprocessing and background information. This work investigates the use of machine learning through convolutional neural networks to identifying plant species. For this, a new dataset of images from 35 plant species were created, collecting images from an arboreal collection, and, using data augmentation, this dataset was expanded. This dataset was used to evaluate the accuracies of four convolutional neural network models. The better accuracy value was equal to 89%, when using the MobileNetV2 model.
APPLICATIONS OF COMPUTING IN GEOMORPHOLOGY STUDIES – A CASE STUDY
Nos últimos anos, a discussão em geomorfologia ganhou uma nova perspectiva, por meio de discussões interdisciplinares envolvendo geotecnologias, com foco na aplicação da computação, especialmente dos sistemas de informações geográficas (SIG). As geotecnologias têm demonstrado potencial para se estabelecerem como ferramentas de planejamento ambiental e gestão de riscos. Este estudo aprofunda o uso de geotecnologias na análise morfométrica da bacia hidrográfica do rio Pirapó (Maringá, Paraná, Brasil).
Direitos autorais (c) 2023 Iberoamerican Journal of Applied Computing
EVALUATION OF PYTHON LANGUAGE IN THE EARLY STAGES OF DIGITAL IMAGE PROCESSING
Our research group has been developing computational systems to assist in solving agricultural problems. Almost all systems were developed using the OpenCV library's support, using the Java language. However, in 2018, it was announced that the most widely used programming language in different fields of application, including the scientific field, is Python. Given this scenario, this work aimed to evaluate the Python language in image processing. For this purpose, a seed analysis computational system developed in the Java was reimplemented in Python, and in order to carry out a practical evaluation, we performed execution time experiments on both codes.
DETECTING DAMAGE IN ROADS USING CONVOLUTIONAL NEURAL NETWORKS
Roads are subject to damage such as cracks and potholes, mainly due to overload and weather over time. To ensure the longevity of roads, to prevent economic losses, and to improve safety, damage detection is crucial in pavement conditions monitoring. Damage detection is usually performed in the field by surveying and is a time-consuming and unsafe task. This paper presents an experimental study using machine learning and convolutional neural networks for detecting damage in roads. Transfer learning and data augmentation techniques were used to build classification models. Three models were evaluated, and the accuracy results on average were near 80%. Best accuracy results were achieved on detecting potholes, exudation, raveling and patches. The models did not perform well when distinguishing among subtypes alligator, transversal, and longitudinal of crack damage. For these types of damage, the models achieved an average accuracy bellow to 70%.
A DATA WAREHOUSE FOR UNCONTROLLED DECISION MAKING AND DECISION MAKING IN GREENHOUSES
A agricultura tem sido estudada por cientistas através de métodos empíricos com o objetivo de analisar a baixa lucratividade da produção e quais são os fatores que predominam essa finalidade. Para melhorar a produção agrícola surgiram as estufas agrícolas, que são ambientes fechados e possibilitam controle de condições necessárias para o desenvolvimento de plantas como temperatura, umidade e luminosidade. Uma estufa permite a construção de modelos e sistemas que auxiliam no melhoramento da produção e na qualidade da colheita. Sensores de temperatura, umidade e luminosidade são exemplos de componentes que recebem informações do meio e possibilitam maior controle destas variáveis. As leituras feitas pelos sensores em estufas trazem uma grande quantidade de informações que poderiam ser armazenadas e utilizadas para auxiliar no apoio à decisão e aprimoramentos de estufas a longo prazo, e não apenas serem verificados em tempo real. Uma forma de gerir grandes volumes de dados se dá pela utilização de Data warehouses (DW), que armazenam dados sumarizados, agregados e consolidados por longos períodos. Com base nesta realidade, este artigo apresenta uma análise de um DW que possibilita o apoio estratégico no planejamento e controle de estufas, armazenando informações de temperatura, umidade, luminosidade da estufa, bem como fatores externos como condições climáticas, investimentos, custos e produtividade. Palavras-chave: Estufas, Data Warehouse, Tomada de Decisão.
Direitos autorais (c) 2021 Iberoamerican Journal of Applied Computing
APPLICATION OF METADATA STANDARDS FOR GRAIN CLASSIFICATION
A produção agrícola no Brasil tem grande potencial de expansão, seja na área produzida, seja com o aumento da produtividade. A grande produção nacional de grãos faz com que seja elevado o número de comercializações envolvidas, a cada comercialização de grãos realizada no território nacional, os governos federal, estadual e municipal, em conjunto com o MAPA, Ministério da Agricultura, Pecuária e Abastecimento, determinam regras para ser seguido. Uma delas é que determina a classificação dos grãos destinados ao consumo humano, in natura ou processados. Cada grão deve seguir um padrão de classificação diferente, de acordo com regulamentos e portarias. Os metadados podem ser chamados de dados sobre os dados, ou seja, são uma forma de descrever os dados, facilitando assim a organização das informações. O padrão de metadados é apresentado de forma simples e compreensível, pois será utilizado em outros sistemas gerenciados, não necessariamente, por diferentes usuários e, como o objetivo deste trabalho é facilitar o processo de classificação de grãos, seria inviável a criação um sistema com necessidade de treinamento para usá-lo.
GENERATING SYNTHETIC 2019-NCOV SAMPLES WITH WGAN TO INCREASE THE PRECISION OF AN ENSEMBLE CLASSIFIER
The objective of this research is to present an alternative data augmentation technique based on WGAN to improve the precision in detection of positive 2019-nCoV samples, as well as compare it with other traditional data augmentation techniques, using a dataset composed of results of exams from individuals tested for COVID-19 in a hospital in Brazil. Given the data imbalance, we presented a gradient-boosted decision tree (GBDT) classifier with the preprocessed data in 13 different oversampling training scenarios, using SMOTE, ADASYN, Random Over Sampling, and WGAN to augment positive samples, as well as no augmentation at all. All over-sampling scenarios are set so that a mixture of real and synthetic samples is presented to the classifier. GBDT classifier was then trained in all scenarios with stratified k-fold (k=10), and its hyperparameters were optimized for the highest possible f1-measure with the random search algorithm for 20000 epochs. A hold-out test set was prepared by randomly removing an even number of really positive and negative samples from the training set and shuffling it for testing. GBDT classifier trained with 50% WGAN synthetic positive samples achieved a precision score of 0.967 on the test set, far outperforming all other scenarios, including similar mixture scenarios using other oversampling strategies. These results indicate that data augmentation with WGAN to oversample the minority class might be an alternative for traditional oversampling techniques, even improving a classifier’s precision.
NEGATIVE INFORMATION INTEGRATION IN PROBABILISTIC CLASSIFIERS
Este estudo apresenta um procedimento para integrar informação negativa quando da realização de inferência bayesiana robusta. No procedimento proposto, a informação negativa é codificada na forma de restrições lineares definidas sobre intervalos de probabilidade condicional que quantificam a incerteza no relacionamento entre as variáveis de um classificador. Durante a inferência, o classificador bayesiano robusto é convertido em um classificador credal. A topologia do classificador é a mesma qua de um classificador bayesiano simples e os problemas de otimização relacionados às inferências são resolvidos via otimização multilinear. Desde que o objetivo de uma inferência é computar o intervalo de probabilidades posteriores de cada classe, a integração da informação negativa permite que o procedimento de inferência obtenha intervalos mais precisos do que aqueles obtidos por um classificador bayesiano robusto. Isto pode facilitar o uso de um critério de decisão chamado dominância intervalar para a seleção de rótulos de classificação plausíveis e na definição de cursos de ação dado um objeto de interesse. A efetividade do procedimento é ilustrada com um exemplo.
A PRE-ANNOTATION TOOL FOR EVENT EXTRACTION IN CARDIOLOGY AMBULATORY TEXTS IN BRAZILIAN PORTUGUESE
Clinical texts offer medical information regarding the patients that are not present elsewhere, so creating tools that automatically extract this information can provide better and more personalized patient care. Creating these tools demands advanced machine learning techniques that require annotated data provided by burdensome annotation processes. Thus, we proposed a dictionary-based pre-annotation tool to diminish the burden of manually annotating all mentions over the texts. We developed a pre-annotation tool to help in our event annotation for cardiology ambulatory texts. The pre-annotation tool was based on a dictionary created during the annotator's training phase and four rounds of the annotation process. We annotated 126 texts with three annotators from the medicine course. We evaluated the pre-annotation performance based on the inter-annotator agreement, the annotation time, the annotation speed, and the pre-annotation coverage (the number of correct pre-annotations present in the gold standard). We concluded that our dictionary's refinement was beneficial to our pre-annotation; it raised the pre-annotation coverage while not reducing the inter-annotator agreement. We noticed that our annotation time decreased over the rounds, which is expected due to the annotators getting used to the annotation guideline and annotation tool over time.
MULTIDIMENSIONAL INFORMATION SYSTEMS: HIGHER EDUCATION DIGITAL SERVICES
Considering the technological scenario that we are inserted, information management moves beyond the influence on reducing product costs and productivity gains in the service sector as it also contributes to the strategic decision making. The present article aims to investigate the multidimensional information flow in information management in an educational institution. Methodologically this article is classified as an exploratory and descriptive study, through qualitative analysis. The result of data mapping points to the indication lack of order in informational flows, generating gap and breakage of the connections SI studied, which in turn comprise the managerial platforms. The results also show that the use of organizing elements of the flows contributes to multidimensional informational mobility.
Direitos autorais (c) 2020 Iberoamerican Journal of Applied Computing
ASSESSING THE WEATHERING OF BASALTIC AGGREGATES USING IMAGE TEXTURE ANALYSIS
Mineral aggregates are the most mined materials in the world. Aggregates are a component of composite materials such as asphalt concrete, used mainly in highway´s paving. The importance of aggregate is because it serves as reinforcement to add strength to the overall composite material. Aggregates should be obtained from non-weathered rocks. Aggregates from weathered rocks commonly present low resistance to abrasion and compression, especially under conditions of frequently repeated vehicle's load, such as on highways. This paper presents an evaluation of image texture analysis when applied to detect the presence of weathering in aggregates. An evaluation of the texture analysis using histograms, entropy, local binary patterns and local phase quantization as descriptors aiming to identify weathering in basaltic aggregates is presented. These texture descriptors and the Naive Bayes, IBK, J48 and Multilayer Perceptron machine learning algorithms were used to detect the presence of weathering in aggregates. The results obtained indicated that red channel stands out as descriptor to differentiate weathered from non-weathered aggregates with an average error around 4%.
COMPARATIVE ANALYSIS BETWEEN GEOLOGICAL MODELLING METHODS
Several stages of a mining enterprise depend on the existence of a geological model. Each of these steps is conducted by professionals who based on this model, perform tasks, and make decisions and plans. However, it is built from a sample dataset, and is therefore subject to uncertainties, which can be reduced with the accuracy of the data and the choice of a modelling method appropriate to each type of deposit. The purpose of this work was to analyze comparatively three methods for the elaboration of digital solid models, which involved manual, geostatistical and implicit modelling methodologies .The data used come from surveys of an iron ore deposit. The lithologies modeled were hematite and itabirite. The manual modelling was built through the parallel section method, which is based on interpretations that depend on the geological knowledge and the experience of the professional. However, the main problem that arises in this context is that many interpretations can be viable, making the method subjective. The elaboration of the geostatistical modelling was through the indicator kriging technique, which partly depends on interpretations, but the result is based on statistical principles, being less subjective than the manual method. Finally, implicit modelling, which uses radial-based functions, is performed “automatically,” so it does not offer the opportunity to add professional knowledge and experience. The results obtained in this research, using the mentioned methodologies, showed similarities in the determination of the models between the lithological contacts, but there were significant divergences in quantitative terms. Thus, it can be concluded that a fast automatic model can help in the interpretation or point out regions where more samples are needed, and that there may be interactivity between the professional who performs the interpretation of the sample data and the methodologies used, so that they can assist in the geological modelling process.
ON SPEEDING UP K-MEANS CLUSTERING USING GRAPHICS PROCESSING UNITS
Due to advancements in technology, modern farms work differently than those from past. Over the years, there was an increase in the number of data collected by sensors, cameras and other systems. In this scenario, the implementation of data mining on parallel computing systems is crucial for ensuring system scalability and better performance as data continues to grow. This paper presents a case study on a parallel implementation of the K-means clustering. Clustering has been used in many applications including image processing, information retrieval and climatology. However, k-means clustering is knowing to be computationally expensive when applied to obtain clusters from large datasets. The parallel k-means implementation is target to general purpose graphics processing units. In order to implement the k-means clustering to this parallel architecture and to provide a better software platform to data science research, the Weka k-means implementation were chosen and adapted. First, a profiler was used to identify the most time-consuming portions of code. By using a profiler, it was possible to verify a 95.57% reduction in the number of lines of code that would need to be analyzed to rewrite the code. After, a parallel k-means clustering was implemented and evaluated. The results show that using the parallel k-means clustering and graphics processing units, data mining results can be achieved in reduced times. The speedup achieved was up to 26 when using all available execution cores.
EVALUATION OF LOW LEVEL FEATURE-BASE METHODS FOR CREATING PANORAMIC MOSAICS OF CROP IMAGES
Unmanned aerial vehicles are making farming more efficient, by allowing farmers to manage crops using georeferenced images. Images acquired from aerial vehicles cameras area used to build image mosaics of the crops. Image mosaicking is the alignment of multiple images into larger compositions which represent portions of a 3D scene. Several image mosaicking algorithms have been proposed over the last two decades. Among all, low-level feature detecting algorithms may be invariant to scale and rotation, among other transformations that commonly occur in agricultural images obtained by unmanned aerial vehicles. This study aimed to evaluate low level feature-based mosaicking methods using agricultural images obtained by unmanned aerial vehicles. The Harris corner detector, the FAST corner detector, the SIFT feature detector and the SURF detector were evaluated according to the computational performance and the quality of the generated mosaics. To assess computing performance, were considered factors such as the detected features average per image, the number of images used to compose the mosaic and the processing times. To assess quality, the mosaics generated by each method were used to estimate the Asian soybean rust severity and a comparison with the commercial software Pix4Dmapper was performed. Regarding quality, there was no significant difference and all methods proved to be on the same level. SURF detector used, on average, only 33.1% of the input images to compose the mosaics. The Harris corner detector achieved the best computing performance results. However, in its final mosaic, the usage of the input images was below than 50%. The FAST corner detector presented the best utilization of the input images, but significant discontinuities of objects where observed in the overlap regions of the resulting mosaics. Besides, the FAST presented the worst computing performance. The SIFT feature detector achieved the second-best processing time, the second-best utilization of the input images and built mosaics without discontinuities in overlapped regions.
AUTOMATION OF THE ANALYSIS OF SOIL PROPERTIES FROM AN ELECTRICAL CONDUCTIVITY SENSOR USING THE ARDUINO MICROCONTROLLER
Due to the environmental impacts that improper handling causes to the soil, the definition of production areas using tools such as soil electrical conductivity sensors helps to perform a faster analysis of the soil type and help in the decision to be taken for soil correction. The aiming of this research was to investigate the correlation between the conductivity of the soil and its properties, in order to perceive the viability of the application of the electrical conductivity sensors in making decisions for soil correction, seeking to automate the data collection process aimed at greater efficiency in the same chemical analysis. In other words, that automation of data collection tends to work with the needs of the precision agriculture, such as looking for ways to spend less in agricultural activities and setting up ideal terms for agricultural crops. The results are promising, because they show that the sensor used with the Arduino microcontroller to measure electrical resistance of the soil may be a useful tool for measurements of some properties of the soil.
Direitos autorais (c) 2018 Iberoamerican Journal of Applied Computing
DIGITAL ROOT STUDY AND ITS USE IN THE GENERATION OF RANDOM NUMBER SEQUENCES
This article presentes an overview of the digital root, demonstrating its functions, methods and applications. This article also presentes a study about the application of the digital root in the generation of random sequences of numbers. For this, an algorithm for generation of random sequence of numbers is proposed, whose results as to its usability, using a battery of tests, are presented in this article.