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.
Copyright (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.
The CHARACTERIZATION OF ADVERSARIAL VULNERABILITIES IN NEURAL NETWORK ARCHITECTURES FOR IMAGE RECOGNITION
Convolutional Neural Networks (CNNs) are central to modern computer vision, particularly in image recognition systems deployed in safety-critical scenarios such as traffic and parking management. Despite their high predictive performance under nominal conditions, these models can be severely affected by adversarial perturbations that remain almost imperceptible to the human eye. This paper investigates the adversarial vulnerability of two widely used architectures, DenseNet121 and GoogLeNet (InceptionV3), in the task of parking-space occupancy recognition. Both models were trained and evaluated on a subset of the PKLot dataset, comprising images from three distinct parking lots under different weather conditions. Adversarial examples were generated using the Fast Gradient Sign Method (FGSM) and the Carlini & Wagner (C&W) attack and subsequently presented to the trained models. The experimental results show that, although the original networks achieve high accuracy, precision, and recall on clean data, their predictions degrade substantially under adversarial perturbations, with a marked reduction in correct occupancy estimates. The C&W attack consistently induces stronger performance degradation than FGSM, highlighting critical weaknesses in state-of-the-art CNN architectures when exposed to carefully crafted adversarial inputs.
BRIDGING EFFICIENCY AND INTERPRETABILITY: EXPLAINABLE EDGE AI FOR CLINICAL DECISION-MAKING IN MEDICAL TELEMETRY
The increasing adoption of medical telemetry and remote patient monitoring has transformed healthcare by enabling continuous observation of physiological signals in real-world environments. In this context, Edge Artificial Intelligence (Edge AI) has emerged as a promising approach for processing data locally, reducing latency and dependence on cloud infrastructures, particularly in resource-constrained settings. However, most edge-based models operate as black boxes, limiting their transparency and hindering clinical adoption. This study presents a systematic review, following PRISMA guidelines, of Explainable Artificial Intelligence (XAI) methods applied to Edge AI systems in medical telemetry. The analysis highlights a persistent trade-off between computational efficiency and interpretability, with most approaches favoring performance over explainability. From a clinical perspective, the findings emphasize that telemetry data alone is insufficient to support medical decision-making, requiring integration with clinical reasoning and contextual patient information. Additionally, the study discusses the risks associated with algorithmic and cognitive biases, as well as the challenges posed by limited infrastructure and professional training, particularly in developing healthcare systems. The results reveal a critical gap in the development of integrated, lightweight, and interpretable models suitable for real-world deployment. Addressing this gap is essential for advancing trustworthy, scalable, and clinically relevant artificial intelligence in healthcare.
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
The growth of vehicle flow in cities has intensified challenges related to urban mobility, requiring data-driven solutions to support traffic planning and management. This work presents the development of an Extraction, Transformation, and Load (ETL) pipeline for the automated processing of public transit data from the city of Curitiba. The solution was implemented with open-source technologies—Python, Apache Airflow, and MySQL—aiming to ensure scalability, automation, and integration between different data sources. The results demonstrated the pipeline’s effectiveness in collecting, transforming, and storing information, enabling the generation of analytical visualizations in Power BI that highlight spatial and temporal patterns of traffic occurrences. The study reinforces the importance of Data Engineering as a tool to support decision-making and the optimization of urban mobility.
Copyright (c) 2025 Iberoamerican Journal of Applied Computing
A MACHINE LEARNING-BASED FRAMEWORK FOR DETECTING FINANCIAL FRAUDULENT TRANSACTIONS
The objective of this study was to develop and evaluate machine learning models for the detection of financial fraud in transactions, aiming to mitigate economic losses and support decision-making in banking institutions. The methodology involved the use of the public Credit Card Fraud Detection dataset, consisting of 284,807 transactions, of which only 492 were fraudulent (≈ 0.172%). The algorithms Random Forest and XGBoost were tested, with and without the application of the SMOTE balancing technique. The evaluation was conducted using metrics such as precision, recall, F1-score, MCC, in addition to ROC and Precision–Recall curves. Complementarily, a qualitative validation was carried out through interviews with four financial sector specialists, in order to analyze the practical applicability of the models. The results showed that all models presented high overall performance, with areas under the ROC curve above 0.96. XGBoost with SMOTE achieved greater sensitivity, with a recall of 85% and 15 false negatives, but with an increase in false positives (22). On the other hand, Random Forest without SMOTE obtained better precision (0.94) and the highest F1-score (0.87), but failed to detect 18 fraud cases. Random Forest with SMOTE showed intermediate performance. The qualitative validation confirmed the relevance of the models, with 75% of the specialists prioritizing maximum fraud detection, even with more false alarms, and 25% valuing the reduction of false alarms, even at the cost of lower sensitivity. It is concluded that the choice of the model should consider the balance between recall and precision, aligned with institutional priorities between reducing financial losses and minimizing operational overload. The study also highlights limitations, such as the use of a specific temporal dataset and the absence of advanced hyperparameter optimization. For future work, it is suggested to explore parameter tuning, incremental learning, and validation on contemporary datasets, aiming for greater robustness and practical applicability of the models.
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
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.
Copyright (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
In recent years, the discourse in geomorphology has taken on a new perspective, through interdisciplinary discussions involving geotechnologies, focusing on the application of computing, especially geographic information systems (GIS). Geotechnologies have demonstrated the potential to establish themselves as tools for environmental planning and risk management. This study delves the using of geotechnologies in the morphometric analysis of the hydrographic basin of Pirapó River (Maringá, Paraná, Brazil).
Copyright (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
Agriculture has been studied by scientists using empirical methods to analyze the low profitability of production and which factors predominate for this purpose. To improve agricultural production, agricultural greenhouses were created, which are closed environments and allow for the control of conditions necessary for the development of plants, such as temperature, humidity, and light. A greenhouse allows the construction of models and systems that help to improve production and crop quality. Temperature, humidity, and light sensors are examples of components that receive information from the environment and enable greater control of these variables. The readings taken by sensors in greenhouses bring a wealth of information that could be stored and used to assist in decision support and long-term greenhouse improvements, not just be verified in real-time. One way to manage large volumes of data is through the use of Data warehouses (DW), which store summarized, aggregated, and consolidated data for long periods. Based on this reality, this article presents an analysis of a DW that enables strategic support in the planning and control of greenhouses, storing information on temperature, humidity, greenhouse luminosity, as well as external factors such as weather conditions, investments, costs, and productivity. Keywords: Greenhouses, Data Warehouse, Decision Making.
Copyright (c) 2021 Iberoamerican Journal of Applied Computing
APPLICATION OF METADATA STANDARDS FOR GRAIN CLASSIFICATION
Agricultural production in Brazil has great potential for expansion, either in the area produced or with the increase in productivity. The great national production of grains causes the number of commercializations involved to be high, for each commercialization of grains carried out in the national territory, the federal, state and municipal governments, together with MAPA, Ministry of Agriculture, Livestock and Supply, determine rules to be followed. One of them is that it determines the classification of grains destined for human consumption, either in natura or processed. Each grain must follow a different classification standard, according to regulations and ordinances. Metadata can be called data about data, that is, it is a way of describing data, thus facilitating the organization of information. The metadata standard is presented in a simple and understandable way, since it will be used in other systems managed, not necessarily, by different users and, since the purpose of this work is to facilitate the grain classification process, it would be impracticable to create a system with need for training to use it.
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
This study presents a procedure for integrating negative information into robust Bayesian inference. In the proposed procedure, negative information is codified as linear restrictions of the conditional probability intervals that quantify the uncertainty in the relationship between the classifying variables. During the inference, the robust Bayesian classifier is converted into a credal classifier. The classifier topology is the same as the Naive Bayesian classifier, and the optimization problems related to the inferences are solved by multilinear optimization. Since the objective of an inference is to compute the posterior probability interval of each class, by integrating the negative information, the inference procedure might obtain more precise intervals than those obtained by a robust Bayesian classifier. This might favor the use of the decision criterion called interval dominance when selecting plausible labels and defining a course of action for a given object of interest. The effectiveness of the procedure is illustrated with an example.
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.
Copyright (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.
Copyright (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.