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The dropout of Brazilian students from higher education is a subject that has been well explored, where high rates of students who drop out are verified. However, despite the vast literature, the problems arising from student's dropout still have no solution since dropout itself is an unsolved problem. This research aims to present a classification via decision trees to predict the evasion of Engineering course students in Brazil. To reach this objective, exploratory field research was conducted, where data was collected employing surveys directed to the students, enabling the elaboration of a classificatory decision tree with the C4.5 algorithm. The survey sample consisted of 91 valid answers. The results were analyzed with the RapidMiner tool and presented a decision tree with 86.81% accuracy. Among the main factors preventing dropout is interaction with professors, the course curriculum, and issues related to mental well-being. [ABSTRACT FROM AUTHOR]

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death., (© 2024. The Author(s).)

Applying the upcoming technologies in agriculture has been a major economic, environmental and social challenge for scientists and farmers. In order to overcome such challenge, this study evaluated the advantages and limitations of using geostatistics and machine learning for soil mapping in agricultural practices and soil surveys. The study occurred in Tocantins State, Brazil, and consisted into seven areas with a total extension of 17.24 km², 222 meters regular gridded resulting in one-point sampling per 0.0493 km² of five randomly sampled cores within a 1 m circle radius. It was collected 332 georeferenced soil samples at 0-20 cm depth using an auger and then, soil laboratory analyses performed. Afterward, liming rate maps were originated from the predicted soil attributes clay, cation exchange capacity and base saturation comparing four methods: ordinary kriging, random forest, cubist, support vector machine and the best model results of each soil attribute. Evaluating the methods, the Pearson's index presented strong results for soil attributes predicted by random forest and ordinary kriging. Machine learning methods can be successfully applied for soil mapping in agricultural practices and soil surveys using less soil samples rather than geostatistical framework. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the expansion of oil exploration in deep waters, assessing the risks associated with offloading operations becomes essential in preventing accidents that may cause huge environmental disasters. In this paper, the system that composed of a turret-moored floating production storage and offloading (FPSO) connected to a conventional shuttle tanker, which is assisted by a tug boat to maintain its position during an offloading operation, will be studied. Using environmental data collected over a period of 6 years, from 2004 to 2009, from the Campos Basin in Brazil, the equilibrium positions of the system were calculated, considering its constraints (operational criteria defined by Petrobras) and verifying the stability of those equilibrium points. The hydrodynamic and aerodynamic static forces were calculated using models validated in the literature. Dynamic effects and oscillations are taken into account by adding safety margins to the operational sectors. With this analysis, we calculated the FPSO heading probabilities during an offloading operation and the expected downtime of operation in Campos Basin. We concluded that the downtime of the offloading operation with a conventional shuttle tanker is close to that with a dynamic positioned (DP) shuttle tanker (10% downtime). Furthermore, the results from the stability analysis were used to generate a simplified set of rules to classify the environmental conditions into four classes of operational risk by applying an unbiased decision tree. This method obtains practical rules based on measurements of wind, wave, and current, allowing the operator to quickly evaluate the risk level before starting the operation. [ABSTRACT FROM AUTHOR]

This work proposes a workflow through numerical simulations to determine the link between lithofacies and electrofacies. The correspondence between them was studied using the gamma-ray, resistivity, neutron porosity, bulk density, and transit time geophysical well logs. The lithofacies information, derived from conventional cores analysis, was used as the target in the process. On the other hand, the classification of lithofacies using logs has been performed by several authors using a wide variety of approaches. Thus, records from boreholes A10 (reference) and A03 (blind test) of a carbonate reservoir in Campos Basin, Southeastern Brazil, were used to develop this study. The cross-plot between the bulk density and gamma-ray logs suggests five electrofacies, which coincide with the five lithofacies proposed in the literature. Throughout the work, Pearson's correlation coefficient (R) and the mean square error (MSE) were used as statistical parameters to assess the goodness of fit. The gamma-ray electrofacies were better adjusted to the lithofacies of each well (R = 0.74 and 0.65, MSE = 0.63 and 1.36), which is recognized by publications that show this log with the best performance in identifications of this type. After that, the wavelet transforms of all normalized logs showed only a good correlation between lithofacies and the transformed signal to the gamma-ray log (R = 0.83 and 0.64, MSE = 0.01 and 0.04). This result also indicates that the electrofacies derived from the normalized and wavelet-transformed gamma ray log exhibited better results than those resulting directly from the simple gamma ray log in both wells compared to the lithofacies. Both results also show that gamma-ray electrofacies are the best input to the simulation process. The decision tree approach modeling then performed the estimates only with the normalized and wavelet-transformed gamma ray log, revealing that this method worked reasonably well in both reference and blind test wells (R = 0.79 and 0 0.63, MSE = 0.02 and 0.06). Outcomes were better with all normalized and wavelet transformed logs for both wells (R = 0.93 and 0.95, MSE = 0.006 and 0.007), with the blind test well having better R but a slightly higher MSE. So, this approach allows to derive lithofacies from electrofacies when a series of steps are taken: a) select the logs that have the most relevant lithological information; b) normalize lithology and logs on the same basis; c) transform the logs by wavelet, and d) use these results as input to the inverse process with the decision tree algorithm. [Display omitted] • Relating the concepts of lithofacies and electrofacies. • Using wavelet transform to filter real data. • Decision tree approach to assess the lithofacies. • Albian carbonate reservoir. • Campos Basin, Southeastern Brazil. [ABSTRACT FROM AUTHOR]

BACKGROUND Knowledge of the sensory profile of coffee quality, associated with genetic and environmental factors, is of utmost importance for the international market, as well as for the productive sector. In this context, the goal of this study was to classify the quality of Coffea arabica L., cv. Yellow Bourbon, according to different scores obtained through sensory evaluations based on the Specialty Coffee Association of America protocol ( SCAA), and by means of decision trees resulting from applying the CHAID method (chi-square automatic interaction detection). To that end, we used a database with the sensory characteristics of cv. Yellow Bourbon and the environmental characteristics of the Mantiqueira de Minas region, State of Minas Gerais, Brazil. RESULTS The method used exhibited promising results regarding accuracy and success rates in order to discriminate coffee sensory quality as a function of the production environment. The results obtained clearly show the effect of the coffee growing environment on the Yellow Bourbon variety, resulting in notable sensory differences in the beverage. CONCLUSION It was possible to discriminate cv. Yellow Bourbon coffee samples, the sensory evaluations of which resulted in scores of ≥88 points, which are associated with growing environments at altitudes of ≥1200 m. © 2015 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência & Saúde Coletiva is the property of Associacao Brasileira de Pos-Graduacao em Saude Coletiva and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000-2010 using high-resolution Landsat satellite imagery. Combining spectral mixture analysis, normalized difference fraction index, and knowledge-based decision tree classification, we mapped and assessed the accuracy to quantify forest (0.97), deforestation (0.85) and forest degradation (0.82) with an overall accuracy of 0.92. We show that 169,074 km2 of Amazonian forest was converted to human-dominated land uses, such as agriculture, from 2000 to 2010. In that same time frame, an additional 50,815 km2 of forest was directly altered by timber harvesting and/or fire, equivalent to 30% of the area converted by deforestation. While average annual outright deforestation declined by 46% between the first and second halves of the study period, annual forest degradation increased by 20%. Existing operational monitoring systems (PRODES: Monitoramento da Florestal Amazônica Brasileira por Satélite) report deforestation area to within 2% of our results, but do not account for the extensive forest degradation occurring throughout the region due to selective logging and forest fire. Annual monitoring of forest degradation across tropical forests is critical for developing land management policies as well as the monitoring of carbon stocks/emissions and protected areas. [ABSTRACT FROM AUTHOR]

Abstract: MODIS 250-m NDVI and EVI datasets are now regularly used to classify regional-scale agricultural land-use practices in many different regions of the globe, especially in the state of Mato Grosso, Brazil, where rapid land-use change due to agricultural development has attracted considerable interest from researchers and policy makers. Variation exists in which MODIS datasets are used, how they are processed for analysis, and what ground reference data are used. Moreover, various land-use/land-cover classes are ultimately resolved, and as yet, crop-specific classifications (e.g. soy–corn vs. soy–cotton double crop) have not been reported in the literature, favoring instead generalized classes such as single vs. double crop. The objective of this study is to present a rigorous multiyear evaluation of the applicability of time-series MODIS 250-m VI data for crop classification in Mato Grosso, Brazil. This study shows progress toward more refined crop-specific classification, but some grouping of crop classes remains necessary. It employs a farm field polygon-based ground reference dataset that is unprecedented in spatial and temporal coverage for the state, consisting of 2003 annual field site samples representing 415 unique field sites and five crop years (2005–2009). This allows for creation of a dataset containing “best-case” or “pure” pixels, which we used to test class separability in a multiyear cross validation framework applied to boosted decision tree classifiers trained on MODIS data subjected to different pre-processing treatments. Reflecting the agricultural landscape of Mato Grosso as a whole, cropping practices represented in the ground reference dataset largely involved soybeans, and soy-based classes (primarily double crop ‘soy-commercial’ and single crop ‘soy-cover’) dominated the analysis along with cotton and pasture. With respect to the MODIS data treatments, the best results were obtained using date-of-acquisition interpolation of the 16-day composite VI time series and outlier point screening, for which five-year out-of-sample accuracies were consistently near or above 80% and Kappa values were above 0.60. It is evident that while much additional research is required to fully and reliably differentiate more specific crop classes, particular groupings of cropping strategies are separable and useful for a number of applications, including studies of agricultural intensification and extensification in this region of the world. [Copyright &y& Elsevier]

• Sugarcane has become the most prominent crop in Brazil. • The use of production environments is an alternative for specific soil management. • Machine learning can be used to define production environments. • The decision tree and geostatistics allow the creation of production environment maps. Sugarcane is one of the most important crops in the Brazilian agricultural market. Techniques that aim to increase the productivity and quality of raw materials, such as localized management, have been applied manually for many years by farmers and have great potential. This study aimed to determine sugarcane production environments using a reduced number of low-cost variables through the machine learning technique. The experiment was conducted in Guatapará, São Paulo State, Brazil. Initially, the database consisted of thirty variables, and six agronomic criteria were selected, three related to soil management and three to pedogenetic processes. The descriptive statistics was performed to understand the behavior of the data, followed by the stepwise regression to determine which variables would be useful to the model. Subsequently, a multicollinearity test and a decision tree were applied. A confusion matrix was prepared to assess the efficiency of the model. The variables related to soil formation factors, in particular sand, were chosen to determine the production environments. The stepwise regression was efficient in selecting the variables, while the decision tree was effective in determining the environments, with a satisfactory accuracy of 75% and the generation of more continuous management environments in the cultivation area. [ABSTRACT FROM AUTHOR]

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context. [ABSTRACT FROM AUTHOR]

The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1-score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by-case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

This study aimed to understand the perception of drought among farmers, in order to support decision-making in the water allocation process. This study was carried out in the Tabuleiro de Russas irrigated perimeter, in northeast Brazil, over the drought period of 2012–2018. Two analyses were conducted: (i) drought characterization, using the Standardized Precipitation Index (SPI) based on drought duration and frequency criteria; and (ii) analysis of farmers' perceptions of drought via selection of explanatory variables using the Random Forest (RF) and the Decision Tree (DT) methods. The 2012–2018 drought period was defined as a meteorological phenomenon by local farmers; however, an SPI evaluation indicated that the drought was of a hydrological nature. According to the RF analysis, four of the nine study variables were more statistically important than the others in influencing farmers' perception of drought: number of cultivated land plots, farmer's age, years of experience in the agriculture sector, and education level. These results were confirmed using DT analysis. Understanding the relationship between these variables and farmers' perception of drought could aid in the development of an adaptation strategy to water deficit scenarios. Farmers' perception can be beneficial in reducing conflicts, adopting proactive management practices, and developing a holistic and efficient early warning drought system. [ABSTRACT FROM AUTHOR]