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Predicting mortality in COVID‐19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India, Brazil, Russia, France, China, and Bangladesh. Data were gathered between December 31, 2019, when COVID‐19 began, and March 31, 2021. The study used 457 observations with 4 variables: daily confirmed cases, daily deaths, daily mortality rate, and date. To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. The autoregressive integrated moving average (ARIMA) model with the lowest AIC value for each nation is found through time series analysis. By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. Additionally, SVM analyzes every country and calculates its R2 and root‐mean‐square error (RMSE). The lowest RMSE value is used to compare all of the time series and machine learning models. According to the comparison table, SVM provides a more accurate model to predict the mortality rate of the seven countries, with the lowest RMSE value. During the study period, mortality rates increased in Brazil and Russia and decreased in the United States, India, France, China, and Bangladesh, according to the comparison value of RMSE in this study. Furthermore, this paper shows that SVM outperforms all other models in terms of performance. According to the author's analysis of the data, SVM is a machine learning technique that can be used to accurately predict mortality in a pandemic scenario. [ABSTRACT FROM AUTHOR]

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

For decades, the data-centric digital circuits of global finance have been driving financial markets toward algorithmic management. The penetration of algorithms into finance dates back to the 1980s, well before becoming a significant trend in Internet and social media companies. AI is now considered the new frontier of this process. This paper examines Brazil, the most important financial center in Latin America, as a case study to explore Machine Learning applications in capital markets, providing new evidence on the penetration of automated trading systems in the region. Grounded in a historical overview of the digitalization of capital markets in Brazil, the paper draws on the STS definition of infrastructures and the political economy of digitalized finance to analyze AI as an emerging financial infrastructure. Beyond its physical and material aspects, this approach also addresses how digitalization and AI relate to financialization and the emergence of new financial risks. [ABSTRACT FROM AUTHOR]

The global energy market has significantly developed in recent years; proof of this is the creation and promotion of smart grids and technical advances in energy commercialization and transmission. Specifically in the Brazilian context, with the recent modernization of the electricity sector, energy trading prices, previously published on a weekly frequency, are now available on an hourly domain. In this context, the definition and forecasting of prices become increasingly important factors for the economic and financial viability of energy projects. In this scenario of changes in the local regulatory framework, there is a lack of publications based on the new hourly prices in Brazil. This paper presents, in a pioneering way, the Dynamic Time Scan Forecasting (DTSF) method for forecasting hourly energy prices in Brazil. This method searches for similarity patterns in time series and, in previous investigations, showed competitive advantages concerning established forecasting methods. This research aims to test the accuracy of the DTSF method against classical statistical models and machine learning. We used the short-term prices of electricity in Brazil, made available by the Electric Energy Commercialization Chamber. The new DTSF model showed the best predictive performance compared to both the statistical and machine learning models. The DTSF performance was superior considering the evaluation metrics utilized in this paper. We verified that the predictions made by the DTSF showed less variability compared to the other models. Finally, we noticed that there is not an ideal model for all predictive 24 steps ahead forecasts, but there are better models at certain times of the day. [ABSTRACT FROM AUTHOR]

The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun's path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm. [ABSTRACT FROM AUTHOR]

In the Brazilian state of São Paulo, contaminated sites (CSs) constitute threats to health, environment and socioeconomic situation of populations. Over the past two decades, the Environmental Agency of São Paulo (CETESB) has monitored these known CSs. This paper discusses the produced dataset through digitising the CETESB reports and making them accessible to the public in English. The dataset reports on qualitative aspects of contamination within the registered sites (e.g., contamination type and spread) and their management status. The data was extracted from CETESB reports using a machine-learning computer vision algorithm. It comprises two components: an optical character recognition (OCR) engine for text extraction and a convolutional neural network (CNN) image classifier to identify checked boxes. The digitisation was followed by harmonisation and quality assurance processes to ensure the consistency and validity of the data. Making this dataset accessible will allow future work on predictive analysis and decision-making and will inform the required policy-making to improve the management of the CSs in Brazil. [ABSTRACT FROM AUTHOR]

Paper aims: This article investigates the worldwide trend of growth in the number of recalls, as well as in the number of products involved in each campaign. Originality: To investigate these facts, a study of the automotive recall was developed, comprising Brazil, the European Union, and the United States of America. Research method: Due to the different availabilities between the locations, search tools and software were developed to obtain and group hidden data from 2010 to 2019. Main findings: In this work, the impacts of the recall were analyzed using three categories of algorithms: clustering, classification, and regression. Analyzes were made about the results obtained and discussions were built about the importance of applying the machine learning technique. Implications for theory and practice: The use of search tools and software to obtain and group hidden data in databases and opens the opportunity for new research in various areas. [ABSTRACT FROM AUTHOR]

Paper aims: To predict monthly corn, soybean, and sugar spot prices in Brazil using hybrid forecasting techniques. Originality: This study combines the Singular Spectrum Analysis with different forecasting methods. Research method: This paper presents a set of hybrid forecasting approaches combining Singular Spectrum Analysis (SSA) with different univariate time series methods, ranging from complex seasonality methods to machine learning and autoregressive models to predict monthly corn, soybean, and sugar spot prices in Brazil. We carry out a range of out-ofsample forecasting experiments and use a comprehensive set of forecast evaluation metrics. We contrast the performance of the proposed approaches with that of a range of benchmark models. Main findings: The results show that the proposed hybrid models present better performances, with the hybrid SSA-neural network approach providing the most competitive results in our sample. Implications for theory and practice: Forecasting agricultural prices is of paramount importance to assist producers, farmers, and the industry in decision-making processes. [ABSTRACT FROM AUTHOR]

Studies comparing large-scale assessment data among educational systems have been an important tool for understanding the differences in how education is delivered worldwide. Many of these studies do not go beyond reporting average student scores in a particular educational system. A more unbiased analysis would avoid the simple use of gross performance and consider educational system contexts. A common approach is to estimate effectiveness by the residuals of parametric linear models. These models rely upon strong assumptions regarding the data-generating process, and are limited to handling extensive datasets. To address this issue, our paper provides a new approach based on machine learning models. The new approach is flexible, allows paired comparison, and is model-independent. An analysis conducted in Brazil verifies the suitability of the method to explore differences in effectiveness between Brazilian educational administrative units at the regional and state levels from 2009 to 2019. Our results are consistent with the existing literature, but the methodology produced a number of new findings that were not observed in studies using more traditional approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid SVR-ANN model for water demand forecasting. Where an adaptation of the methodology proposed by Zhang (Neurocomputing 50:159–175, 2003) is used to decompose the time series of 10 reservoirs that supply the Metropolitan Region of Salvador (RMS). The data used are from the historical consumption from January 2017 to February 2022, obtained from the local supply company, Empresa Baiana de Águas e Saneamento, and meteorological data obtained from the National Institute of Meteorology of Brazil. The results demonstrated the feasibility of using the proposed model, compared to other traditional models such as the multilayer perceptron (MLP), support vector regression (SVR), short long-term memory (LSTM) and autoregressive integrated moving average (ARIMA). [ABSTRACT FROM AUTHOR]

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations. [ABSTRACT FROM AUTHOR]

User eXperience (UX) has been used to achieve improvements in digital information systems based on how people perceive them. In particular, this paper establishes a framework that employs methods for eye and mouse tracking, keyboard input, self-assessment questionnaire and artificial intelligence algorithms to evaluate user experience and categorize users in terms of performance profiles. The results obtained with this framework are artifacts that can be used to support customizations of the User Interface (UI) on the websites. Moreover, the established framework is generic and flexible and can be applied to any information system, such as the case study shown in the website of the Federal Revenue of Brazil (RFB). The main objectives of this paper are as follows: (i) to set out a powerful UX framework based on three tracking techniques – the AIT2-UX; (ii) to provide the T2-UXT to collect, collate, process and visualize data obtained from users' interactions (iii) to use and compare machine learning algorithms with the classification of user performance profiles; (iv) to use the artifacts generated by the framework to manually customize the UI with the website. [ABSTRACT FROM AUTHOR]

Laborious and time-consuming tests are required for the determination of the soil–water characteristic curve (SWCC), often leading to the adoption of estimation methods. To answer the challenge of SWCC prediction, numerous pedotransfer functions (PTF) have been developed. Yet, previous studies have not considered the special behavior of bimodal tropical soils. These materials present dual porosity that is generally attributed to particle aggregation. This paper presents a novel PTF, specifically designed for bimodal tropical soils and based on artificial neural networks (ANNs). The model was trained and tested utilizing a database that was assembled containing soils from tropical regions of Brazil and featuring data for the grain-size distribution (GSD), consistency limits, and SWCC. Natural and remolded soils were included in the training database, but no distinction between soil conditions was made in the ANN. GSDs in the aggregated and disaggregated states were used to offer information to the ANN regarding the effect of particle aggregation on the water retention. The developed model was able to reproduce the typical SWCC shape of bimodal soils. Predictions for the degree of saturation were moderately correlated with directly measured data, with a coefficient of determination of 0.69. The air-entry value and residual suction of the macropores proved to be the most difficult SWCC attributes to be estimated. The ANN presented superior performance when compared to other PTFs not designed specifically for bimodal tropical soils, such as the Arya-Paris and ROSETTA models. It can be concluded from the obtained results that the developed ANN architecture and general approach showed a high capability to capture the main features of the SWCC. [ABSTRACT FROM AUTHOR]

Historical data in climatology is important for recognizing patterns and discovering trends. However, data gaps often occur in some weather station time series. This paper presents a framework consisting of machine learning techniques combined with triangulation methods to identify missing meteorological data. Our approach is based on using data from neighboring weather stations as input for triangulation methods in combination with machine learning techniques. The current focus of the proposed framework is on filling missing temperature data and it was applied in ten different regions of Brazil, each with a different climatic configuration. Furthermore, a statistical study was conducted to estimate the best configuration which showed that a popular mathematical triangulation model combined with neural networks produced the most satisfactory results in predicting missing data, outperforming the results of traditional triangulation methods. [ABSTRACT FROM AUTHOR]

Disease surveillance is used to monitor ongoing control activities, detect early outbreaks, and inform intervention priorities and policies. However, data from disease surveillance that could be used to support real-time decisionmaking remain largely underutilised. Using the Brazilian Amazon malaria surveillance dataset as a case study, in this paper we explore the potential for unsupervised anomaly detection machine learning techniques to discover signals of epidemiological interest. We found that our models were able to provide an early indication of outbreak onset, outbreak peaks, and change points in the proportion of positive malaria cases. Specifically, the sustained rise in malaria in the Brazilian Amazon in 2016 was flagged by several models. We found that no single model detected all anomalies across all health regions. Because of this, we provide the minimum number of machine learning models top-k models) to maximise the number of anomalies detected across different health regions. We discovered that the top three models that maximise the coverage of the number and types of anomalies detected across the thirteen health regions are principal component analysis, stochastic outlier selection, and the minimum covariance determinant. Anomaly detection is a potentially valuable approach to discovering patterns of epidemiological importance when confronted with a large volume of data across space and time. Our exploratory approach can be replicated for other diseases and locations to inform monitoring, timely interventions, and actions towards the goal of controlling endemic disease. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic continues to have a destructive effect on the health and well-being of the global population. A vital step in the battle against it is the successful screening of infected patients, together with one of the effective screening methods being radiology examination using chest radiography. Recognition of epidemic growth patterns across temporal and social factors can improve our capability to create epidemic transmission designs, including the critical job of predicting the estimated intensity of the outbreak morbidity or mortality impact at the end. The study's primary motivation is to be able to estimate with a certain level of accuracy the number of deaths due to COVID-19, managing to model the progression of the pandemic. Predicting the number of possible deaths from COVID-19 can provide governments and decision-makers with indicators for purchasing respirators and pandemic prevention policies. Thus, this work presents itself as an essential contribution to combating the pandemic. Kalman Filter is a widely used method for tracking and navigation and filtering and time series. Designing and tuning machine learning methods are a labor- and time-intensive task that requires extensive experience. The field of automated machine learning Auto Machine Learning relies on automating this task. Auto Machine Learning tools enable novice users to create useful machine learning units, while experts can use them to free up valuable time for other tasks. This paper presents an objective method of forecasting the COVID-19 outbreak using Kalman Filter and Auto Machine Learning. We use a COVID-19 dataset of Ceará, one of the 27 federative units in Brazil. Ceará has more than 235,222 confirmed cases of COVID-19 and 8850 deaths due to the disease. The TPOT automobile model showed the best result with a 0.99 of R 2 score. [ABSTRACT FROM AUTHOR]

Copyright of Latin American Business Review is the property of Taylor & Francis Ltd 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.)

The Coronavirus Disease 2019 (COVID-19) pandemic has had social and clinical effects over the healthcare system. Globally, over 314.019.135 confirmed cases and 5.507.370 deaths have been recorded at 12 January 2022, based on the Dashboard updated by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The continuous rise in the number of asymptomatic, pre-symptomatic, and symptomatic patients has developed efficient and accessible models for prediction using Open Source Libraries and Cloud Services. This paper proposes different machine-learning algorithms to analyse the COVID-19 OpenData Resources from Mexico and Brazil, which represent the two major populations, economics and affected by disease countries in Latin America. This model uses only the COVID-19 patient's geographical, social conditions, economics conditions, clinical risk factors, symptom reports, and demographic data to predict recovery and death. The model of Mexico has an accuracy of 93% and the perceived mean of recall and the precision (F1 Score) of 0.79 on the dataset used. On the other hand, the model of Brazil has an accuracy of 69% and F1 score of 0.75 on the dataset studied. The result considers data from patients under the age of 0 and 120 years. The contribution of the work is the application of Big Data technologies and Machine Learning algorithms using Open Resource Libraries and Amazon Web Services (AWS) with the vision to improve the clinical diagnosis, even infectious disease with mathematical approaches. [ABSTRACT FROM AUTHOR]

Censuses and other surveys responsible for gathering socioeconomic data are expensive and time consuming. For this reason, in poor and developing countries there often is a long gap between these surveys, which hinders the appropriate formulation of public policies as well as the development of researches. One possible approach to overcome this challenge for some socioeconomic indicators is to use satellite imagery to estimate these variables, although it is not possible to replace demographic census surveys completely due to its territorial coverage, level of disaggregation of information and large set of information. Even though using orbital images properly requires, at least, a basic remote sensing knowledge level, these images have the advantage of being commonly free and easy to access. In this paper, we use daytime and nighttime satellite imagery and apply a transfer learning technique to estimate average income, GDP per capita and a constructed water index at the city level in two Brazilian states, Bahia and Rio Grande do Sul. The transfer learning approach could explain up to 64% of the variation in city-level variables depending on the state and variable. Although data from different countries may be considerably different, results are consistent with the literature and encouraging as it is a first analysis of its kind for Brazil. [ABSTRACT FROM AUTHOR]

[Display omitted] • Assessment of the status and trends of PV DG in Brazil based on big data processing; • Application of machine learning algorithms to estimate the installed power of individual systems; • Application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions; • Application of the DEA method to rank Brazilian states in terms of efficiency in PV DG deployment; • The paper is expected to be valuable for regulatory agencies, researchers, and PV aggregator companies. The deployment of photovoltaic (PV) distributed generation (DG) has been increasing substantially in Brazil. In this context, the present paper exploits a robust dataset provided by the Brazilian Electricity Regulatory Agency (ANEEL) to evaluate the status and trends concerning PV DG. As of Nov/2022, such a dataset consists of more than 1,4 million lines (one for each system) and thirty columns (various information concerning the systems). For an in-depth assessment, three items are addressed: (i) the application of machine learning algorithms to estimate the installed power of individual systems based on other information available in the dataset, (ii) the application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions, and (iii) the application of the data envelopment analysis (DEA) method to rank Brazilian states in terms of efficiency in PV DG deployment. While items (i)-(iii) present distinct specific goals, they use the same dataset and provide essential insights concerning PV DG in Brazil. In item (i), elastic net (EN), decision tree (DT), random forest (RF), extra tree (ET), AdaBoost (AB), and gradient boosting (GB) are applied to select the most accurate algorithm. In item (ii), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing (HWES), Bass diffusion model (BDM), and multilayer perceptron artificial neural network (MLP-ANN) are applied. In item (iii), output-oriented DEA CCR and BCC are applied. Concerning item (i), results demonstrate that estimating the installed power of individual systems is not very simple. Nonetheless, the machine learning algorithms imply a significant accuracy increase compared to taking the dataset's average as the estimation, especially the DT and RF algorithms with a coefficient of determination of 29%. Regarding item (ii), results demonstrate that the installed capacity in Brazil and its regions is expected to increase approximately linearly in the horizon of months but with weekly seasonal characteristics. Moreover, the most accurate model depends on the region, i.e., the ANN is superior for the Northeast (MAPE of 3.25%) and South (MAPE of 1.28%) regions, the HWES method is superior for Brazil (MAPE of 0.51%) and the Southeast region (MAPE of 2.26%), and the BDM is superior for the Mid-west (MAPE of 1.95%) and North (MAPE of 3.37%) regions. Concerning item (iii), results demonstrate that DEA CCR is more representative of the Brazilian reality since DEA BCC assigns several states as efficient, even if they showcase a very low installed capacity, particularly small states. DEA CCR indicates that Minas Gerais, Rio Grande do Sul, and Mato Grosso are the most efficient states in PV DG deployment relative to their inputs, closely followed by São Paulo with an efficiency of 95%. Furthermore, states located in the North and Northeast region require more attention from ANEEL since their efficiencies are generally low. A literature review demonstrated that this paper is the first of its kind in exploiting so thoroughly and robustly the dataset provided by ANEEL. Therefore, the paper is expected to assist ANEEL in the task of properly regulating the sector and also assist researchers and PV aggregator companies through in-depth insights. Moreover, the conducted procedures can also be applied to other countries with available datasets, thus ensuring high applicability for the research. [ABSTRACT FROM AUTHOR]

Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency's concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator's experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks' application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory. [ABSTRACT FROM AUTHOR]

The subduction and export of Antarctic Intermediate Water (AAIW) is important for the heat, freshwater, carbon, and nutrient budgets of the world's oceans. Three types of AAIW are identified by applying an unsupervised machine learning approach to individual Argo profiles. The likely sources and pathways of these AAIWs are indicated by the locations and magnitudes of the salinity and oxygen extrema. The Southeast Pacific AAIW forms north of the Subantarctic Front (SAF) in the west corner of the Drake Passage and is exported north into the subtropical gyre. The South Pacific AAIW forms in the South Pacific along the SAF and is transported through the Drake Passage, thereby becoming a saliter and denser AAIW. The circumpolar AAIW, the coldest and freshest AAIW, has a circumpolar source and is injected into the ocean interior in the Brazil‐Malvinas confluence. The results clarify different types of AAIW have different origins. Plain Language Summary: The formation and export of Antarctic Intermediate Water (AAIW) strongly influence the global climate system by redistributing heat, freshwater, carbon, and nutrients from high latitudes to the tropics. Thanks to an increasing number of observations from autonomous instruments and Argo floats, our understanding of the sources and pathways of AAIW has deepened in recent decades. Even so, there is controversy about the formation of AAIW. Unlike previous studies using traditional property‐driven classification, we apply an unsupervised classification technique to automatically classify the Argo temperature and salinity profiles and identify three types of AAIWs from the regimes in the Southern Ocean. The classification results are consistent with previous studies. Moreover, we find that different types of AAIW form in different regions. This paper shows that the machine learning technique benefits us in dealing with a large data set and identifying AAIW classes that is hard to distinguish by the traditional property‐driven classification method. Key Points: An unsupervised learning approach, the profile classification model (PCM), is applied to classify Argo temperature and salinity profilesPCM identifies three types of Antarctic Intermediate Water (AAIW) in the Southern Ocean and reveals their locations and boundariesThe Polar Frontal Zone in the South Pacific sector is established as the source region for AAIW formation [ABSTRACT FROM AUTHOR]

Mineral prospectivity mapping (MPM), like other geoscience fields, is subject to a variety of uncertainties. When data about unfavorable sites to find deposits (i.e., drill intersections to barren rocks) is lacking in MPM using machine learning (ML) methods, the synthetic generation of negative datasets is required. As a result, techniques for selecting point locations to represent negative examples must be employed. Several approaches have been proposed in the past; however, one can never be certain that the points chosen are true negatives or, at the very least, optimal for training. As a consequence, methodologies that account for the uncertainty of the generation of negative datasets in MPM are needed. In this paper, we compare two criteria for selecting negative examples and quantify the uncertainty associated with this process by generating 400 potential maps for each of the three ML methods utilized (200 maps for each criterion), which include random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNC). The results showed that applying a geological constraint to the creation of negative datasets reduced prediction uncertainty and improved overall model performance but produced larger areas of very high probability (i.e., >0.9) when compared to using only the spatial distribution of known deposits and occurrences as a constraint. SHAP values were used to find approximations for the importance of features in nonlinear methods, and kernel density estimations were used to examine how they varied depending on the negative dataset used to train the ML models. Prospectivity models for magmatic-hydrothermal gold deposits were generated using data from the shuttle radar terrain mission, gamma-ray, magnetic lineaments, and proximity to dykes. The Juruena Mineral Province, situated in Northern Mato Grosso, Brazil, represented the case study for this work. [ABSTRACT FROM AUTHOR]

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics. [ABSTRACT FROM AUTHOR]

Knowledge and monitoring of invasive species are fundamental measures to determine the short- and long-term effect on invaded ecosystems, in addition to developing strategies to control the problem or its specific solution. In this context, the lionfish is an invasive species that worries managers and scientists of fisheries and marine conservation, this is due to the affected area that spread starting from the east coast of the United States to the coasts of Brazil and it is recently extending to include the Mediterranean Sea. The diet of the invasive fish is small species of fish, crustaceans and invertebrates; the consequent damage is the decrease of food for species at the next level of the food chain and the lack of species to keep coral reefs healthy. In this paper, we propose a lionfish detection system that will be installed in an autonomous underwater vehicle, as part of a monitoring strategy that will allow real-time determination of the number of Lionfish, their location and without human intervention. We compared two detection systems, namely YOLOv4 and SSD-Mobilenet-v2, by training with cross-validation and evaluation with the test set we obtained the best model with 63.66% recall, 89.79% precision, and 79.15% mAP with images in the natural environment, implemented on NVIDIA's Jetson Nano embedded system. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Gestão em Sistemas de Saúde is the property of Revista de Gestao em Sistemas de Saude (RGSS) 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.)

We model investor behavior by training machine learning techniques with financial data comprising more than 13,000 investors of a large bank in Brazil over 2016 to 2018. We take high-frequency data on every sell or buy operation of these investors on a daily basis, allowing us to fully track these investment decisions over time. We then analyze whether these investment changes correlate with the IBOVESPA index. We find that investors decide their investment strategies using recent past price changes. There is some degree of heterogeneity in investment decisions. Overall, we find evidence of mean-reverting investment strategies. We also find evidence that female investors and higher academic degree have a less pronounced mean-reverting strategy behavior comparatively to male investors and those with lower academic degree. Finally, this paper provides a general methodological approach to mitigate potential biases arising from ad-hoc design decisions of discarding or introducing variables in empirical econometrics. For that, we use feature selection techniques from machine learning to identify relevant variables in an objective and concise way. [ABSTRACT FROM AUTHOR]

A documentary review was carried out on the production and publication of research papers related to the study of the variables INTELLIGENT SYSTEMS, ARTIFICIAL INTELLIGENCE and AUTOMATIC LEARNING. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022 by Latin American institutions, achieving the identification of 196 publications. The information provided by this platform was organized through graphs and figures categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors towards the proposed theme is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Brazil with 104 publications was the Latin American country with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material referring to the study of Intelligent Systems, Artificial Intelligence and Machine Learning was Computer Science with 151 published documents, and the Type of Publication most used during the period indicated above were Conference Articles with 55% of the total scientific production. [ABSTRACT FROM AUTHOR]

This paper examines the remarkable growth of photovoltaic (PV) solar energy technology in Brazil, which stands out among the leading countries in photovoltaic installations. The recent implementation of Law 14300 on grid-connected PV systems has caused a stir in the Brazilian market, with investors anticipating installations to avoid reductions in benefits. This phenomenon, previously observed in Europe, instigates an understanding of the variables underlying the exponential growth of the Brazilian market. This study seeks to identify the predictors that explain the PV systems diffusion in Brazil, in two stages: a systematic review of the literature to map approaches and methods, followed by the construction of a database and the application of predictive models. The literature revealed 24 methods in six approaches, with 44 predictors. For Brazil, the database contains 90,129 instances, 22 predictors, and covers all 5570 municipalities. The results highlight the crucial importance of the electricity tariff as the main predictor, followed by irradiation and municipal Gross Domestic Product. Predictors such as the number of companies, minimum wage, HDI education, demographic density, and vehicle fleet prove to be relevant. In conclusion, the spatially explicit analysis highlights the complexity of the market, offering valuable implications for energy planners and stakeholders. [ABSTRACT FROM AUTHOR]

Empirical models based on machine learning methods have been used for landslide susceptibility mapping. The most accurate model is usually chosen to generate the final map. This paper demonstrates the importance of analyzing the spatial pattern of susceptibility maps, since models with similar performance can produce different output values. The relevance of terrain attributes and the sensitivity of models to input variables are also discussed. The applications of random forest (RF) and artificial neural network (ANN) models to the identification of landslide susceptible areas in the Fão River Basin, Southern Brazil, were evaluated and compared. The following have been included in the methodology: (1) the extraction of predictive attributes (e.g., slope, aspect, curvatures, valley depth) from a digital elevation model; (2) the organization of a landslide scar inventory; (3) the calibration and validation procedures of the models; (4) the analysis of model performance according to accuracy (area under the receiver operating characteristic curve) and parsimony (Akaike Information Criterion); (5) the reclassification of maps into susceptibility categories. All model configurations resulted in an accuracy above 0.9, demonstrating the ability of both techniques in landslide susceptibility mapping. The RF model stood out in this respect, recording the highest accuracy index among all tested configurations (0.949). The ANN model was more parsimonious, obtaining an accuracy of 0.925 with a much smaller number of internal connections. Thus, even with both having high and equivalent accuracy indexes, the models can establish different relationships between the input and the output susceptibility indexes, resulting in various possible landslide occurrence scenarios. These differences, together with the difficulty in defining which model presents more coherent results, reinforce the possibility of extracting spatial statistics, considering multiple configurations of models that combine accuracy and parsimony, in landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation. [ABSTRACT FROM AUTHOR]

Brazil is one of the countries most affected by the COVID-19 pandemic, with more than 16 million confirmedcases and 454,429 confirmed deaths by May 26, 2021 (according to the Johns Hopkins Coronavirus Resource Center). Brazil was and still is one of the countries most impacted by the first wave of Covid-19 which first recorded case on 26th February 2020 and reached community transmission from 20th March 2019 onwards to dates, that caught Brazil unprepared and unable to response due to the strain on hospital capacity such as the intense and lengthy request for ICU (incentive care unit) beds, professionals, personal protection equipment and healthcare resources. A data science team at Sirio Libanês, a top-tier hospital in Brazil, decided to use ML to help reduce the strain on hospital's ICU beds where the objective is to develop a ML model to predict if a patient of confirmed COVID-19 case would require admission to the ICU. With that objective in mind, the team has collected a decent amount of clinical data from patients i.e. the features of COVID patients, and the target (those been admitted to ICU). The paper describes the complete Model design using features of contemporary Machine Learning Models to interpret the Collected Data from the hospital to produce a decent report as a solution to the above mentioned hindrances at crucial times. [ABSTRACT FROM AUTHOR]

Brazil's education system lags behind international standards, with two-fifths of students scoring below the minimum level of proficiency in mathematics, science, and reading. Thus, this study combined machine learning with traditional statistics to identify the most important predictors and to interpret their effects on proficiency in the PISA 2018 mathematics, science, and reading tests. Predictors encompassed a wide range of variables, sociodemographic characteristics, teaching and learning processes, and non-cognitive skills. The outcome of the present study was proficiency in mathematics, science, and reading. PISA proficiency levels were grouped into "low proficiency" and "proficient" categories, using a classification system commonly employed in PISA reports. Using random forest analysis, a machine learning method, I compared the importance of predictors for proficiency in mathematics, science, and reading. I then adjusted multilevel logistic regression analyses to investigate the relationship between the top predictors and the outcomes. Among the top predictors for the three outcomes identified, annual household income, parents' highest occupational status, and early childhood education and care were positively associated with proficiency in mathematics, science, and reading, while grade repetition and additional instruction were negatively associated with these outcomes. These findings urge Brazilian policymakers and educators to prioritize initiatives that strengthen early childhood programs, minimize grade repetition, and promote effective learning strategies. [ABSTRACT FROM AUTHOR]

The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring and estimating the yield parameters of forage plants, especially in agroforestry systems (AFS) in this biome. This study aimed to compare the predictive ability of different indexes with regard to the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in AFS in the Caatinga biome and to evaluate the influence of removing system components on model performance. The normalized green red difference index (NGRDI) and the visible atmospherically resistant index (VARI) showed higher correlations (p < 0.05) with the variables. In addition, removing trees from the orthomosaics was the approach that most favored the correlation values. The models based on classification and regression trees (CARTs) showed lower RMSE values, presenting values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher CCC values (0.94). Using NGRDI and VARI, removing trees from the images, and using CART are recommended in estimating biomass and leaf area index in agroforestry systems in the Caatinga biome. [ABSTRACT FROM AUTHOR]

The Atlantic Forest fragments have suffered from the impacts of climate change, resulting in the increased production of coarse woody debris (CWD), which needs to be evaluated in space and time to generate accurate estimates of carbon accumulation. Thus, the goals of this study were (i) to quantify the CWD volume, necromass, carbon stock, and annual increment of carbon (AIcarb) over a period of 4 years; and (ii) to select the optimal combination of climatic, topographic, edaphic, and intrinsic forest variables to accurately predict AIcarb using machine learning and multivariate analysis. The CWD volume, necromass, and carbon stock increased between 2017 and 2020. The AIcarb was 1.09 MgC ha−1 year−1 (2017–2018), 1.24 MgC ha−1 year−1 (2018–2019), and 2.31 MgC ha−1 year−1 (2019–2020). Statistical analysis indicated that climate variables had greater weight in the CWD carbon increment in the 2018–2019 and 2019–2020 periods, while edaphic, topographical, and intrinsic forest variables were more important for the 2017–2018 period. Our findings showed that the carbon increase in CWD was linked to temporal and spatial variables within forests. These results demonstrate the importance of this parameter in the carbon cycle of forest ecosystems and highlight that there should be greater international research efforts to quantify this carbon pool. [ABSTRACT FROM AUTHOR]

We develop a nonparametric model to study health spillover effects of policy interventions. We use double/debiased machine learning to estimate the model using data from 74 hospitals in Rio de Janeiro, Brazil, and examine cross-patient spillover effects during the COVID-19 pandemic. The pandemic forced hospitals to develop new protocols to offer intensive care to both COVID and non-COVID patients. Our results show that the need to care for COVID patients affects health outcomes of non-COVID patients. Controlling for a number of confounders, we find that mortality rates and length of stay of non-COVID ICU patients increase when hospitals simultaneously offer intensive care to both types of patients. Policy simulations suggest that an increase in the number of ICU beds can counter morbidity spillover, but it is unlikely to be a feasible approach to counter mortality spillover. [ABSTRACT FROM AUTHOR]

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. [ABSTRACT FROM AUTHOR]

Summary: Fraud in electrical energy consumption represents a critical economic burden for utility companies around the world. Despite systematic efforts to mitigate electricity theft, this practice persists mostly in developing countries where companies rely on traditional detection methods. In Brazil it is estimated that around 7% of the total electrical energy available for consumption in 2016 was lost due to frauds. Here we describe an efficient and scalable system to predict fraudulent behavior and guide in loco inspections. We compared the performances of several machine learning algorithms using consumption and inspection data provided by CPFL Energia. We show that proper feature engineering and boosted classification trees trained with XGBoost are able to extract patterns related to fraud occurrence and to achieve predictive power of practical consequences. Moreover, we demonstrate how Shapley additive explanation (SHAP) values can be employed to build user friendly explanations. Together, the proposed model and its explainers contribute not only to reveal potentially fraudulent behavior but also to understand root causes, what can be used to devise robust mitigation strategies. [ABSTRACT FROM AUTHOR]

Water resources management in Latin America and the Caribbean is particularly threatened by climatic, economic, and political pressures. To assess the region's ability to manage water resources, we conducted an unprecedented literature review of over 20 000 multilingual research articles using machine learning and an understanding of the socio-hydrologic landscape. Results reveal that the region's vulnerability to water-related stresses, and drivers such as climate change, is compounded by research blind spots in niche topics (reservoirs and risk assessment) and subregions (Caribbean nations), as well as by its reliance on an individual country (Brazil). A regional bright spot, Brazil, produces well-rounded water-related research, but its regional dominance suggests that funding cuts there would impede scientifically informed water management in the entire region. [ABSTRACT FROM AUTHOR]

Electricity generation in Brazil heavily depends on hydroelectric power, making it vulnerable to fluctuations due to its reliance on weather patterns. Accurately forecasting water inflow into hydroelectric plants is vital for the National Electric System Operator to make decisions regarding the monthly scheduling and operation of the power system. In this paper, an evaluation of predicted flows for a 14-day horizon are evaluated for the Tucuruíhydroelectric plant, located in the Tocantins river in the North of Brazil. The temporal fusion transformer (TFT), long short-term memory (LSTM), and temporal convolutional networks (TCN) are compared. The findings demonstrate that the TFT is a more suitable alternative than LSTM and TCN models for predicting inflows for the next 14 days. The TFT model is hypertuned by Optuna to achieve an optimized structure (h-TFT). The h-TFT had a mean absolute percentage error of 13.1 and a Nash–Sutcliffe of 0.96, outperforming its initial version and even the bidirectional LSTM model in benchmarking. Based on the error results, h-TFT showed promise for flow forecasting and provides insights into decision-making processes in the Brazilian electricity sector. • Hypertuning the TFT improves its performance in median-term forecasting. • The h-TFT outperforms LSTM, and TCN models for time series forecasting. • Deep learning applied for scheduling operation of the electrical power system. • Energy planning based on the inflow forecasting of hydroelectric power plants. [ABSTRACT FROM AUTHOR]

Introduction: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis. Methods: In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors: age, gender, history of living in a mud or wooden house, history of being bitten by a triatomine bug, and family history of Chagas disease. We analyzed data from the Retrovirus Epidemiology Donor Study (REDS) to train five popular machine learning algorithms. The sample comprised 2,006 patients, divided into 75% for training and 25% for testing algorithm performance. We evaluated the model performance using precision, recall, and AUC-ROC metrics. Results: The Adaboost algorithm yielded an AUC-ROC of 0.772, a precision of 0.199, and a recall of 0.612. We simulated the decision boundary using various thresholds and observed that in this dataset a threshold of 0.45 resulted in a 100% recall. This finding suggests that employing such a threshold could potentially save 22.5% of the cost associated with mass testing of Chagas disease. Conclusion: Our findings highlight the potential of applying machine learning to improve the sensitivity and effectiveness of Chagas disease diagnosis and prevention. Furthermore, we emphasize the importance of integrating socio-demographic and environmental factors into neglected disease prediction models to enhance their performance. Author summary: Chagas disease, a severe parasitic illness prevalent in Latin America, poses significant challenges due to delayed detection and treatment. Machine learning algorithms, advanced computer programs, have emerged as valuable tools for disease prediction and diagnosis. In our study, we utilized these algorithms to forecast Chagas disease risk based on factors such as age, gender, and living conditions. Drawing on data from the Retrovirus Epidemiology Donor Study (REDS), we trained five algorithms, with one showing promising results, achieving an impressive score of 0.772 out of 1. By establishing a specific threshold, we could potentially reduce testing costs while maintaining high detection rates. This research highlights the potential of machine learning in improving Chagas disease diagnosis and prevention by incorporating socio-demographic and environmental factors. Integrating these elements into predictive models has the potential to enhance their effectiveness and sensitivity, thereby improving disease management outcomes and ultimately reducing the burden of Chagas disease in affected regions. [ABSTRACT FROM AUTHOR]

This article investigates the historical uses and types of artificial intelligence (AI) systems and resources in Brazilian journalistic products. It is a work anchored in critically analyzing the literature on the subject, mapping and observing cases, seeking to identify uses and innovative processes, and analyzing AI projects for journalism. A search was carried out in web repositories, specifically Google, Google Scholar, and Scopus, using the terms: "inteligência artificial" + "jornalismo", "bot + jornalismo", "Geração de linguagem natural [NLG] + jornalismo", "aprendizado de máquina [machine learning] + jornalismo", and "algoritmos + jornalismo". The corpus analysis (N = 45) includes the evaluation of the impacts of AI on the production and distribution of news in the context of Brazilian digital journalism. We try to answer questions about the uses of databases, approximation with platforms, uses of shared codes, connections with other Ais, and sources of funding, and whether they are backend or frontend initiatives. In a parallel investigation, we try to identify if Brazilian newsrooms are officially using ChatGPT, a generative AI. The findings point to advances in using low-cost and low-impact AI, with the predominance of bots. The great availability of this kind of AI in web repositories is believed to facilitate native digital media to incorporate innovative processes in using these technologies. [ABSTRACT FROM AUTHOR]

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners. [ABSTRACT FROM AUTHOR]

• IoT and machine learning promote soil moisture forecast for smart irrigation. • Our approach is fully data-driven and independent of domain-knowledge features. • Results come from twelve fields on four farms, from eight crop types in four years. • Boosting outperforms neural networks, and blended models improve performance. • Poor data quality is a challenge for machine learning soil moisture forecast. The rise of the Internet of Things allowed higher spatial–temporal resolution soil moisture data captured through in situ sensing. Such abundance of data enables machine learning-based soil moisture forecast as an alternative to traditional mechanistic approaches for irrigation water need estimation. This paper develops a guideline for soil moisture forecast modeling based on machine learning, tested in a real case analysis comprehending eight crop types in twelve fields from four farms distributed over diverse climatic scenarios in Brazil. Instead of a single value, we predict the following days' minimum and maximum values as targets to monitor risks of extreme soil moisture values. Furthermore, modeling soil moisture directly in volumetric water content (VWC) is better than modeling soil matric potential (SMP) to later convert in soil moisture VWC. We test several algorithms and find out that LightGBM outperforms linear regression, decision tree, random forest, multilayer perceptron, LSTM, and StemGNN. Also, blending predictions via algorithm ensemble provides an additional accuracy gain. For model training and accuracy measurement, we use weighted datasets to privilege rare but critical data points. We show that soil moisture forecast reaches its maximum performance considering only past soil moisture, a context-aware index, and a precipitation forecast. Finally, we demonstrate that traditional domain-knowledge features - such as evapotranspiration, crop phenology, and soil hydraulic behavior - are not relevant to improving SM forecast performance. Consequently, our paper suggests full data-driven approaches for irrigation water need estimation, observed some care regarding data quality. [ABSTRACT FROM AUTHOR]

School dropout is a relevant socio-economic problem across the globe. Predictive models have been developed to determine the likelihood of students dropping out of their studies precociously to overcome such a problem. Academic systems, which gather data from many students, are potential sources for datasets that feed dropout prediction algorithms, thus leading to general improvements in education quality. Despite successful past attempts to predict dropout, several works depict small datasets with features that are hard to reproduce. Furthermore, predicting whether a student will drop out is not enough to diagnose and prevent the problem as it is also necessary to provide potential justifications for the dropout. This paper proposes an approach for creating and enriching a dataset for dropout prediction, which has been applied for dropout prediction using data from 19 schools in Brazil. With this dataset and using classifiers and model explaining techniques, our experiments achieved Area Under the Precision–Recall Curve (AUC-PR) scores of up to 89.5%, Precision up to 95%, Recall up to 93%, and Kolmogorov–Smirnov (KS) rates up to 97% when predicting dropout at different year moments. This study also shows differences when predicting dropouts in different educational stages, such as preschool and secondary education, with the former being more complex than the latter. In addition to the high recognition rates, our proposal identifies potential reasons for student dropout, which are relevant for educational institutions to take preemptive actions. • Dropout prediction is performed with educational, financial, and public data. • Dropout analysis is conducted in different educational stages and time of the year. • Explainable machine learning allows reasoning behind potential dropouts. [ABSTRACT FROM AUTHOR]

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health. [ABSTRACT FROM AUTHOR]

Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These uncertainties can significantly influence both short-term and long-term operational planning in hydropower systems. To mitigate these effects, this study introduces a novel Bayesian model averaging (BMA) framework to improve the accuracy of streamflow forecasts in real hydro-dominant power systems. Designed to serve as an operational tool, the proposed framework incorporates predictive uncertainty into the forecasting process, enhancing the robustness and reliability of predictions. BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. This approach updates weights periodically using recent historical data of forecasted and measured streamflows. Tested on inflows to 139 reservoirs and hydropower plants in Brazil, the proposed BMA framework proved to be more skillful than individual models, showing improvements in forecasting accuracy, especially in the South and Southeast regions of Brazil. This method offers a more reliable tool for streamflow prediction, enhancing decision making in hydropower system operations. [ABSTRACT FROM AUTHOR]

Objectives: To identify and describe the profile of potential transthyretin cardiac amyloidosis (ATTR-CM) cases in the Brazilian public health system (SUS), using a predictive machine learning (ML) model. Methods: This was a retrospective descriptive database study that aimed to estimate the frequency of potential ATTR-CM cases in the Brazilian public health system using a supervised ML model, from January 2015 to December 2021. To build the model, a list of ICD-10 codes and procedures potentially related with ATTR-CM was created based on literature review and validated by experts. Results: From 2015 to 2021, the ML model classified 262 hereditary ATTR-CM (hATTR-CM) and 1,581 wild-type ATTR-CM (wtATTR-CM) potential cases. Overall, the median age of hATTR-CM and wtATTR-CM patients was 66.8 and 59.9 years, respectively. The ICD-10 codes most presented as hATTR-CM and wtATTR-CM were related to heart failure and arrythmias. Regarding the therapeutic itinerary, 13% and 5% of hATTR-CM and wtATTR-CM received treatment with tafamidis meglumine, respectively, while 0% and 29% of hATTR-CM and wtATTR-CM were referred to heart transplant. Conclusion: Our findings may be useful to support the development of health guidelines and policies to improve diagnosis, treatment, and to cover unmet medical needs of patients with ATTR-CM in Brazil. [ABSTRACT FROM AUTHOR]

Global environmental impacts such as climate change require behavior from society that aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels with other energy sources. An important aspect of efficient and sustainable management of the electricity supply in Brazil is the prediction of some variables of the national electric system (NES), such as the price of differences settlement (PLD) and wind speed for wind energy. In this context, the present study investigated two distinct forecasting approaches. The first involved the combination of deep artificial neural network techniques, long short-term memory (LSTM), and multilayer perceptron (MLP), optimized through the canonical genetic algorithm (GA). The second approach focused on machine committees including MLP, decision tree, linear regression, and support vector machine (SVM) in one committee, and MLP, LSTM, SVM, and autoregressive integrated moving average (ARIMA) in another. The results indicate that the hybrid AG + LSTM algorithm demonstrated the best performance for PLD, with a mean squared error (MSE) of 4.68. For wind speed, there is a MSE of 1.26. These solutions aim to contribute to the Brazilian electricity market's decision making. [ABSTRACT FROM AUTHOR]