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Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Este trabajo tiene como objetivo determinar la aplicación de la inteligencia artificial en la industria de la hospitalidad en Latinoamérica. Para lograrlo, se optó por un enfoque de revisión de alcance, adecuado para casos como éste, en los que la literatura existente no ha sido revisada de manera exhaustiva o presenta una naturaleza compleja y heterogénea. En este estudio se seleccionaron 35 documentos que cumplían con los criterios: ser de autores latinoamericanos y con temática de inteligencia artificial aplicada en la región. Al realizar el análisis con la metodología de las 4 P’s (Predecir, Producir, Promocionar y Proporcionar) se obtuvo un total de 86 contribuciones, de éstas el primer lugar con 42% se concentra en proporcionar experiencias a clientes, 22% en predecir información de valor para la toma de decisiones, 19% en promocionar sus ofertas y el 17% en mejorar la producción de servicios y productos. Además, se encontró que las técnicas de inteligencia artificial más empleadas en el sector de la hospitalidad son aprendizaje de máquina y procesamiento de lenguaje natural. Sin embargo, se detectó que las investigaciones sobre esta temática en los países latinoamericanos representan menos del 3,5% de la producción global. Por tanto, con este trabajo se pretende contribuir a una mayor comprensión de la aplicación de la inteligencia artificial en la industria de la hospitalidad y turismo en América Latina, abarcando tanto la perspectiva del cliente como la empresarial. No obstante, resalta la urgencia de fortalecer la investigación en este ámbito para impulsar el crecimiento y la innovación de la industria de la hospitalidad y el turismo en la región, dado su papel fundamental en la economía de estos países.

Neste trabalho, aplicamos modelos de Machine Learning (árvores de regressão, random forest, boosting e XGBoost) para precificação ou tarifação de uma carteira de seguro automóvel e comparamos com modelos lineares generalizados (GLM) em dados de sinistros de seguro automóvel considerando as principais características do segurado e do veículo. Com base em critérios de avaliação de peformance fora-da-amostra, os resultados indicaram que o XGBoost é o melhor método preditivo tanto para frequência como para severidade, apresentando ganhos na predição quando comparado ao GLM comumente utilizado em tarifação de seguros.

Agricultural and urban expansion has caused considerable degradation of ecosystems. In the case of Peruvian high Andean grasslands, it was reported that between 2000 and 2009, this ecosystem was reduced by 7%. The limited or no protection they receive is partly due to the fact that the benefits of ecosystem services are not widely known. This research aims to establish and predict the influence of high Andean grasslands on the annual occurrence of landslides. To do so, we identified occurrences of landslides, falls, huaycos, avalanches, and alluviums in high Andean grasslands. We also examined urban areas and agricultural zones of Peru for the period from 2003 to 2016. Subsequently, we extracted data on precipitation, temperature, slopes, soil types, and geographical variables. This data was used to train a machine learning model. The results show that 96% of landslides occurred in human-intervened areas, and only 4% in high Andean grasslands. Precipitation and slope thresholds for landslide occurrence are higher in high Andean grasslands compared to agricultural and urban areas. The best-performing machine learning models were linear regression, Gaussian processes, random forest, and support vector machine. They had coefficients of determination of R² = 0.80, 0.80, 0.66, and 0.64, respectively. Predictions show that if agricultural or urban areas are established in wet or dry puna grasslands, the average number of occurrences multiplies. The multiplier factors are 2.1 and 7.08, the number of deaths by 2.8 and 10.49, the number of houses destroyed by 2.4 and 7.51, and the number of roads destroyed by 2.2 and 7.37, respectively. The study demonstrates that conserving high Andean grasslands significantly reduces landslides compared to urban or agricultural areas.

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Abstract In this study, the principles of complex climatology were applied to delineate topoclimates in the mountains of eastern Cuba. A regional numerical weather model, driven by reanalysis, was used to obtain temperature patterns at a resolution of 0.6 km. Unsupervised machine learning techniques were then utilized to identify weather types based on temperature during rainy and less rainy periods, as well as geographical location. This analysis was supplemented with historical precipitation and geographical data to identify 23 topoclimates in the study area. The methods allowed for the estimation of topoclimate identification errors and provided insights into the representativeness of surface meteorological stations for the study area. The results showed that the rainfall distribution of the identified topoclimates was consistent with that of local climate-forming factors and previous research. Furthermore, new insights into the climatological rainfall characteristics of the lower and middle heights of the mountains in eastern Cuba were identified.

Objective: To verify the accuracy of deep learning models in detecting cellular alterations in histological images of oral mucosa. Material and Methods: The study compares three convolutional neural network (CNN) architectures for classifying histological images: EfficientNet-B3, MobileNet-V2, and VGG16. Efficient and focused on computer vision, each has specific advantages. A Kaggle database with 5192 images was used, divided into training (70%), validation (15%), and test (15%) sets. The CNNs were implemented using the Keras library, trained with pre-trained ImageNet weights, and evaluated using accuracy and AUC metrics. Results: The findings indicate that EfficientNet-B3 achieved the lowest training and validation losses at epoch 30, with the highest accuracy and stability during training. Evaluation metrics showed EfficientNet-B3 with 98% accuracy and 99% sensitivity for oral squamous cell carcinoma (OSCC) images, outperforming MobileNet-V2 and VGG16. MobileNet-V2 achieved 97% accuracy and 96% sensitivity, while VGG16 reached 94% accuracy and 93% sensitivity for OSCC images. All models exhibited high sensitivity and specificity in differentiating between normal and OSCC images, as demonstrated by ROC curves. EfficientNet-B3 had the highest AUC (0.982), followed by MobileNet-V2 (AUC=0.967) and VGG16 (AUC=0.937). These findings underscore the effectiveness of EfficientNet-B3 for accurately detecting cellular alterations in histological images of oral mucosa. Conclusion: Our study reveals the superior performance of CNNs, particularly EfficientNet-B3, in classifying histological images of OSCC.

This study developed an automated image recognition model for inspecting residential roofs using the YOLOv8 architecture to identify three types of damage. The methodology involved images from 167 buildings captured by drones and annotated in CVAT, which were used to train and test the model. YOLOv8 was applied for anomaly detection and classification, achieving 79% precision. The limitations were the small dataset and the limited variety of capture angles. The originality of the work lies in the innovative use of YOLOv8 for roof inspection. Future research will focus on developing the YOLOv9 and YOLOv10 architectures and expanding the dataset and damage classes.

The size of urban settlements is rapidly increasing worldwide. This sprawl triggers changes in land cover with the consumption of natural areas and affects ecosystems with important ecological, climate, and social transformations. Detecting, mapping, and monitoring the growth and spread of urban areas is therefore important for urban planning, risk analysis, human health, and biodiversity conservation. Satellite images have long been used to map human settlements. The availability of the Sentinel constellation (S2) allows the monitoring of urban sprawl over large areas (e.g., countries) and at high frequency (with possible monthly updates). This massive data stream allows the proposal of new types of urban products at a spatial resolution of 10 meters. In this context, we developed a fully automated and supervised processing chain (URBA-OPT) using open-source libraries and optimized for rapid calculation on high-performance computing clusters. This processing chain has been applied to all of France. The objective is to propose an annual product at 10 m spatial resolution available through the THEIA Data and Services Centre. Our results demonstrate the feasibility and accuracy of producing an annual urban settlement map using this methodology, providing a valuable tool for urban planning and environmental monitoring.

Assessing landslide susceptibility in a municipality is crucial for disaster prevention, and Artificial Neural Networks (ANN´s) have proven effective in this analysis. This study aimed to model landslides susceptibility in the municipality of Joinville, Santa Catarina state, southern Brazil, using ANNs. The municipality has a significant history of such events, allowing for an inventory of occurrence areas (OC) through polygon mapping on satellite images. For non-occurrence areas (NO), a 1 km radius buffer was used, subtracting OC from it. Random points were generated at 10 m intervals, with a value of 1 for OC and 0 for NO. The explanatory variables were divided into three groups: (i) morphometric variables, (ii) horizontal distances to roads and structural lineaments, and (iii) geo-environmental cartographic databases. Five ANN´s configurations were tested. Validation employed metrics such as area under the ROC curve (AUC) and overall accuracy (ACC), with the best modeling yielding an AUC of 0.90 and ACC of 0.84. This result utilized all explanatory variables except land use and cover, which caused a slight bias in the ANN due to the predominance of landslides in forested areas in the inventory. Geology played a crucial role in determining susceptibility.

Objective: Verbal communication contains key information for mental health assessment. Researchers have linked psychopathology phenomena to certain counterparts in natural language processing. We characterized subtle impairments in the early stages of psychosis, developing new analysis techniques, which led to a comprehensive map associating features of natural language processing with the full range of clinical presentation. Methods: We used natural language processing to assess spontaneous and elicited speech by 60 individuals with at-risk mental states and 73 controls who were screened from 4,500 quota-sampled Portuguese speaking residents of São Paulo, Brazil. Psychotic symptoms were independently assessed with the Structured Interview for Psychosis-Risk Syndromes. Speech features (e.g., sentiments and semantic coherence), including novel ones, were correlated with psychotic traits (Spearman’s-ρ) and at-risk mental state status (general linear models and machine-learning ensembles). Results: Natural language processing features were informative for classification, presenting a balanced accuracy of 86%. Features such as semantic laminarity (as perseveration), semantic recurrence time (as circumstantiality), and average centrality in word repetition graphs carried the most information and were directly correlated with psychotic symptoms. Grammatical tagging (e.g., use of adjectives) was the most relevant standard measure. Conclusion: Subtle speech impairments can be detected by sensitive methods and can be used in at-risk mental states screening. We have outlined a blueprint for speech-based evaluation, pairing features to standard psychometric items for thought disorder.

La detección temprana y la predicción de enfermedades en el ganado son esenciales para garantizar la salud y el bienestar de los animales, mejorar la productividad y reducir las pérdidas económicas. En este contexto, el Machine Learning (ML), un avance prominente dentro de la inteligencia artificial emerge como una herramienta revolucionaria para transformar el proceso de identificación y manejo de enfermedades en los animales. Esta tecnología permite desarrollar algoritmos complejos capaces de analizar grandes volúmenes de datos clínicos y ambientales, identificando patrones de alerta temprana en síntomas y comportamientos asociados a enfermedades. A través de modelos predictivos, el ML evalúa factores de riesgo y estima la probabilidad de aparición de enfermedades, lo que mejora significativamente la precisión diagnóstica y la efectividad de los tratamientos. Este artículo revisa de manera exhaustiva el uso de ML en la producción ganadera, abordando aplicaciones, modelos y técnicas de vanguardia para la detección y manejo sanitario del ganado, y plantea oportunidades para una gestión ganadera más eficiente y ética, considerando además los desafíos éticos y de privacidad inherentes a la implementación de estas tecnologías

A crescente evasão e reprovação em cursos de Tecnologia tem encontrado na inteligência artificial uma aliada para mitigar seus impactos. Este estudo investigou a capacidade preditiva de dados demográficos na identificação de alunos em risco de reprovação em turmas de pensamento computacional. Para isso, construiu modelos de machine learning e avaliou seus desempenhos, também considerando o impacto de diferentes técnicas de amostragem. Os resultados indicaram uma baixa correlação entre dados demográficos e o desempenho dos alunos, sugerindo que esses dados, isoladamente, possuem limitações preditivas. Contudo, o uso de técnicas como SMOTE mostrou-se significativo, elevando as métricas de avaliação dos modelos. Destacam-se a regressão logística, com recall de 53% para a classe de reprovação, e o SVC, que atingiu 75% de acurácia quando combinado com SMOTE e Tomek links. Esses achados reforçam o papel de estratégias de balanceamento para melhorar a eficácia preditiva em cenários desafiadores.

This systematic literature review (SLR) explores current state-of-the-art artificial intelligence (AI) methods for forecasting hotel demand. Since revenue management (RM) is crucial for business success in the hotel industry, this study aims to identify state-of-the-art effective AI-based solutions for hotel demand forecasting, including machine learning (ML), deep learning (DP), and artificial neural networks (ANNs). The study conducted an SLR using the PRISMA model and identified 20 papers indexed in Scopus and the Web of Science. It addresses the gaps in the literature on AI-based demand forecasting, highlighting the need for clarity in model specification, understanding the impact of AI on pricing accuracy and financial performance, and the challenges of available data quality and computational expertise. The review concludes that AI technology can significantly improve forecasting accuracy and empower data-driven decisions in hotel management. Additionally, this study discusses the limitations of AI-based demand forecasting, such as the need for high-quality data. It also suggests future research directions for further enhancing AI forecasting techniques in the hospitality industry.

Purpose – This paper aims to investigate whether professional data analysts’ trust of black-box systems is increased by explainability artifacts. Design/methodology/approach – The study was developed in two phases. First a black-box prediction model was estimated using artificial neural networks, and local explainability artifacts were estimated using local interpretable model-agnostic explanations (LIME) algorithms. In the second phase, the model and explainability outcomes were presented to a sample of data analysts from the financial market and their trust of the models was measured. Finally, interviews were conducted in order to understand their perceptions regarding black-box models. Findings – The data suggest that users’ trust of black-box systems is high and explainability artifacts do not influence this behavior. The interviews reveal that the nature and complexity of the problem a black-box model addresses influences the users’ perceptions, trust being reduced in situations that represent a threat (e.g. autonomous cars). Concerns about the models’ ethics were also mentioned by the interviewees. Research limitations/implications – The study considered a small sample of professional analysts from the financial market, which traditionally employs data analysis techniques for credit and risk analysis. Research with personnel in other sectors might reveal different perceptions. Originality/value – Other studies regarding trust in black-box models and explainability artifacts have focused on ordinary users, with little or no knowledge of data analysis. The present research focuses on expert users, which provides a different perspective and shows that, for them, trust is related to the quality of data and the nature of the problem being solved, as well as the practical consequences. Explanation of the algorithm mechanics itself is not significantly relevant.

Copyright of JBES: Brazilian Journal of Health Economics / Jornal Brasileiro de Economia da Saúde is the property of JBES: Brazilian Journal of Health Economics 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.)

CO2 emissions from urban areas have been perceived as a central point in the Global Carbon Cycle, although we still understand little about their social dimension in space and time. The work aims to model CO2 emissions from road traffic and evaluate the relationship between socio-economic characteristics of the population living in the city of Rio de Janeiro between 7 and 31 January 2021. A mapping model was used to estimate CO2 emissions at the street level, using Machine Learning algorithms, mobility data, Geographic Information System, and R. Emissions were aggregated to planning areas and regions, as well as to the IPEA digital mesh containing socioeconomic indicators, such as population density, average per capita household income and number of jobs. The results showed that the ML model effectively represents the spatio-temporal variability of hourly vehicle flows and average speed at road level in the city of Rio de Janeiro, presenting an RMSE of 138 veh h-1 and 7.0 km h-1, respectively. The model estimated high emissions on Avenida Brasil, the city's main expressway, with a maximum of 4,750 tCO2 km-2. The biggest emissions occur on the expressways that connect the city towards the Center and the edge of the South Zone and Barra da Tijuca, old and new centralities, where there is a greater job offer. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence in courts around the world has sparked the interest of jurists, aiming to determine the legal nature of algorithms and machine learning in order to confer a specific role to AI in the construction of virtuous jurisprudence. While some researchers believe that machines should contribute quantitatively, attempting to reduce the Judiciary's backlog, there are scholars who see technology as a qualitative alternative, resulting in greater legal certainty and uniformity of decisions. However, both alternatives carry their respective risks. Therefore, it is necessary to approach the issue to determine the role of artificial intelligence in Brazilian courts. Thus, through a methodology of bibliographic ref investigation, the nuances of using predictive technology and artificial intelligence in the Brazilian Judiciary will be analyzed. [ABSTRACT FROM AUTHOR]

Copyright of Arte & Ensaio is the property of Arte & Ensaio 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.)

Copyright of Brazilian Business Law Journal / Administração de Empresas em Revista is the property of Administracao de Empresas em Revista 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.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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.)

This research conducts a scoping review on the application of machine learning (ML) in social media marketing, an increasingly pivotal area in digital marketing strategies. Machine learning plays a crucial role in analyzing user data, identifying consumer behavior, personalizing content, and optimizing marketing campaigns. Through a systematic search of academic journals, conference proceedings, and other relevant sources, this review identifies and synthesizes studies that explore the use of ML in social media marketing. The selected studies are analyzed to provide a comprehensive overview of ML's impact and applications within this domain. Key findings highlight the significant role of ML in enhancing personalization, improving user engagement, and driving more effective marketing strategies. However, challenges such as data privacy concerns, algorithmic biases, and the need for greater transparency are also noted. The practical implications for marketers include the importance of ethical practices in data handling, algorithm development, and consumer trust-building. Additionally, this review identifies gaps in the current literature and suggests directions for future research, offering valuable insights for both researchers and practitioners aiming to leverage machine learning in social media marketing.

Soil fertility evaluation is fundamental for sustainable agricultural practices, often relying on conventional laboratory methods. These methods, while accurate, are labor-intensive, time-consuming, and require chemical reagents. Spectroscopic sensors, such as energy-dispersive X-ray fluorescence (EDXRF), offer a rapid and non-destructive alternative but require calibration of machine learning models for accurate prediction of fertility attributes. In this context, this study compares the performance of four machine learning algorithms—multiple linear regression (MLR), partial least square regression (PLS), support vector machine regression (SVM), and random forest regression (RF)—in predicting soil pH, organic carbon (SOC), sum of exchangeable bases (BS), and cation exchange capacity (CEC) using EDXRF data from two soil datasets. Results indicate that PLS models outperformed others (the hierarchy of accuracy was PLS > MLR > SVM > RF). Overall, we emphasize the benefits of integrating PLS with EDXRF, capable of mitigating the use of traditional soil analysis.

Green Chemistry aims to prevent pollution resulting from activities in the chemical sector. Generally, laboratory activities generate pollution because toxic substances are used to perform analyses, which produce waste once the analyses are completed. This study aimed to monitor the quality of the Uberaba River using more sustainable and non-destructive techniques through orbital remote sensing and to develop methods for estimating water quality at reduced costs using orbital sensors and machine learning. The values of water quality parameters, such as iron, nitrate, chloride, total phosphorus, and total nitrogen, were studied between 2018 and 2023. Orbital sensing data (spectral bands and vegetation indices) were taken from the exact geographic coordinates of the collection points. Data were analyzed using Pearson’s correlation coefficient and random forest regression analysis. The study concludes that it is possible to perform orbital remote sensing by estimating water quality through random forest regression, correlating the information obtained from Sentinel-2 images with the values of these parameters. This approach is a sustainable technique that does not generate waste and represents a 100% saving compared to conventional chemical analyses.

Abtract This study aims analyze the important influencing factors that affect the life satisfaction of Koreans, and to identify the relative importance of these factors. For this purpose, we utilize academic papers on what influences life satisfaction, and questionnaire data from the survey on social integration conducted annually by the Korean Government. A topic modelling analysis method was used to derive important influencing factors, and a neural network analysis method, one of the machine learning methods, was used to analyze the relative importance of influencing factors. The analysis showed that the factor that had the greatest impact on Koreans’ life satisfaction was satisfaction with work. Other factors included self-esteem, level of worry and anxiety, and level of satisfaction with health status. The study used methods such as topic modeling and neural network analysis to derive the main factors affecting life satisfaction and analyze the relative importance of the factor involved. The study results suggest that in recognition of the importance of job satisfaction, future research should be expanded, and that the Korean Government should introduce various policies to increase job satisfaction.

The landslide susceptibility mapping (LSM) plays an important role in risk management. This study evaluated the predictive capabilities of three machine learning (ML) approaches applied to LSM: logistic regression (LR), random forests (RF), and artificial neural networks (ANN). The study was conducted in a mountainous region of Mariana/MG, Brazil. Initially, a point inventory with 364 landslides and 364 stable regions was randomly partitioned in a 70% training and 30% testing ratio for the models. Nine landslide conditioning factors (LCF), ranked by information gain (IG), were considered: slope angle (IG=0.486), geomorphology (IG=0.235), topographic wetness index - TWI (IG=0.138), lithology (IG=0.077), slope orientation (IG=0.067), topographic position index - TPI (IG=0.052), distance from drainage (IG=0.032), slope curvature (IG=0.029) and the distance from roads (IG=0.024). The evaluation of the area under the curve (AUC-ROC) and the classification efficiency rates in high () and low () susceptibility were used to compare the results of the approaches. The results demonstrated that although RF (AUC-ROC=0,947, =6,808, =0,030) slightly outperformed LR (AUC-ROC=0,936, =5,695, =0,050) and ANN (AUC-ROC=0,934, =6,495, =0,060), all the approaches exhibited high predictive capability in identifying areas susceptible to landslides.

Abstract The objective of this work was to compare the performances of logistic regression and machine learning algorithms to predict infection caused by Fasciola hepatica in cattle. A dataset on 30,151 bovines from Uruguay was used. Logistic regression (LR) and the algorithms k-nearest neighbor (KNN), classification and regression trees (CART), and random forest (RF) were compared. The interquartile range (IQR) and z-score were used to improve the classification and compared to each another. Sex, age, carcass conformation score, fat score, productive purpose, and carcass weight were used as independent variables for all algorithms. Infection by F. hepática was used as a binary dependent variable. The accuracies of LR, KNN, CART, and RF were 0.61, 0.57, 0.57, and 0.58, respectively. The variable importance of LR showed that adult cattle tended to be infected by F. hepatica. All models showed low accuracy, but LR successfully distinguished variables related to F. hepatica. Both the IQR and z-score show similar results in improving the classification metrics for the used dataset. In the dataset, data related to climate or factors such as body weight can improve the reliability of the model in future studies.

ABSTRACT OBJECTIVE: To develop and validate a predictive model utilizing machine-learning techniques for estimating the length of hospital stay among patients who underwent coronary artery bypass grafting. METHODS: Three machine learning models (random forest, extreme gradient boosting and neural networks) and three traditional regression models (Poisson regression, linear regression, negative binomial regression) were trained in a dataset of 9,584 patients who underwent coronary artery bypass grafting between January 2017 and December 2021. The data were collected from hospital discharges from 133 centers in Brazil. Algorithms were ranked by calculating the root mean squared logarithmic error (RMSLE). The top performing algorithm was validated in a never-before-seen database of 2,627 patients. We also developed a model with the top ten variables to improve usability. RESULTS: The random forest technique produced the model with the lowest error. The RMLSE was 0.412 (95%CI 0.405–0.419) on the training dataset and 0.454 (95%CI 0.441–0.468) on the validation dataset. Non-elective surgery, admission to a public hospital, heart failure, and age had the greatest impact on length of hospital stay. CONCLUSIONS: The predictive model can be used to generate length of hospital stay indices that could be used as markers of efficiency and identify patients with the potential for prolonged hospitalization, helping the institution in managing beds, scheduling surgeries, and allocating resources.

Objetivo: Descrever o processo de desenvolvimento de uma IA com o uso de Machine Learning para a tomada de decisões relacionadas ao Covid-19. Metodologia: Este estudo quantitativo, descritivo e exploratório utilizou dados secundários de domínio público dos sistemas da rede SUS, coletados na plataforma OpenDataSUS e na rede municipal de saúde. O objetivo foi descrever o processo de desenvolvimento de uma IA chamada VIGIEXCELÊNCIA com o uso de Machine Learning para tomada de decisão rápida em resposta à Covid-19. Resultado: Pelo meio do uso do machine learning o algoritmo foi capaz de realizar avaliações e fornecer respostas rápidas com base em modelos preditivos. Conclusão: O uso de IA na vigilância epidemiológica melhora a prestação de serviço de saúde.

The integration of artificial intelligence (AI) into psychiatric diagnosis heralds a new era in mental health care, offering unprecedented opportunities to enhance diagnostic accuracy, personalize treatment, and streamline clinical workflows. A systematic approach was utilized, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. This literature review explores the current state of AI in psychiatric diagnosis, highlighting key technologies such as machine learning, natural language processing, and deep learning. We discuss the application of these technologies across various psychiatric disorders, including depression, anxiety, and schizophrenia. While AI holds immense promise, significant challenges remain, including issues of data privacy, model bias, and the clinical validation of AI tools. Furthermore, ethical and regulatory considerations must be addressed to ensure responsible implementation. This review also examines the potential future directions of AI in psychiatry, emphasizing the importance of collaboration between AI systems and human clinicians. As the field evolves, AI has the potential to transform psychiatric practice, offering new avenues for early detection, personalized care, and therapeutic monitoring.

Abstract Objective To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography. Data source Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with the search terms “Artificial Intelligence,” “Mammography,” and their respective MeSH terms. We filtered publications from the past ten years (2014 – 2024) and in English. Study selection A total of 1,878 articles were found in the databases used in the research. After removing duplicates (373) and excluding those that did not address our PICO question (1,475), 30 studies were included in this work. Data collection The data from the studies were collected independently by five authors, and it was subsequently synthesized based on sample data, location, year, and their main results in terms of AUC, sensitivity, and specificity. Data synthesis It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence. When used in conjunction with radiologists, statistically higher accuracy in mammogram evaluation was reported compared to the assessment by radiologists alone. Conclusion AI algorithms have emerged as a means to complement and enhance the performance and accuracy of radiologists. They also assist less experienced professionals in detecting possible lesions. Furthermore, this tool can be used to complement and improve the analyses conducted by medical professionals.

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

Background: The rising global population and decreasing agricultural land challenge the food supply, worsened by land degradation and water scarcity. Using IoT solutions, precision farming offers promise for addressing these issues by enhancing productivity, profitability, and environmental sustainability. The paper proposes an IoT-based automated monitoring and irrigation system with predictive analysis as a solution. Aims: To develop an IoT-based irrigation model with cloud computing, a mobile app for remote monitoring and irrigation control, and a predictive analysis method for forecasting weather conditions. Methods: IoT sensors are connected to a node MCU, and they monitor real-time temperature, humidity, and soil moisture. A water pump adjusts irrigation accordingly. Data is sent to ThingSpeak for visualization and Firebase for storage and is accessible via a mobile app. Predictive analysis combines sensor and historical weather data using the Random Forest algorithm to optimize irrigation. This determines the ideal irrigation frequency, duration, and timing based on predicted temperature and soil moisture levels, ensuring optimal soil moisture for plant growth. Results: The system exhibited encouraging outcomes. It displayed transformative potential by harnessing IoT, cloud computing, and predictive analytics. Offering precise soil moisture monitoring, instant data access, and tailored advice, the system can elevate crop management, streamline water consumption, and boost productivity among Indian farmers. Discussion: The automated crop monitoring and irrigation system has transformative potential in farming. Some future scope and enhancements may include broadening crop compatibility, integrating satellite data, enhancing pest monitoring, improving connectivity, enriching the mobile app, scaling up through partnerships, and refining the predictive models. Conclusions: The IoT-driven crop monitoring system revolutionizes Indian agriculture by optimizing irrigation and integrating weather forecasts. With mobile app enhancements and future improvements, it promises sustainable farming and empowerment for farmers. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering 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.)

En la era de la gobernanza digital, la utilización creciente de datos y tecnologías de Big Data e Inteligencia Artificial en los sistemas de protección social se ha convertido en un asunto relevante. Los esfuerzos del Estado por impulsar la datificación ciudadana y la implementación de la IA para la gestión y provisión de servicios y beneficios sociales están vehiculizados por las ideas de neutralidad, objetividad y eficiencia tecnológica. No obstante, estas tecnologías no solo pueden asignar beneficios sociales; sino que también tienen el potencial de controlar, monitorear, sancionar y excluir a los beneficiarios. Ante el tecno-optimismo que marca el diseño de la política social contemporánea, específicamente sus mecanismos de asignación, este trabajo apuesta por los Estudios de la Ciencia, Tecnología y Sociedad para dar luz sobre cómo los sistemas de datos y las tecnologías de IA transforman la noción de pobreza e impactan en la definición de beneficiarios y no beneficiarios. Este enfoque se centra en desentrañar la dimensión política –de la politics– de los sistemas de datos y las tecnologías de la IA que comienzan a mediar la relación entre el Estado y los ciudadanos. A través del estudio del caso ecuatoriano del Registro Social, el objetivo recae en nutrir el examen crítico de los Estados digitales de bienestar en la región, analizando sus implicaciones en la sociedad y su potencial para producir o amplificar desigualdad y exclusión; desafiando las visiones deterministas del cambio tecnológico y del diseño de políticas públicas.

Abstract Introduction: Chronic kidney disease (CKD) and metabolic syndrome (MS) are recognized as public health problems which are related to overweight and cardiometabolic factors. The aim of this study was to develop a model to predict MS in people with CKD. Methods: This was a prospective cross-sectional study of patients from a reference center in São Luís, MA, Brazil. The sample included adult volunteers classified according to the presence of mild or severe CKD. For MS tracking, the k-nearest neighbors (KNN) classifier algorithm was used with the following inputs: gender, smoking, neck circumference, and waist-to-hip ratio. Results were considered significant at p < 0.05. Results: A total of 196 adult patients were evaluated with a mean age of 44.73 years, 71.9% female, 69.4% overweight, and 12.24% with CKD. Of the latter, 45.8% had MS, the majority had up to 3 altered metabolic components, and the group with CKD showed statistical significance in: waist circumference, systolic blood pressure, diastolic blood pressure, and fasting blood glucose. The KNN algorithm proved to be a good predictor for MS screening with 79% accuracy and sensitivity and 80% specificity (area under the ROC curve – AUC = 0.79). Conclusion: The KNN algorithm can be used as a low-cost screening method to evaluate the presence of MS in people with CKD.

Objective: to describe the development of a predictive nursing workload classifier model, using artificial intelligence. Method: retrospective observational study, using secondary sources of electronic patient records, using machine learning. The convenience sample consisted of 43,871 assessments carried out by clinical nurses using the Perroca Patient Classification System, which served as the gold standard, and clinical data from the electronic medical records of 11,774 patients, which constituted the variables. In order to organize the data and carry out the analysis, the Dataiku® data science platform was used. Data analysis occurred in an exploratory, descriptive and predictive manner. The study was approved by the Ethics and Research Committee of the institution where the study was carried out. Results: the use of artificial intelligence enabled the development of the nursing workload assessment classifier model, identifying the variables that most contributed to its prediction. The algorithm correctly classified 72% of the variables and the area under the Receiver Operating Characteristic curve was 82%. Conclusion: a predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient’s electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.

ABSTRACT With the increasing soybean production in Brazil, and the demand for soybeans with high protein and oil content, it is essential to conduct an in-depth study of the constituents of this grain, which can vary according to genotypes and growing conditions. Therefore, the objective of this study was to classify soybean genotypes, cultivated in different environments and sowing seasons, according to their chemical composition and the spectrum generated by near-infrared spectroscopy (NIRS). For this purpose, artificial intelligence and its machine learning technique were employed. 10 soybean genotypes were used, sown in two sowing seasons and cultivated 7 cities in Rio Grande do Sul. The chemical composition of the samples was analyzed using the FOSS NIRS DS2500 equipment, selecting the band between 807 and 817 nm. The applied algorithms were J48, Random Forest, CVR, lBk, MLP, using the Resample filter. The Weka software, version 3.8.6, was employed for data mining. The IBk algorithm achieved the best performance, reaching 89% correct classification of attributes. From the Confusion Matrix, it was observed that all genotypes obtained results above 60/70 for correctly predicted values, highlighting the algorithms’ good performance. In the metrics, IBk achieved 0.89 Precision, Recall, and F-Measure, and 0.94 ROC Area. Thus, it was possible to classify the genotypes according to their chemical composition related to the data obtained in the spectral curve, sowing season, and environment, using artificial intelligence and machine learning.

Nesta entrevista com a pesquisadora, artista e professora livre-docente Giselle Beiguelman (FAU-USP), são abordadas questões latentes disparadas pelas recentes tecnologias de Inteligência Artificial. Como pano de fundo das discussões, está o livro Políticas da Imagem: vigilância e resistência na dadosfera (2021), da própria entrevistada. São tratados temas como os processos artísticos e suas possibilidades frente às novas tecnologias, o debate ético (mas também estético) em torno dos deepfakes, a integração entre as visualidades e a política na contemporaneidade, a noção de preservação digital frente às mudanças tecnológicas e a viabilidade de se construir relações de aprendizado mútuo com as máquinas.

ABSTRACT Traditional hydrological models have been widely used in hydrologic studies, providing credible representations of reality. This paper introduces a hybrid model that combines the traditional hydrological model Soil Moisture Accounting Procedure (SMAP) with the machine learning algorithm XGBoost. Applied to the Sobradinho watershed in Brazil, the hybrid model aims to produce more precise streamflow forecasts within a three-month horizon. This study employs rainfall forecasts from the North America Multi Model Ensemble (NMME) as inputs of the SMAP to produce streamflow forecasts. The study evaluates NMME forecasts, corrects bias using quantile mapping, and calibrates the SMAP model for the study region from 1984 to 2010 using Particle Swarm Optimization (PSO). Model evaluation covers the period from 2011 to 2022. An XGBoost model predicts SMAP residuals based on the past 12 months, and the hybrid model combines SMAP's streamflow forecast with XGBoost residuals. Notably, the hybrid model outperforms SMAP alone, showing improved correlation and Nash-Sutcliffe index values, especially during periods of lower streamflow. This research highlights the potential of integrating traditional hydrological models with machine learning for more accurate streamflow predictions.

Objetivo: o objetivo deste artigo é apresentar uma revisão da literatura empírica recente a respeito do uso de métodos de screening (ou filtros econômicos) para a detecção de cartéis no Brasil e no mundo. Tais métodos têm sido cada vez mais utilizados por autoridades antitruste na medida em que complementam e reforçam métodos reativos tradicionais de detecção de cartel, como acordos de leniência. Há diversos estudos que discutem suas aplicações, a maioria com foco na abordagem de screening comportamental. Este artigo visa a complementar tais trabalhos de duas maneiras: (i) avaliar a literatura mais recente sobre o tema, a qual tem se especializado, por exemplo, em metodologias de machine learning; e (ii) discutir aplicações de abordagens estruturais, que são métodos mais simplificados de detecção do que o screening comportamental, mas também utilizados por autoridades antitruste e na literatura. Método: revisão e análise qualitativa da literatura sobre detecção de cartel nacional e internacional. Conclusões: diversas autoridades antitruste têm seguido as recomendações de organizações internacionais de combinar o uso de métodos reativos com proativos na detecção de cartéis. Há iniciativas importantes no uso de screenings, especialmente no âmbito de licitações públicas dada a maior disponibilidade de dados. Na literatura empírica técnicas de machine learning têm ganhado espaço seja devido aos desafios de identificação dos modelos econométricos, seja pelo contexto de aumento na complexidade do funcionamento dos acordos colusivos. Trata-se de uma nova tendência que está voltada principalmente à maior previsibilidade e precisão no processo de detecção de cartéis, em detrimento de conclusões sobre causalidade. Tendo em vista as diferentes dinâmicas setoriais e regionais na economia, sua utilização ainda exige cautela por parte dos pesquisadores e autoridades antitruste, mas certamente representa avanço relevante e promissor no combate a cartéis.

Machine Learning (ML) is a powerful artificial intelligence branch that can help businesses, whether small or large, in a variety of industries. It is an option for replacing resources with high operating costs. The aim of this study was to use the Edge Impulse platform as an ML tool option. The system applies low-code frameworks which abstracts a series of complex techniques applied in ML, such as data processing and AI components structure. It implies a time reduction during the development period. Using Edge Impulse allows a more user-friendly interface alternative with an easy-to-interpret logic flow. The study focused an application to do an object recognition, aiming the system capacity limit. The autogenerated accuracy value, pointed by the system indicated 97.9 % after the training step and 89 % after retesting the first 20 mices, photographed in different image angles, indicating a possible model overfitting. Even though, the system showed promise in terms of classifying objects. Some adjustments in the image dataset can improve the model capacity of recognition, as the amount of images showed insufficient at the survey's conclusion.

Se estima que en España hay alrededor de 70.000 balsas. Según la legislación vigente, usuarios y administraciones tienen la obligación de clasificarlas en función del riesgo potencial frente una eventual rotura si tienen más de 100.000 m3 o el dique mide más de 5 m. Con el objetivo de construir un sistema de ayuda para su clasificación que facilite y agilice este proceso, se ha llevado a cabo el proyecto ACROPOLIS. A tal fin, la herramienta de modelización numérica Iber fue elemento esencial y de carácter transversal. Por un lado, el empleo de Iber permitió desarrollar un método simplificado para pre-clasificar balsas en base a técnicas de machine learning y, por otro lado, se desarrollaron metodologías ad hoc en Iber que agilizan y automatizan los procesos, como la extracción automática de información hidráulica y de peligrosidad en puntos vulnerables y la paralelización del código para el cálculo mediante técnicas de computación de alto rendimiento.