One of the most challenging aspects of agricultural pest control is accurate detection of insects in crops. Inadequate control measures for insect pests can seriously impact the production of corn and soybean plantations. In recent years, artificial intelligence (AI) algorithms have been extensively used for detecting insect pests in the field. In this line of research, this paper introduces a method to detect four key insect species that are predominant in Brazilian agriculture. Our model relies on computer vision techniques, including You Only Look Once (YOLO) and Detectron2, and adapts them to lightweight formats—TensorFlow Lite (TFLite) and Open Neural Network Exchange (ONNX)—for resource-constrained devices. Our method leverages two datasets: a comprehensive one and a smaller sample for comparison purposes. With this setup, the authors aimed at using these two datasets to evaluate the performance of the computer vision models and subsequently convert the best-performing models into TFLite and ONNX formats, facilitating their deployment on edge devices. The results are promising. Even in the worst-case scenario, where the ONNX model with the reduced dataset was compared to the YOLOv9-gelan model with the full dataset, the precision reached 87.3%, and the accuracy achieved was 95.0%.

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder after Alzheimer's disease, affects approximately 10 million individuals worldwide. The disease is characterized by both motor and non-motor symptoms, and clinical aspects are pivotal for diagnosis. Vocal abnormalities can be identified in about 90% of PD patients in the early stages of the condition. Machine Learning (ML), a prominent subfield of Artificial Intelligence (AI), holds significant promise in the medical domain, particularly for early disease detection, enabling effective preventive measures and treatments. In this paper, we considered the unique characteristics of each ML algorithm. Seventeen ML algorithms were applied to a dataset of voice recordings from Healthy Control and PD individuals, sourced from a publicly available repository. We leveraged the PyCaret Python library's ML algorithms and functions, which were introduced in this article, to demonstrate their simplicity and effectiveness in dealing with real-world data. Among these algorithms, Extra Trees Classifier (ETC), Gradient Boosting Classifier (GBC), and K Neighbors Classifier (KNN) exhibited the best performance for the given dataset. Furthermore, to enhance the models' performance, we employed various techniques, including Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, feature selection based on correlation, and hyperparameter tuning. Our findings highlight the potential of the PyCaret ML library demonstrated in this article as a valuable tool for applying ML to the classification of Parkinson's disease through voice analysis. The application of ML in this context can greatly support clinical decision-making, leading to more informed and precise interventions.

Background: Inefficient use of public funds can have a negative impact on the lives of citizens. The development of machine learning-based technologies for data visualisation and prediction has opened the possibility of evaluating the accountability of publicly funded projects. Methods: This study describes the conception and evaluation of the architecture of a system that can be utilised for project profile definition and prediction. The system was used to analyse data from 20,942 System of Management of Agreements and Transfer Contracts (SICONV) projects in Brazil, which are government-funded projects. SICONV is a Brazilian Government initiative that records the entire life cycle of agreements, transfer contracts, and partnership terms, from proposal formalisation to final accountability. The projects were represented by seven variables, all of which were related to the timeline and budget of the project. Data statistics and clustering in a lower-dimensional space calculated using t-SNE were used to generate project profiles. Performance measures were used to test and compare several project-profile prediction models based on classifiers. Results: Data clustering was achieved, and ten project profiles were defined as a result. Among 25 prediction models, k-Nearest-Neighbor (kknn) was the one that yielded the highest accuracy ($0.991\pm 0.002$). Conclusions: The system predicted SICONV project profiles accurately. This system can help auditors and citizens evaluate new and ongoing project profiles, identifying inappropriate public funding.

Levando-se em conta as políticas públicas de apoio ao desenvolvimento da agricultura familiar, especificamente no âmbito da disponibilização de recursos por meio de compras públicas em convênios vinculados ao Ministério da Agricultura, Pecuária e Abastecimento (MAPA), este artigo tem como objetivo a realização de um estudo exploratório descritivo qualitativo, por meio da aplicação de métodos de mineração de dados descritiva, para a identificação dos possíveis impactos decorrentes da pandemia da COVID-19 sobre agricultura familiar, no que tange à disponibilização de recursos públicos vinculados à convênios do MAPA. São utilizados dados de convênios provenientes do MAPA, submetidos a tratamentos específicos para identificação de registros que se enquadrem como recursos de potencial apoio à agricultura familiar, e dados do Ministério da Saúde sobre a evolução das infecções da Covid-19.

Parkinson’s disease (PD) is a neurological condition that affects approximately 10 million individuals globally and is ranked as the second most prevalent neurodegenerative condition after Alzheimer’s disease. Vocal disorders can be identified in approximately 90% of PD patients in the early stages of the disease. In this study, 19 machine learning (ML) algorithms were applied to a database of voice recordings of healthy individuals and individuals with PD obtained from a public repository. Different feature selection (FS) and hyperparameter optimization techniques were applied to all models for training, testing, and validation data. Among the ML algorithms, support vector machine with radial kernel, Naïve Bayes (NB), and Gaussian process classifier (GPC) yielded promising results when considering all features. Linear discriminant analysis, K neighbors classifier (KNN), extra trees classifier, GPC, and NB demonstrated excellent performance on the testing data after employing FS techniques. Decision tree classifier, KNN, and GPC emerged as the top performers when applied to the validation dataset. Our findings, derived from an extensive and chronological review of studies utilizing the same dataset, which surpass previous benchmarks, provide a comprehensive understanding of ML’s application in voice analysis to support accurate clinical decision-making.

Embora a agricultura familiar seja proporcionalmente menor em termos de ocupação territorial, ela é considerada como a principal responsável pela produção de alimentos que é disponibilizada para a população do Brasil. Apesar de tal importância, ainda são muitos os desafios e obstáculos a serem superados, os quais permeiam questões como melhores políticas públicas e incentivos governamentais. Neste cenário, este estudo tem o objetivo de realizar um panorama da visão da agricultura familiar por parte da cobertura jornalística no âmbito do portal de notícias G1. As análises realizadas são baseadas no uso de soluções computacionais de mineração de dados. Os resultados obtidos apresentam detalhes quanto aos principais assuntos abordados e o volume quantitativo de peças veiculadas.