Analyzing the Predictive Power of Machine Learning Models for Autism Detection

This study delves into the application of machine learning models for the early detection of Autism Spectrum Disorder (ASD). Early diagnosis and intervention are critical for improving the lives of individuals with ASD and their families. This research compares various machine learning models, including Decision Tree, Random Forest, Support Vector Machine, k-Nearest Neighbors, and more, assessing their performance based on key metrics such as F1-Score, accuracy, precision, and recall. The study reveals the Multi-layer Perceptron (MLP) as the top-performing model with an impressive F1-Score of 79.35%, demonstrating its potential for accurate ASD detection. The feature importance analysis highlights the significant roles of gender, genetic predisposition, age at diagnosis, and ethnicity-related features in predicting ASD. This study underscores the promise of machine learning in ASD detection and emphasizes the importance of early intervention and personalized approaches to diagnosis

Classification Models Analysis for Stroke Prediction

This study explores the application of machine learning in the prediction of stroke occurrences, a critical task in healthcare with the potential to save lives and reduce the impact of this life-altering medical event. Leveraging the "Healthcare Stroke Data" dataset, we employed two powerful classification models, the Random Forest and Support Vector Machine (SVM), to forecast stroke likelihood. Our analysis encompasses data preprocessing, model training, and comprehensive evaluation using classification metrics and confusion matrices. The study reveals the trade-offs between accuracy, recall, precision, and the F1 score in both models. While the Random Forest exhibits higher accuracy, the SVM excels in recall, a crucial factor in healthcare. Precision challenges in both models highlight the need for further refinement. Additionally, we conducted a feature importance analysis, emphasizing the pivotal role of age, BMI, and glucose levels in stroke prediction. This work exemplifies the potential of machine learning in healthcare and contributes to ongoing efforts in improving stroke prediction and prevention

ESTUDO DE EFICIÊNCIA ENERGÉTICA APLICADO A UM AEROGERADOR DO TIPO SAVONIUS

A energia elétrica é um dos principais temas que está sendo debatido entre governos e empresas, pois existe uma demanda muito alta com o avanço da tecnologia desde das décadas passadas, a principal forma de suprir essa diligência, foi optando por energias renováveis, destacando-se a energia eólica, cujo foco é a transformação de energia dos ventos em energia útil, a obtenção do mesmo, é antiga, utilizada nos moinhos para gerar energia mecânica, e tendo evolução para geração de energia elétrica nos dias atuais, o mesmo, apresenta um impacto ambiental praticamente nulo. Os geradores eólicos apresentam duas formas distintas, de eixo horizontal, são as mais usuais, por serem mais produtivas em ventos atrativos em sua rota e eixo vertical, relativamente novas no mercado, usadas para produção de ventos menos atrativos. Assim, esse artigo visa os estudos em aerogeradores de eixo vertical, desde sua estrutura até sua potência em produção máxima, permitindo assim visualizar sua eficiência energética e relação de potência