|
|
|
|
|
Fátima Trindade Neves, Manuela Aparicio and Miguel de Castro Neto
In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial tas...
ver más
|
|
|
|
|
|
|
|
|
Nirmal Acharya, Padmaja Kar, Mustafa Ally and Jeffrey Soar
Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental hea...
ver más
|
|
|
|
|
|
|
|
|
Jason Cornelius, Sven Schmitz, Jose Palacios, Bernadine Juliano and Richard Heisler
This work details the development and validation of a methodology for high-resolution rotor models used in hybrid Blade Element Momentum Theory Unsteady Reynolds Averaged Navier?Stokes (BEMT-URANS) CFD. The methodology is shown to accurately predict sing...
ver más
|
|
|
|
|
|
|
|
|
Sunny Kumar Poguluri and Yoon Hyeok Bae
The incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not underg...
ver más
|
|
|
|
|
|
|
|
|
Kevin Mero, Nelson Salgado, Jaime Meza, Janeth Pacheco-Delgado and Sebastián Ventura
Unemployment, a significant economic and social challenge, triggers repercussions that affect individual workers and companies, generating a national economic impact. Forecasting the unemployment rate becomes essential for policymakers, allowing them to ...
ver más
|
|
|
|