An artificial neural network model for the prediction of entrained droplet fraction in annular gas-liquid two-phase flow in vertical pipes

Aliyu, Aliyu M.; Choudhury, Raihan; Sohani, Behnaz; Atanbori, John; Ribeiro, Joseph X. F.; Ahmed, Salem K. Brini; Mishra, Rakesh

doi:10.1016/j.ijmultiphaseflow.2023.104452

Cited by 13 publications

(1 citation statement)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enhance prediction accuracy and expedite model convergence, it was imperative for the data to be normalized within a specific range. The min-max normalization strategy was employed to ensure that both input and target values resided within the [0, 1] range, which is optimal for the activation function’s performance [ 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Machine learning in epidemiology: Neural networks forecasting of monkeypox cases

Alnaji

2024

PLoS ONE

View full text Add to dashboard Cite

This study integrates advanced machine learning techniques, namely Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit models, to forecast monkeypox outbreaks in Canada, Spain, the USA, and Portugal. The research focuses on the effectiveness of these models in predicting the spread and severity of cases using data from June 3 to December 31, 2022, and evaluates them against test data from January 1 to February 7, 2023. The study highlights the potential of neural networks in epidemiology, especially concerning recent monkeypox outbreaks. It provides a comparative analysis of the models, emphasizing their capabilities in public health strategies. The research identifies optimal model configurations and underscores the efficiency of the Levenberg-Marquardt algorithm in training. The findings suggest that ANN models, particularly those with optimized Root Mean Squared Error, Mean Absolute Percentage Error, and the Coefficient of Determination values, are effective in infectious disease forecasting and can significantly enhance public health responses.

show abstract

Section: Methodsmentioning

confidence: 99%