Boosting Model of Attention Network Task

Human attention is a limited resource that makes it valuable. Moreover, it is the first step in human brain cognition. Multiple factors affect their performance. The cognitive model of attention helps to understand the complexity of its role and involved factors. This study aimed at developing a novel machine-learning system of human care by different psychological tasks and collected information, which able to predict attention scores. A web-based system is developed for multiple cognitive tasks to gather information. The proposed model evaluates the human attention performance level by the Attentional Network Test (ANT). Data were collected from 62 participants. Decision tree, AdaBoost, and Gradient boosting models were developed, and the hyperparameters model was tuned. The model selection is based on finding performance. The performance was analyzed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AdaBoost regression shows the best performance with MAE: 0.69 and RMSE: 3.9198 in comparison with other models. The AdaBoost model ensures the rapid and accurate estimation performance of attention.

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