Knowledge workers mental workload prediction using optimised ELANFIS

The competitive society in the new era calls for more research to improve the well-being of workers as well as to improve their productivity. Knowledge workers face a high mental workload in terms of planning and coordination. One solution is to predict the mental workload of knowledge workers. Some machine learning models have been implemented for mental workload prediction, but deep learning models are yet to be introduced for this purpose. Deep learning models are superior to machine learning models because of their ability to correct inaccurate predictions if they ever occur. Therefore, this study aims to optimize the extreme learning adaptive neuro-fuzzy inference system (ELANFIS) by integrating particle swarm optimization into a micro-genetic algorithm to predict the mental workload of knowledge workers. Although the adaptive neuro-fuzzy inference system (ANFIS) shows reasonable prediction performance, it also suffers from the curse of dimensionality and has a poor computation time. Thus, ELANFIS is introduced because its curse of dimensionality is less severe when solving problems with a high number of input dimensions. The integration of the advantages of a micro-genetic algorithm and particle swarm optimization is suggested to optimize the premise parameters of ELANFIS, as this can allow better solutions to be located at a faster rate. The proposed model yields promising prediction results, with improvements of 6.0665 in the Mean Squared Error(MSE) and 1.279 in the Root Mean Squared Error (RMSE) for regression; the proposed model even surpasses the prediction results of ELANFIS optimized with PSO alone, with improvements of 1.5369 in MSE and 0.4094 in RMSE for regression. The findings are expected to assist employers in determining an appropriate working lifestyle for their employees.