Design of P-FLANN Model for Intelligent Water Fountain Sound Pleasantness Monitoring Using Bio-inspired Computing and Human Speech Perception

Abstract

Cognitive-inspired Computational Computing systems play a crucial role in designing intelligent health monitoring systems which help both patients and hospitals. It also helps in early and consistent decision-making for various health issues including human psychological health. Water fountains built in parks and public spaces are used as decorative instruments which not only give appealing visuals but also it provides a relaxing environment to the visitors. These natural sounds have a direct effect on the psychological health of visitors. Very few research works are reported on developing the relationship between water sounds and their corresponding psychological impact. This assessment needs trained manpower and a lot of experimental time which is costly and may not be always available. In this paper to access the human health-friendly water fountain sounds from the pleasantness, a Perceptually Weighted functional link artificial neural network (P-FLANN) model is developed. To reduce the computational complexity of training and for faster convergence, swam intelligence-based optimization algorithm is used for updating the weights. It is observed from the comparative simulation results that the proposed P-FLANN model can effectively perform prediction tasks which is not only cost-effective but also 95% accurate and can play a crucial role in designing human health-friendly water fountains in smart cities.