Mostrar el registro sencillo del ítem

dc.contributor.authorGupta, Neeraj
dc.contributor.authorKhosravy, Mahdi
dc.contributor.authorPatel, Nilesh
dc.contributor.authorDey, Nilanjan
dc.contributor.authorGonzález-Crespo, Rubén
dc.description.abstractSmart monitoring of off-road vehicles is cursed by their complex and expensive IoT sensors technologies. High dependence on the cloud/fog computation, availability of the network, and expert knowledge make it handicap in the rural off-network areas. Use of edge devices, such as smartphones, attributed by computation capabilities is the solution that is yet to be developed at commercial level (Fawwaz and Chung 2020) and (Zhengwei et al., 2021). Additionally, the user's growing demand for economic and user-friendly technology motivates to shift from costly and complex sensors to economic. In this article, we present the hybridized computational intelligence methodology to develop an edge-device-enabled AI technology for health monitoring and diagnosis (HM&D) of the off-road vehicles, taking use of super economic microphones as sensors. Smartphones are benefited by integrated microphones, and thus, the App-based developed technology is generalized for all vehicles from old to new. Enhanced selection and log-scaled mutation genetic algorithms is used to evolve the structure of the artificial neural network toward an optimally lightweight structure. Each evolved lightweight ANN structure is trained by scaled conjugate gradient back-propagation training algorithm to optimize corresponding weights and biases. The comparative results with currently reported genetic algorithms for edge computation prove it a breakthrough technology for edge-device-enabled HM&D of off-road vehicles (Yan et al., 2020).es_ES
dc.publisherIEEE Computer Societyes_ES
dc.relation.ispartofseries;vol. 18, nº 1
dc.subjectartificial intelligence (AI)es_ES
dc.subjectartificial neural network (ANN)es_ES
dc.subjectedge computationes_ES
dc.subjectgenetic algorithm (GA)es_ES
dc.subjectoff-road vehicleses_ES
dc.titleLightweight Computational Intelligence for IoT Health Monitoring of Off-Road Vehicles: Enhanced Selection Log-Scaled Mutation GA Structured ANNes_ES

Ficheros en el ítem


No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem