01956nas a2200253   4500000000100000000000100001008004100002260001200043653002000055653001800075653002100093653001100114653002300125653002100148100001600169700002400185700002000209245010000229856007900329300000900408490000600417520126500423022001401688       2021                            d  c09/202110aCloud Computing10aFog Computing10aMachine Learning10aHealth10aQuality of Service10aData Segregation1 aAmit Kishor1 aChinmay Chakraborty1 aWilson Jeberson00aA Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning  uhttps://www.ijimai.org/journal/sites/default/files/2021-08/ijimai6_7_1.pdf  a7-170 v63 aIn the recent scenario, the most challenging requirements are to handle the massive generation of multimedia data from the Internet of Things (IoT) devices which becomes very difficult to handle only through the cloud. Fog computing technology emerges as an intelligent solution and uses a distributed environment to operate. The objective of the paper is latency minimization in e-healthcare through fog computing. Therefore, in IoT multimedia data transmission, the parameters such as transmission delay, network delay, and computation delay must be reduced as there is a high demand for healthcare multimedia analytics. Fog computing provides processing, storage, and analyze the data nearer to IoT and end-users to overcome the latency. In this paper, the novel Intelligent Multimedia Data Segregation (IMDS) scheme using Machine learning (k-fold random forest) is proposed in the fog computing environment that segregates the multimedia data and the model used to calculate total latency (transmission, computation, and network). With the simulated results, we achieved 92% as the classification accuracy of the model, an approximately 95% reduction in latency as compared with the pre-existing model, and improved the quality of services in e-healthcare.  a1989-1660