Congestion Aware WSN-IoT-Application Layer Protocols for Healthcare Services

In the healthcare industry, WSN-IoT networks can be used to gather patient data for statistical purposes. IoT-based application-level protocols do not take into account these facts while forwarding the data to the gateway or server, which may degrade the network performance if the data was collected from a patient with ordinary/critical health issues and the route was busy or congested. In this paper, we'll look at the performance of two application layer protocols (i.e. CoAP and MQTT) within the constraints of a scalable network by integrating a congestion-aware scheme with them

Multi-class segmentation skin diseases using improved tuna swarm-based U-EfficientNet

Abstract Early location of melanoma, a dangerous shape of skin cancer, is basic for patients. Indeed, for master dermatologists, separating between threatening and generous melanoma could be a troublesome errand. Surgical extraction taken after early determination of melanoma is at its way to dispense with the malady that will result in passing. Extraction of generous injuries, on the other hand, will result in expanded dismalness and superfluous wellbeing care costs. Given the complexity and likeness of skin injuries, it can be troublesome to create an accurate determination. The proposed EfficientNet and UNet are combined and arrange to extend division exactness. Also, to decrease data misfortune amid the learning stage, adjusted fish swarm advancement (IMSO) is utilized to fine-tune the U-EfficientNet’s movable parameters. In this paper, a ViT-based design able to classify melanoma versus noncancerous injuries is displayed. On the HAM1000 and ISIC-2018 datasets, the proposed ViT demonstrated accomplished the normal precision of 99.78% and 10.43% FNR with computation time of 134.4632s of ISIC-2018 datasets. The proposed ViT show accomplished the normal exactness of 99.16% and 9.38% FNR in with computation time of 133.4782s of HAM1000 dataset