The Dynamic Movement of Disaster Management Systems Based on Vehicle Networks and Applied on the Healthcare System

Abstract

In order to save human life and assets, the emergency management system (DMS) requires roving rescue teams to respond promptly and effectively. Installation and restoration of appropriate communication infrastructure are important for reducing the effect of disasters and enabling and coordinating information flow among relief teams working in the region. This paper describes a data collection system based on vehicular cloud network services that incorporates the advantages of both architectures of vehicular ad hoc networks (VANETs) with the cloud to establish vehicular cloud networks (VCNs). Vehicles in the current plan perform tasks like monitoring the environment, gathering data, and transmitting data to the control center depending on their positions and instructions. To build a disaster management system, the proposed system uses hybrid wireless networking, which includes both a central system and ad hoc networks. The implementation results show that the suggested system is more dependable and efficient; even light density is improved in terms of reachability with few hops. Furthermore, as compared to the existing system, the suggested system has a lower end-to-end delay and a higher packet delivery ratio.

Applying Dynamic Systems to Social Media by Using Controlling Stability

“…In the arrangement of values for a parameter k , [ 10 ] has proposed another 3D attractor that shows chaotic behavior in distinct respects and not diffeomorphic with Lorenz [ 11 – 18 ]. The first chaotic nonlinear system has been suggested by Lorenz [ 19 – 22 ] in which is a generalization of the Lorenz system. The Lorenz system's messy structure is utilized.…”

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric

“…Studies such as those reviewed to carry out this work have been able to design applications with very high precision, although always bearing in mind that a radiology professional would have to validate the prediction [10][11][12][13][14][15][16]. In this project, data augmentation methods have been used to obtain a classification model with a precision percentage of 78.37%.…”

[Retracted] Human‐Computer Interaction Using Manual Hand Gestures in Real Time

“…In this way, if luminance information is used, those pixels that exceed a set threshold are considered skin and must be taken into account for further analysis. If good lighting and a sufficiently opaque background are guaranteed, a threshold can be set at which good segmentation can be achieved [ 11 ].…”