Security-Aware Routing on Wireless Communication for E-Health Records Monitoring Using Machine Learning

An ad hoc structure is self-organizing, self-forming, and system-free, with no nearby associations. One of the significant limits we must focus on in frameworks is leading. As for directions, we can send the packet or communications from the sender to the recipient node. AODV Routing Protocol, a short display that will make the tutorial available on demand. Machine Learning (ML) based IDS must be integrated and perfected to support the detection of vulnerabilities and enable frameworks to make intrusion decisions while ML is about their mobile context. This paper considers the combined effect of stooped difficulties along the way, problems at the medium get-right-of-area to impact layer, or pack disasters triggered by the remote control going off route. The AODV as the Routing MANET protocol is used in this work, and the process is designed and evaluated using Support Vector Machine (SVM) to detect the malicious network nodes.

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

<p>Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification.</p>

Who Bears the Burden of a Pandemic? COVID-19 and the Transfer of Risk to Digital Platform Workers

In this paper, we analyze the recessionary effects of the COVID-19 pandemic on digital platform workers. The crisis has been described as a great work-from-home experiment, with platform ecosystems positing as its most advanced form. Our analysis differentiates the direct (health) and indirect (economic) risks incurred by workers, to critically assess the portrayal of platforms as buffers against crisis-induced layoffs. We submit that platform-mediated labor may eventually increase precarity, without necessarily reducing health risks for workers. Our argument is based on a comparison of the three main categories of platform work—“on-demand labor” (gigs such as delivery and transportation), “online labor” (tasks performed remotely, such as data annotation), and “social networking labor” (content generation and moderation). We discuss the strategies that platforms deploy to transfer risk from clients onto workers, thus deepening existing power imbalances between them. These results question the problematic equivalence between work-from-home and platform labor. Instead of attaining the advantages of the former in terms of direct and indirect risk mitigation, an increasing number of platformized jobs drift toward high economic and insuppressible health risks.

Nanoengineered on-demand drug delivery system improves efficacy of pharmacotherapy for epilepsy

Electroresponsive and synergistic brain-targeting nanoparticles improve efficacy of pharmacotherapy for epilepsy.

Design and Simulation of a Digital Twin Mobility Concept: An Electric Aviation System Dynamics Case Study with Capacity Constraints

Vertical mobility, as a commercial service, has been strongly focused on the scheduled volume and long-distance mobility services. Thus, limiting its potential coverage, flexibility, and adaptability with high investments and centralized mobility hubs, called airports. In this context, a customized and on-demand air mobility concept providing high flexibility in location combinations and time schedules appears as an unexplored challenge for regional mobility needs. As a result, the aim of this research is to provide a generic framework for various mobility means as well as to design a holistic air mobility management concept for electric vertical mobility for profitable and sustainable operations by providing a service to society. A system dynamics simulation case study applies the conceptual model for an on-demand air mobility network of electric aircrafts in a regional area considering capacity constraints in vertiports, aircrafts, charging, and parking stations. Thus, bottlenecks and delays can be quantified by using a digital twin tool for customized scenarios. Simulation results show how an optimal maintenance management and redistribution of aircraft units improve service indicators in passenger quantity and customer order lead time as well as reduce aircraft on ground time. As a result, a digital twin air mobility network model with simulation capabilities is a key factor for successful operations.