Restricted Boltzmann machine Assisted Secure Serverless Edge System for Internet of Medical Things

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10251455" target="_blank" >RIV/61989100:27240/23:10251455 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85131740372&origin=resultslist&sort=plf-f" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85131740372&origin=resultslist&sort=plf-f</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JBHI.2022.3178660" target="_blank" >10.1109/JBHI.2022.3178660</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Restricted Boltzmann machine Assisted Secure Serverless Edge System for Internet of Medical Things

Popis výsledku v původním jazyce

The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users&apos; obtaining real-time healthcare services. Many patients and doctors&apos; hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost. IEEE

Název v anglickém jazyce

Restricted Boltzmann machine Assisted Secure Serverless Edge System for Internet of Medical Things

Popis výsledku anglicky

The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users&apos; obtaining real-time healthcare services. Many patients and doctors&apos; hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost. IEEE

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Journal of Biomedical and Health Informatics

ISSN

2168-2194

e-ISSN

2168-2208

Svazek periodika

27

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

673-683

Kód UT WoS článku

000943693600013

EID výsledku v databázi Scopus

2-s2.0-85131740372