Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386401852> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W4386401852 endingPage "110804" @default.
- W4386401852 startingPage "110804" @default.
- W4386401852 abstract "Alzheimer’s is a dangerous disease prevalent in human societies, and unfortunately, its incidence is increasing daily. The number of patients is on the rise, while the availability of physical doctors has become limited and their schedules are packed. Consequently, the adoption of digital healthcare systems for Alzheimer’s disease (AD) has become more common, aiming to alleviate the burden on both AD patients and doctors. AD digital healthcare is a highly complex domain that incorporates various technologies, including fog computing, cloud computing, and deep learning algorithms. However, the implementation of these fog, cloud, and deep learning technologies has encountered challenges related to high computational time during AD detection processes. To address these challenges, this paper focuses on the convex optimization problem, which aims to optimize computation time and accuracy constraints in digital healthcare applications for AD. Convex optimization necessitates the use of an evolutionary algorithm that can enhance different AD constraints in distinct phases. The paper introduces a novel scheme called Evolutionary Deep Convolutional Neural Network Scheme (EDCNNS), designed to minimize computation time and achieve the highest prediction accuracy criteria for AD. EDCNNS comprises several phases, including feature extraction, selection, execution, and scheduling on the fog cloud nodes. The simulation results demonstrate that EDCNNS optimized security by 38%, reduced the deadline failure ratio by 29%, and improved selection accuracy by 50% across different Alzheimer’s classes compared to existing studies." @default.
- W4386401852 created "2023-09-04" @default.
- W4386401852 creator A5022185985 @default.
- W4386401852 creator A5037685353 @default.
- W4386401852 creator A5080711681 @default.
- W4386401852 creator A5082046789 @default.
- W4386401852 date "2023-11-01" @default.
- W4386401852 modified "2023-09-27" @default.
- W4386401852 title "EDCNNS: Federated learning enabled evolutionary deep convolutional neural network for Alzheimer disease detection" @default.
- W4386401852 cites W2917347002 @default.
- W4386401852 cites W3174447824 @default.
- W4386401852 cites W3191178079 @default.
- W4386401852 cites W3194565976 @default.
- W4386401852 cites W4210500502 @default.
- W4386401852 cites W4223611754 @default.
- W4386401852 cites W4224281602 @default.
- W4386401852 cites W4224943656 @default.
- W4386401852 cites W4226493583 @default.
- W4386401852 cites W4280556554 @default.
- W4386401852 cites W4290603418 @default.
- W4386401852 cites W4292196086 @default.
- W4386401852 cites W4292662067 @default.
- W4386401852 cites W4294989982 @default.
- W4386401852 cites W4297229688 @default.
- W4386401852 doi "https://doi.org/10.1016/j.asoc.2023.110804" @default.
- W4386401852 hasPublicationYear "2023" @default.
- W4386401852 type Work @default.
- W4386401852 citedByCount "0" @default.
- W4386401852 crossrefType "journal-article" @default.
- W4386401852 hasAuthorship W4386401852A5022185985 @default.
- W4386401852 hasAuthorship W4386401852A5037685353 @default.
- W4386401852 hasAuthorship W4386401852A5080711681 @default.
- W4386401852 hasAuthorship W4386401852A5082046789 @default.
- W4386401852 hasConcept C105902424 @default.
- W4386401852 hasConcept C108583219 @default.
- W4386401852 hasConcept C111919701 @default.
- W4386401852 hasConcept C11413529 @default.
- W4386401852 hasConcept C119857082 @default.
- W4386401852 hasConcept C120314980 @default.
- W4386401852 hasConcept C124101348 @default.
- W4386401852 hasConcept C126255220 @default.
- W4386401852 hasConcept C137836250 @default.
- W4386401852 hasConcept C148483581 @default.
- W4386401852 hasConcept C154945302 @default.
- W4386401852 hasConcept C159149176 @default.
- W4386401852 hasConcept C206729178 @default.
- W4386401852 hasConcept C33923547 @default.
- W4386401852 hasConcept C41008148 @default.
- W4386401852 hasConcept C75684735 @default.
- W4386401852 hasConcept C79974875 @default.
- W4386401852 hasConcept C81363708 @default.
- W4386401852 hasConceptScore W4386401852C105902424 @default.
- W4386401852 hasConceptScore W4386401852C108583219 @default.
- W4386401852 hasConceptScore W4386401852C111919701 @default.
- W4386401852 hasConceptScore W4386401852C11413529 @default.
- W4386401852 hasConceptScore W4386401852C119857082 @default.
- W4386401852 hasConceptScore W4386401852C120314980 @default.
- W4386401852 hasConceptScore W4386401852C124101348 @default.
- W4386401852 hasConceptScore W4386401852C126255220 @default.
- W4386401852 hasConceptScore W4386401852C137836250 @default.
- W4386401852 hasConceptScore W4386401852C148483581 @default.
- W4386401852 hasConceptScore W4386401852C154945302 @default.
- W4386401852 hasConceptScore W4386401852C159149176 @default.
- W4386401852 hasConceptScore W4386401852C206729178 @default.
- W4386401852 hasConceptScore W4386401852C33923547 @default.
- W4386401852 hasConceptScore W4386401852C41008148 @default.
- W4386401852 hasConceptScore W4386401852C75684735 @default.
- W4386401852 hasConceptScore W4386401852C79974875 @default.
- W4386401852 hasConceptScore W4386401852C81363708 @default.
- W4386401852 hasFunder F4320321145 @default.
- W4386401852 hasLocation W43864018521 @default.
- W4386401852 hasOpenAccess W4386401852 @default.
- W4386401852 hasPrimaryLocation W43864018521 @default.
- W4386401852 hasRelatedWork W2731899572 @default.
- W4386401852 hasRelatedWork W2999805992 @default.
- W4386401852 hasRelatedWork W3014300295 @default.
- W4386401852 hasRelatedWork W3116150086 @default.
- W4386401852 hasRelatedWork W3133861977 @default.
- W4386401852 hasRelatedWork W4200173597 @default.
- W4386401852 hasRelatedWork W4291897433 @default.
- W4386401852 hasRelatedWork W4312417841 @default.
- W4386401852 hasRelatedWork W4321369474 @default.
- W4386401852 hasRelatedWork W4380075502 @default.
- W4386401852 hasVolume "147" @default.
- W4386401852 isParatext "false" @default.
- W4386401852 isRetracted "false" @default.
- W4386401852 workType "article" @default.