Gaussian relevance vector MapReduce based annealed Glowworm optimization for big medical data scheduling
Lade...
Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2021
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Journal of the Operational Research Society
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
73
Ausgabe / Nummer
10
Seiten / Dauer
2204-2215
Patentnummer
Verlag / Herausgebende Institution
Taylor & Francis
Verlagsort / Veranstaltungsort
London
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Various big-data analytics tools and techniques have been developed for handling massive amounts of data in the healthcare sector. However, scheduling is a significant problem to be solved in smart healthcare applications to provide better quality healthcare services and improve the efficiency of related processes when considering large medical files. For this purpose, a new hybrid model called Gaussian Relevance Vector MapReduce-based Annealed Glowworm Optimization Scheduling (GRVM-AGS) was designed to improve the balancing of large medical data files between different physicians with higher scheduling efficiency and minimal time. First, a GRVM model was developed for the predictive analysis of input medical data. This model reduces the storage complexity of large medical data analysis by means of eliminating unwanted patient information and predicts the disease class with help of a Gaussian kernel function. Afterwards, GRVM performs AGS to schedule the efficient workloads among multiple datacenters based on the luciferin value in the smart healthcare environment with reduced scheduling time. Through computational experiments, we demonstrate that GRVM-AGS increases the scheduling efficiency and reduces the scheduling time of large medical data analysis compared to state-of-the-art approaches.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1476-9360
0160-5682
0160-5682
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Hybrid
Lizenz
Zitation
PATAN, Rizwan, Suresh KALLAM, Amir H. GANDOMI, Thomas HANNE und Manikandan RAMACHANDRAN, 2021. Gaussian relevance vector MapReduce based annealed Glowworm optimization for big medical data scheduling. Journal of the Operational Research Society. 2021. Bd. 73, Nr. 10, S. 2204–2215. DOI 10.1080/01605682.2021.1960908. Verfügbar unter: https://doi.org/10.26041/fhnw-7108