Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling
نویسندگان
چکیده
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 smart applications provide better quality services improve efficiency related processes when considering large medical files. For this purpose, new hybrid model called Gaussian Relevance Vector MapReduce-based Annealed Glowworm Optimization Scheduling (GRVM-AGS) was designed balancing files between different physicians with higher minimal time. First, GRVM predictive analysis input data. This reduces storage complexity by means eliminating unwanted patient information predicts disease class help kernel function. Afterwards, performs AGS schedule efficient workloads among multiple datacenters based on luciferin value environment reduced Through computational experiments, we demonstrate that GRVM-AGS increases time compared state-of-the-art approaches.
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ژورنال
عنوان ژورنال: Journal of the Operational Research Society
سال: 2021
ISSN: ['0160-5682', '1476-9360']
DOI: https://doi.org/10.1080/01605682.2021.1960908