Hierarchical auto-associative polynomial CNN for cloud scheduling with privacy optimization using white shark

In this research a novel Privacy Oriented White Shark Encompassed hierarchical auto-associative polynomial Convolutional Neural NetwoRk (POWER) framework for task scheduling has been proposed. Initially, the Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN) for scheduling the healthcare task by considering the parameters. The HAP-CNN has been optimized using White Shark Optimization (WSO) for enhancing the accuracy in generating the schedule. The proposed task scheduling model is calculated based on several characteristics, including task migration, reaction time, transmission time, makespan, and cost. Recall, specificity, accuracy, precision, and F1 score were utilized to assess the proposed method’s efficacy. With the suggested model, 99.32% classification accuracy was attained. The proposed model enhanced the total accuracy by 2.29%, 1.07% and 7.37% better than Task Scheduling utilizing a multi-objective grey wolf optimizer (TSMGWO), Prioritized Sorted Task-Based Allocation (PSTBA), and Large-Scale Industrial Internet of Things asynchronous Advantage Actor Critic system (LsiA3CS) respectively.