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Cyber–physical systems (CPSs) can be treated as an emerging technology that has the ability to handle the physical process and computational view of interlinked systems. At the same time, the high-performing processing capability provides assurance of CPS applications in real time. Besides, task scheduling is considered as the Nondeterministic Polynomial (NP)-hard problem and optimal allocation of tasks is important for the CPS environment. The primary concept of the optimum energy-based scheduling approach searches for the physical host allocation vector to the allotted virtual machine with an aim of reducing energy utilization. The multiple processor packet scheduling technique defined that every task in the system is already divided into processors by the task allocating scheme and every process can execute on the distinct or identical single processor scheduling technique. With this motivation, this paper presents a new quantum invasive weed optimization-based energy-aware scheduling (QIWO-EATS) technique for the CPS environment. The goal of the QIWO-EATS technique is to assign n autonomous tasks to m dissimilar resources, and thereby the whole task completion duration gets reduced and resources are completely used. The proposed model has been simulated using the MATLAB tool. The experimental results highlighted the better outcomes of the QIWO-EATS technique over the recent approaches in terms of several evaluation metrics. [ABSTRACT FROM AUTHOR]

Rapid industrialization has led to the generation of a considerable amount of waste, both solid and liquid, in industrial fields like food processing, sugar, pulp, sago or starch, dairies, paper, fruit processing, poultry, distilleries, slaughterhouses, tanneries, and so forth. Despite the requirement for pollution control measures, the waste is discharged into water bodies or generally dumped on land without appropriate management, and thus becomes a significant source of environmental pollution and health hazards. The most essential step of waste management is the segregation of waste into the various elements, and normally this process is done automatically by hand-picking. A smart waste material classification technique is required to simplify the procedures. Therefore, the study presents a new Metaheuristics with Deep Transfer Learning Enabled Detection and Classification Methods for Industrial Waste Management (MDTLDC-IWM) method. The presented MDTLDC-IWM model facilitates the use of DL models for the identification and classification of waste materials in the IWM system. To accomplish this, the presented MDTLDC-IWM model follows two key phases, namely waste object recognition and waste object classification. At the initial stage, the YOLO-v5 object detector with the Harris Hawks Optimization (HHO) algorithm is used. Next, in the second stage, the stacked sparse auto encoder (SSAE) model is applied for the waste object classification method. The SSAE model is effectively optimized using the Aquila Optimization Algorithm (AOA), which helps to accomplish maximum classification of waste objects. The MDTLDC-IWM model has achieved a precision of 96.84 percent and an F score of 96.71 percent. A benchmark dataset is used to test the experimental validity of the presented MDTLDC-IWM model. Extensive comparative analysis reported the enhanced performance of the MDTLDC-IWM model over recent state-of-the-art approaches. [Display omitted] • This study introduces a novel Metaheuristics with Deep Transfer Learning Enabled Detection and Classification Methods for Industrial Waste Management (MDTLDC-IWM) approach. • The described MDTLDC-IWM model supports the employment of DL models in the IWM system for the identification and classification of waste products. Since the YOLO-v5 object detector with the Harris Hawks Optimization (HHO) method is employed. • The stacked sparse auto encoder (SSAE) model is utilised for the classification of waste objects. [ABSTRACT FROM AUTHOR]