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 [Formula: see text] autonomous tasks to [Formula: see text] 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.
In this paper we describe a method to classify the micro array gene expression data based on their tissue sample types. Normally dimensionality reduction is performed over the micro array gene expression data set. Here, we propose a statistical approach for extracting significant genes from the gene expression data set. But, the statistical approach does not correctly identify the important genes. Hence, the ultimate objective is to solve the drawbacks in dimensionality reduction as they have a direct impact on the robustness of the generated fuzzy rules. Consequently, the goal is to generate fuzzy rules based on dimensionality reduced data. Hence, fuzzy inference is selected in our approach for classification and the fuzzy rules are utilized to train the fuzzy inference system (FIS). The classification performance of the fuzzy inference system (FIS) is similar to that of other classifiers, but simpler and easier to interpret. The classification performance of the FIS classifier is compared over the existing Fuzzy Genetic, Fuzzy Neural Network ProbPCA and PCA classifiers. The classification performance of the proposed technique is evaluated over the cancer datasets of Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL).
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