An IoT-based Optimization scheme on task scheduling for minimizing energy in Cloud Computing

Kumar, D. N. S Ravi; Kumar, K. Praveen; Raju, K. Goutham; Gowsalya, S; Balraj, Lavina; Srivastava, A. K.

doi:10.1109/icaccs54159.2022.9785171

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach, known as mHBA is based on use of the dimensional learning hunting (DLH) method, an effective local search technique, which is incorporated into the HBA. In addition, HBA has proved its efficiency in quantum applications as reported in [29]. The authors proposed an IoT-based optimization scheme for task scheduling for minimizing energy in cloud computing using the HBA.…”

Section: Introductionmentioning

confidence: 99%

Quantum Chaotic Honey Badger Algorithm for Feature Selection

et al. 2022

View full text Add to dashboard Cite

Determining the most relevant features is a critical pre-processing step in various fields to enhance prediction. To address this issue, a set of feature selection (FS) techniques have been proposed; however, they still have certain limitations. For example, they may focus on nearby points, which lowers classification accuracy because the chosen features may include noisy features. To take advantage of the benefits of the quantum-based optimization technique and the 2D chaotic Hénon map, we provide a modified version of the honey badger algorithm (HBA) called QCHBA. The ability of such strategies to strike a balance between exploitation and exploration while identifying the workable subset of pertinent features is the basis for employing them to enhance HBA. The effectiveness of QCHBA was evaluated in a series of experiments conducted using eighteen datasets involving comparison with recognized FS techniques. The results indicate high efficiency of the QCHBA among the datasets using various performance criteria.

show abstract

Section: Introductionmentioning

confidence: 99%