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Enhanced moth flame optimization based Supervision Kernel Entropy Component Analysis for high‐speed encrypted transmission model
Author(s) -
M Babu,
Sathish Kumar G A
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4831
Subject(s) - computer science , encryption , naive bayes classifier , entropy (arrow of time) , kernel (algebra) , component (thermodynamics) , transmission (telecommunications) , data mining , artificial intelligence , machine learning , computer engineering , support vector machine , computer network , physics , mathematics , combinatorics , thermodynamics , telecommunications , quantum mechanics
Summary Recently, encryption technology plays a vital role in providing secure information. In the multimedia sector, several images and posts are broadcasted via the internet on a daily basis; in which the images are transmitted on social media and are subjected to several security attacks. As a result, it is essential to secure the images from illegal or forbidden accessing. This paper aims to develop a novel Supervision Kernel Entropy Component Analysis‐enhanced moth flame optimization (SKECA‐EMFO)‐based encrypted transmission system. Here, an effective encryption transmission system is designed using a Bayes minimum risk classifier to secure sensitive information during the transmission processes. Furthermore, the SM4 encryption algorithm performs high‐speed encrypted transmission speed and achieves intelligent recognition performance. The novel SKECA‐EMFO approach is employed in demonstrating facial expression recognition thereby obtaining an optimal feature set. Finally, seven benchmark functions are utilized to examine and calculate the efficiency of the newly established proposed approach. The comparative analysis is carried out with a few approaches, and the results reveal that the proposed approach provides superior performances than other existing techniques.