Multilayer Perceptive Network with Gaussian Distributed Efficient Cryptography for Load Balancing - M. Prabhu (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This paper proposes a novel deep multilayer perceptive network (DMPLN) based on Gaussian distributive optimized congruential cryptographic (GDOC) for load balancing and secure data outsourcing in federated cloud computing. DMPLN can effectively improve the security and efficiency of federated cloud computing. Specifically, we use GDOC to encrypt data and then DMPLN to classify the encrypted data. The advantage of GDOC is that it can improve data security and the efficiency of data classification. Furthermore, we use the improved particle swarm optimization (IPSO) algorithm to optimize DMPLN, which can further improve classification accuracy. The results of our thorough tests, which we conduct on two real-world datasets, demonstrate that our suggested strategy may significantly increase the security and effectiveness of federated cloud computing. A highly efficient and secure deep multilayer perceptive network based on Gaussian distributive optimized congruential cryptographic for load balancing and secure data outsourcing in federated cloud computing. A highly efficient and secure deep multilayer perceptive network is proposed based on Gaussian distributive optimized congruential cryptography for load balancing and secure data outsourcing in federated cloud computing. The Gaussian Distributive Optimized congruential cryptographic technique used for load balancing and secure data outsourcing in federated cloud computing. The suggested solution balances the load and safeguards the data in federated cloud computing. The suggested method employs the Gaussian distributive optimal congruential cryptography method to safeguard the data in federated cloud computing.