The Healthcare system is an organization that consists of important requirements corresponding to security and privacy, for example, protecting patients' medical information from unauthorized access, communication with transport like ambulance and smart e-health monitoring. Due to lack of expert design of security protocols, the healthcare system is facing many security threats such as authenticity, data sharing, the conveying of medical data. In such situation, block chain protocol is used. In this manuscript, Efficient Block chain Network for securing Healthcare data using Multi-Objective Squirrel Search Optimization Algorithm (MOSSA) is proposed to generate smart and secure Healthcare system. In this the block chain is a decentralized and the distributed ledger device that consists of various blocks linked with digital signature schemes, consensus mechanisms and chain of hashing, offers highly reliable storage capabilities. Further the block chain parameters, such as block size, transaction size and number of block chain channels are optimized with the help of MOSSA. With the evolution of the MOSSA provide new features for enhancing security and scalability. The simulation process is executed in the JAVA platform. The experimental result of the proposed method shows higher throughput of 26.87%, higher efficiency of 34.67%, lowest delay of 22.97%, lesser computational overhead of 37.03%, higher storage cost of 34.29% when compared to the existing method such as Block chain-ECIES-HSO, Block chain-hybrid GO-FFO, Block chain-SDN-HSO algorithm for healthcare technologies.
This research consists of three phase. The first model includes a crystal payload encryption method watermarking scheme and an attack-free encryption scheme called international data encryption algorithm (IDEA). The second model is a binary grey scale image in chicken swarm optimization (CSO) applied to copyright production parameter optimized swarm intelligence domain-based approach, which is compared to conventional approaches. The work performance has been evaluated for conventional machine learning approach using MATLAB. The simulation results show that proposed hybridized crystal payload algorithm with chicken swarm optimization (HCPECSO) scheme achieves a high copyright production with the lowest mean square error values and highest peak signal noise ration when compared with the existing approaches schemes like machine learning SVM, logistic regression, and neural network. The proposed HCPECSO attained less processing time of 32.33s and processing cost compared to existing schemes.
In this chapter, the authors explore a cost model and the come about cost-minimization client booking issue in multi-level mist figuring organizations. For an average multi-level haze figuring network comprising of one haze control hub (FCN), different fog access nodes (FANs), and user equipment (UE), how to model the cost paid to FANs for propelling assets sharing and how to adequately plan UEs to limit the cost for FCN are still issues to be settled. To unravel these issues, multi-level cost model, including the administration delay and a straight backwards request dynamic installment conspire, is proposed, and a cost-minimization client planning issue is defined. Further, the client planning issue is reformulated as an expected game and demonstrated to have a Nash equilibrium (NE) arrangement.
In the modern world, the digital signal processing embeds more in real time applications. Several researchers focused on filtering process to identify the limitation in traditional methods. In this article, the meta-heuristic algorithm is deployed for optimizing infinite impulse response (IIR) filter design. The traditional IIR filter results create computational complexity and its performance is worse in the case of a noisy environment. In signal processing, IIR plays several roles in filtering and monitoring the signal amplitude. The African Buffalo Optimization (ABO) is quite easy for implementation and its performance outcomes solved many problems in various domains. Hence, it is selected for solving IIR filter problems for obtaining optimal filter coefficients. Initially, IIR filter is designed for different orders under ABO concept. The ABO based IIR filter’s performance is superior to those obtained by Genetic Algorithm and cuckoo search algorithm. The proposed method’s performance result proves that it has a smaller magnitude error and phase error with fast convergence rate.
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