Combining information from thresholding techniques through an evolutionary Bayesian network algorithm

Lightweight Cryptographic Simulation of Power IoT Fused with Bayesian Network Algorithms

In the power system, the transmission and processing of information is a very important link, and the core part of it is electronic data, and the transmission and processing of electronic data is the most important link in the power system. Because information is continuously passed between network nodes, the security requirements for information are high. With the development of Internet technology, its application field has been widely expanded to various industries. Therefore, to better ensure power quality and improve network operation efficiency, it is necessary to rationally and effectively manage the entire communication system. Power Internet of Things technology combines information transmission and processing links and realizes data sharing between various communication nodes in the entire network system through intelligent management, thereby improving overall information security. This paper first introduces the research of Bayesian network algorithm, then studies the process of lightweight encryption implementation of power Internet of Things, and then simulates and compares various encryption algorithms to obtain the best encryption scheme, and finally verifies through simulation that the algorithm can effectively ensure the safe transmission of information and improve the efficiency of network operation.

An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.