Studies on triple junction electric field in ferroelectric cathodes

To investigate the effect of grid thickness and dielectric constant on the ferroelectric cathode triple junction electric field distribution in a two-dimensional structure, simulation has been carried out using finite element method (FEM) code ANSYS. Triple junction electric field plays a major role in the emission of electrons from a ferroelectric cathode and it approaches towards its limiting value even if the dielectric constant of the ferroelectric material is increased considerably. It is important to increase the triple junction electric field without increasing the applied field to reduce the mechanical stresses in the ferroelectric material. A dielectric layer (εr<100) has been introduced between the ferroelectric material and the grid to increase the triple junction electric field. FEM simulation results showed that the triple junction electric field is more than 48 times the applied field in this case. This structure not only enhances the triple junction electric field but it also changes the E|| and E┴ ratio (β) favorably. Effects of dielectric constant and the thickness of the dielectric layer on triple junction electric field have been studied

GBJOF: Gradient Boosting Integrated with Jaya Algorithm to Optimize the Features in Malware Analysis

Malware analysis is used to identify suspicious file transferring in the network. It can be identified efficiently by using the reverse engineering hybrid approach. Implementing a hybrid approach depends on the feature selection because the dataset contains static and dynamic parameters. The given dataset contains 85 attributes with 10 different class labels. Since it has high dimensional and multi-classification data, existing approaches of ML could be more efficient in reducing the features. The model combines the enhanced JAYA genetic algorithm with a gradient boosting technique to identify the efficiency and a smaller number of features. Many existing approaches for feature selection either implement correlation analysis or wrapper techniques. The major disadvantages of these issues are that they are facing fitting problems with a very small number of features. With the Usage of the genetic approach, this paper has achieved 95% accuracy with 12 features, approximately 7% greater than ML approaches