Comparative Analysis of Random Forest Classification Over SVM Classifier to Detect Cyber Thefts in Credit Card to Reduce False Rate

Aim: To reduce the false rate of cyber thefts in credit card attacks based on binary selection Random Forest classifier and SVM classifier. Materials and Methods: Classification is performed by Random forest classifier (N=28) over SVM classifier (N=28) is for false rate detection. Results and Discussion: The values obtained in terms of accuracy is identified by random state in Random forest (94.4%) over SVM (91.4%) Conclusion: The reduction of false rate with sigma value 0.126 appears to be better in Random Forest classifier than SVM classifier.

Random Numbers Can Help Solve Difficult Problems in Many-body Physics

Theorists review a random state vector-based description of quantum many-body systems which helps greatly reduce the computational burden involved in their numerical simulations, opening doors to applications in quantum computing.

Parametric Analysis of the Sensitivity of a Prestressed Concrete Beam Using the DOE Simulation Technique

The paper presents the method of simplified parametric analysis of the sensitivity of a pre-tensioned concrete beam. The presented approach is based on the DOE (design of experiments) data collection which is simulation technique allowing for identification of variables deciding about the effectiveness and costs of designed structures. Additionally, application of the hyper-surface of the construction response allows designers to the development of multi-dimensional trade-off graphs to facilitate, the assessment of the scope of changes in random state variables permitted due to the adequate criteria and selection of their values close to optimum. Design basics, procedures and results of the presented considerations of sensitivity assessment and reliability of the structure has been shown on the example of a pre-stressed concrete beam designed in accordance with the requirements and procedures of Eurocode 2

Network generation and evolution based on spatial and opinion dynamics components

In this paper, a model for a spatial network evolution based on a Metropolis simulation using an energy function that depends both on the distance between the nodes and the stated preferences is studied. The agents influence their network neighbors using the CODA model, where each agent has a preference between two options based on its probabilistic assessment of which option is the best one. The algorithm generates realistic networks for opinion problems as well as temporal dynamics for those networks. The transition of a random state to an ordered situation, as temperature decreases, leads to different outcomes depending on the relative strength of the spatial and opinion components of the energy.