A Process based Algorithm for Random Number Generation

Abstract

Background: Random number generation plays a crucial role in various scientific, computational, cryptographic applications and stochastic processes. This paper introduces a novel algorithm designed to address the demand for high-quality random numbers with improved statistical properties. The proposed Algorithm uses the elements of hardware-based entropy to generate random numbers for efficiency and unpredictability.
Methods: Pseudo-random numbers are sequences derived from algorithms known as pseudo-random number generators (PRNGs). These generators use an initial value, or seed, to produce sequences that exhibit statistical properties akin to truly random numbers.
Results:For the z-test statistic and the corresponding p-value in the above five samples we found that p-value is not less than α = 0.05, therefore we fail to reject the null hypothesis.
Conclusions: Module U behaves as a Random number generation algorithm. Further, we can test the module U for large samples and we can use module U for simulation of different Random processes to verify it’s correctness.