Performance Analysis of Linear Congruential Random Generator Algorithms Using Python and Java Languages

Giving Consideration to the era of Generic AI and Internet of things where high band width, connectivity, servers, storage and decisions play a important role. Hence speed and security is a obvious need. As pseudo-random number generation (PRNG)is also a basic need when security, probability, heuristics and many other issues are of concern. For this purpose and by considering the recent research outcomes with respect to programming languages like java and Python. We selected Linear congruential Generator (LCG) algorithm which is one of the popular PRNG. In this study, we analyze the performance of Linear Congruential Generator (LCG) pseudo-random number generators (PRNGs) implemented in Python and Java using three seeding techniques: manual, system time, and hash/object based. Our results show that system-time seeding offers the best trade-off between speed and randomness, with Java outperforming Python in execution times. The results noticed have proved the strengths and weaknesses of Java and Python. These findings provide practical guidance for developers in selecting appropriate PRNG implementations for applications in IoT, AI, and statistical modeling.