Comparing Linear Congruential Generators: Python vs. Java | Why is PRNG important in AI and IoT ?
1Comparative Analysis of Linear Congruential Random Generator Algorithms Using Python and Java Languages
Layman Abstract : This study looks at how computers generate random numbers, which are crucial for security, artificial intelligence (AI), and the Internet of Things (IoT). There are two main ways to generate random numbers:
True Random Number Generators (TRNGs) – Use unpredictable natural processes.
Pseudo-Random Number Generators (PRNGs) – Use mathematical formulas to create random-like numbers.
Since PRNGs are widely used in security, probability, and AI, this study tested a popular method called the Linear Congruential Generators (LCG) in Python and Java. The research compared three different ways of starting (seeding) the generator:
Manual input
System time (using the computer’s clock)
Hash/object-based methods
The results showed that using system time as a seed provided the best balance of speed and randomness. Java performed faster than Python, generating numbers 10 times quicker. However, both languages were effective for PRNGs, and the choice of language had little impact on randomness.
These findings help developers choose the best random number generation method for applications in security, AI, and statistical modeling.
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What is a Linear Congruential Generator (LCG)?
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Why is PRNG important in AI and IoT?
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How does system-time seeding improve randomness?
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How do Python and Java implement LCG differently?
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