Random Number Generator Based on Hopfield Neural Network with Xorshift and Genetic Algorithms
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47765-2_21
Reference18 articles.
1. Aydin, Ö., Kösemen, C.: XorshiftUL+: a novel hybrid random number generator for Internet of Things and wireless sensor network applications. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 26(5), 953–958 (2020)
2. Bassham III, L.E., et al.: SP 800-22 Rev. 1a. A statistical test suite for random and pseudorandom number generators for cryptographic applications. National Institute of Standards & Technology (2010)
3. Bouteghrine, B., Tanougast, C., Sadoudi, S.: A survey on chaos-based cryptosystems: implementations and applications. In: Skiadas, C.H., Dimotikalis, Y. (eds.) 14th Chaotic Modeling and Simulation International Conference, pp. 65–80. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96964-6_6
4. Brown, R.G.: DieHarder: a GNU public licensed random number tester. Draft paper included as file manual/dieharder.tex in the dieharder sources. Last version dated 20 (2006)
5. Hameed, S.M., Ali, L.M.M.: Utilizing Hopfield neural network for pseudo-random number generator. In: 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1–5. IEEE (2018)
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