In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To prove the hypothesis, I use these mathematical models to compute complex operations of naive Bayes, linear regression, decision trees, and neural network algorithms on Ethereum public test networks. The smart contracts achieve 95\% prediction accuracy compared to traditional programming language models, proving the soundness of the numerical derivations. Many non-real-time applications can use our solution for trusted and secure prediction services.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc2179338 |
| Date | 07 1900 |
| Creators | Badruddoja, Syed |
| Contributors | Dantu, Ram, He, Yanyan, Tunc, Cihan, Bhowmick, Sanjukta |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Badruddoja, Syed, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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