01784nas a2200157   4500008004100000245004500041210004500086520130700131100002101438700001501459700001501474700001901489700001801508700002301526856007701549       2022                        eng d00aMaking Smart Contracts Predict and Scale0 aMaking Smart Contracts Predict and Scale3 a<p>The machine learning algorithms can predict the events based on the trained models and datasets. However, a reliable prediction requires the model to be trusted and tamper-resistant. Blockchain technology provides trusted output with consensus-based transactions and an immutable distributed ledger. The machine learning algorithms can be trained on blockchain smart contracts to produce trusted models for reliable prediction. But most smart contracts in the blockchain do not support floating-point data type, limiting computations for classification, which can affect the prediction accuracy. In this work, we propose a novel method to produce floating-point equivalent probability estimation to classify labels on-chain with a Naive Bayes algorithm. We derive a mathematical model with Taylor series expansion to compute the ratio of the posterior probability of classes to classify labels using integers. Moreover, we implemented our solution in Ethereum blockchain smart-contract with the Solidity programming language, where we achieved a prediction accuracy comparable to the scikit-learn library in Python. Our derived method is platform-agnostic and can be supported in any blockchain network. Furthermore, machine learning and deep-learning algorithms can borrow the derived method.</p>
1 aBadruddoja, Syed1 aDantu, Ram1 aHe, Yanyan1 aThompson, Mark1 aSalau, Abiola1 aUpadhyay, Kritagya  uhttps://nsl.cse.unt.edu/content/making-smart-contracts-predict-and-scale