@conference {575,
	title = {Making Smart Contracts Predict and Scale},
	booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)},
	year = {2022},
	abstract = {<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>
},
	doi = {10.1109/BCCA55292.2022.9922480},
	author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya}
}
@conference {533,
	title = {Making Smart Contracts Smarter},
	booktitle = {2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Sydney, Australia},
	abstract = {<p>Blockchain technology develops static smart contracts for decentralized business transactions, lacks dynamic decision-making capabilities that limit the possibilities of ever-increasing demands of modern business applications. Artificial intelligence, a computational prediction platform provides intelligent predictions, actions, and recognition that lacks the ability to hold on to the integrity of the prediction result and requires the help of external authorities to secure the system. Blockchain-based AI prediction can cover the gaps of individual technologies and can mutually benefit from one another to develop a decentralized machine learning architecture that promises to yield better security, automation, and dynamism of the application. This paper proposes a Naive Bayes prediction algorithm to perform prediction with inside blockchain smart contracts that promises to open up more opportunities in the field of Blockchain-AI decentralized applications.</p>
},
	keywords = {artificial intelligence, blockchain, DApp, machine learning, Naive Bayes, smart contract},
	isbn = {978-1-6654-3578-9},
	doi = {10.1109/ICBC51069.2021.9461148},
	url = {https://ieeexplore.ieee.org/abstract/document/9461148},
	author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Upadhayay, Kritagya and Thompson, Mark}
}
