Gautam Srivastava

TL;DR: This paper proposes a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease with the hybrid random forest with a linear model (HRFLM).

TL;DR: This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible.

TL;DR: This work proposes a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network that make IoT application data and transactions more secure and anonymous over a blockchain-based network.

TL;DR: Two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms using publicly available Cardiotocography dataset from University of California and Irvine Machine Learning Repository to prove that PCA outperforms LDA in all the measures.

TL;DR: Thorough experimental analysis shows that the adaptive genetic algorithm with fuzzy logic (AGAFL) model has outperformed current existing methods in diagnosing heart disease at early stages.