Thar Baker

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: The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices, and runs efficiently and is cost-effective in terms of data acquisition and manipulation.

TL;DR: This work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

TL;DR: A decentralized authentication and access control mechanism is proposed for lightweight IoT devices and is applicable to a large number of scenarios and demonstrates a superior performance when compared to a state-of-the-art blockchain-based authentication technique.

TL;DR: This paper develops a novel multi-cloud IoT service composition algorithm called E2C2 that aims at creating an energy-aware composition plan by searching for and integrating the least possible number of IoT services, in order to fulfil user requirements.