Hajar Mousannif

TL;DR: A performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer datasets is conducted and Experimental results show that SVM gives the highest accuracy with lowest error rate.

TL;DR: An analysis of the security and privacy requirements for the most dominant IoT application domains, including Personal and home, Government and utilities, and Enterprise and industry, is conducted.

TL;DR: Techniques for enhancing DQ in IoT are presented with a special focus on data cleaning techniques which are reviewed and compared using an extended taxonomy to outline their characteristics and their fitness for use for IoT.

TL;DR: The method used in this paper combines Big Data, Remote Sensing and Data Mining algorithms to process data collected from satellite images over large areas and extract insights from them to predict the occurrence of wildfires and avoid such disasters.

TL;DR: A conceptual framework is outlined whereby DB is viewed in terms of different dimensions established within the Driver–Vehicle–Environment (DVE) system, and an interpretive framework incorporating multiple dimensions influencing the driver’s conduct is identified.