Daehan Kwak

TL;DR: An intelligent collaborative security model to minimize security risk is proposed; how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context is discussed; and various IoT and eHealth policies and regulations are addressed to determine how they can facilitate economies and societies in terms of sustainable development.

TL;DR: A smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches and obtains accuracy of 98.5%, which is higher than existing systems.

TL;DR: A type-2 fuzzy ontology–aided recommendation systems for IoT-based healthcare to efficiently monitor the patient's body while recommending diets with specific foods and drugs and the experimental results show that the proposed system is efficient for patient risk factors extraction and diabetes prescriptions.

TL;DR: This work proposes an ontology and latent Dirichlet allocation (OLDA)-based topic modeling and word embedding approach for sentiment classification, which achieves accuracy of 93%, which shows that the proposed approach is effective for sentiment Classification.

TL;DR: In this article, the authors proposed a fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers.