Fereidoon Shams Aliee

TL;DR: A holistic AI-driven IoT eHealth architecture based on the concept of Collaborative Machine Learning approach in which the intelligence is distributed across Device layer, Edge/Fog layer, and Cloud layer is proposed, which enables healthcare professionals to continuously monitor health-related data of subjects anywhere at any time and provide real-time actionable insights which ultimately improves the decision-making power.

TL;DR: DCTM-RPL provides a comprehensive hierarchical model for trusting of things in IoT, which has a multi-dimensional vision of trust and puts the combination of metrics and necessary activities to deal with attacks under the umbrella of trust level calculation.

TL;DR: A quantitative AHP-based method toward expert-based EA analysis for characterizing and using enterprise architecture quality attributes and introducing a data structure that contains required information about quality attribute achievement of different EA scenarios in enterprises.

TL;DR: This paper proposes a new approach for confidence modeling in the context of recommender systems that derives the users’ and items’ confidence values from both local and global perspectives and shows the effectiveness of this approach on real-world data sets.

TL;DR: This chapter seeks to look at the history of IoT, more clearly define it, and review its terms and concepts, as well as review vertical IoT markets and higher-level use cases that have successfully adopted IoT solutions.