Special Issue "AI 2.0-Enabled Next Generation Intelligence of Things for Smart Enterprise Systems"

Submission Deadline: 31 May 2021 (closed)
Guest Editors
Dr. Chinmay Chakraborty, Birla Institute of Technology, India.
Dr. Celestine Iwendi, SMIEEE, Fellow Higher Education Academy, Board Member IEEE Sweden Section.
Dr. Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway.
Dr. Gabriella Casalino, University of Bari, Italy.

Summary

The last decade was about connectivity, and we describe that dynamic with the Internet of Things. This decade is really about adding intelligence to different devices, services, etc. We have been confronted with a new IoT-The intelligence of things. Meanwhile, thefuture of Intelligence of Things and AIencompasses advanced cognitive methods capable of doing what ordinary machine learning (ML) and deep learning (DL) systems cannot attain easily or attain at all in parallel and distributed systems architectures for Smart City. Artificial intelligence 2.0 (A1 2.0) ushers in the combination of sustainable Industrial Internet of things and Intelligence of Things (AI/IoT)under smart enterprise systems. It means that AI/IoT will in the near future logically and effortlessly interrelate with human experts and operators, providing them with articulate clarifications and answers, even at the edge of the network or in robotic devices and navigation. Moreover, we shall witness the exploration of IoT cooperation, navigation at highest form autonomous automation navigation, wireless sensor network and Cloud integration. In this thematicissue, we solicit the submission of high-quality original researchand survey articlesclosely related to the following topics, particularly interdisciplinary submissions that bring together next generation Intelligence of Things, Artificial intelligence, Smart information processing, IoTsecurity, privacy and trust researchers.


Keywords
Artificial Intelligence 2.0, Internet of Things, Smart enterprise systems, Learning Analyics, Security

Published Papers
  • An Improved Machine Learning Technique with Effective Heart Disease Prediction System
  • Abstract Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we achieved the component perception measured… More
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