Calls for papers

 

International Journal of Grid and Utility Computing

 

Special Issue on: "AI-Enabled Data Analysis in Emerging of Internet of Things Based Applications"

Guest Editors:
Dr. Manoj Diwakar, Graphic Era Deemed to be University, India
Dr. Prabhishek Singh, Bennett University, India
Dr. Maanak Gupta, Tennessee Technological University, USA
Dr. Vinayakumar Ravi, Prince Mohammad Bin Fahd University, Saudi Arabia

The emergence of Internet of Things (IoT)-based applications has made it more difficult to perform extensive data analysis on devices with low resources. Because IoT devices have limited resources, there is an increasing requirement for novel learning and data mining approaches to help make sense of the massive amounts of data being collected. Thanks to recent advances in AI technology, applications may now rely on a billion-strong sensor network that understands its operational environment and can adapt to it based on what it hears, sees, and learns. This enables apps to react correctly to changes in their surroundings. This enables applications to provide unique features and capabilities across a wide range of use cases, all while improving safety, reducing complexity, and increasing dependability. Both industry and academia recognize the challenges presented by data analysis in limited networks such as the Internet of Things (IoT). This challenge is created by the prevalence of small, low-powered devices that constitute a substantial portion of the network. Intelligent data computing has a great deal which is not yet able to satisfactorily meet the requirements in the vast majority of emerging applications for the Internet of Things (IoT). To make technological progress, new ideas and methods need to be conceived and created. This includes enhancing the performance of computer models and coming up with more creative applications for AI and machine learning.

The overarching objective of this special issue is to encourage academics to address challenges associated with advancing technology in deep learning-driven approaches to data analytics for Internet of Things-based applications. A central concern in this special issue is whether or not we can build a connection between traditional techniques based on flexible and interpretable models with the emerging trend in artificial intelligence, augmented intelligence, deep learning, and machine learning etc. We encourage researchers to submit original and distinctive publications, as well as to evaluate articles, in order to achieve this goal.

Subject Coverage
Suitable topics include, but are not limited, to the following:

• AI-driven data analytics for IoT based applications
• Real implementation of data analytics for IoT applications
• Computational intelligence in IoT data collection
• Artificial intelligence for IoT-based data analytics
• Data analytics in IoT-based healthcare
• Edge and fog computing enabled data analytics
• Intelligent data analytics in healthcare
• Edge Computing on IoT: architectures, techniques, and challenges
• Data analysis of cognitive, behavioural, and emotional features for IoT based applications
• Disease analysis and prediction in IoT applications
• Big data analytics in IoT applications
• Intelligent data sensing and processing in IoT applications
• Advance computational intelligence for medical imaging in IoT applications

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).

All papers are refereed through a peer review process.

All papers must be submitted online. To submit a paper, please read our Submitting articles page.

Important Dates

Manuscripts due by: 30 April, 2023

Notification to authors: 30 July, 2023

Final versions due by: 30 September, 2023