Call for Papers - Resiliency Techniques in AI-enabled Cloud Computing Infrastructures for Connected Community
Without artificial intelligence and cloud computing, the most cutting-edge technology breakthroughs cannot be fully implemented in the connected community. Numerous new environmental goals may be reached and realised via the combination of the technologies. Recent coupling technologies include digital assistants, which combine AI and cloud computing to create an interconnected community environment for connected computing infrastructure. Furthermore, the AI-enabled cloud infrastructure has prepared the way for the creation of a variety of commercial activities that can be made more productive and predictive using AI's storage combined features like fuzzy logic and deep learning. Additionally, a network with a vast amount of data might be developed for an artificial intelligence system that requires continuous learning and analysis of data acquired from the environment.
Further, the benefits include intelligent automation, cost efficiency, improved data management and security. Apart from the aforementioned benefits, the system confronts a number of hurdles, including data privacy concerns for AI data kept in the cloud, as well as connection issues inherent in the cloud environment. Deep learning, neural networks, genetic algorithms, and machine learning algorithms are just a few of the AI methods that may be used to tackle cloud computing difficulties of the connected community.
In the long term, the adoption of cloud-based resilience solutions to address difficulties that arise as a result of the interaction between artificial intelligence and the cloud may show to be a viable solution in a connected community. The strategies that are utilised in resilience improvement have a variety of implications, including the avoidance of revenue loss for a business that has incorporated cloud computing. The prevention is achieved using various resiliency techniques such as failure forecasting, protection of data with pre-determined redundant computational strategies, replication, checkpoint implementation, and restoration that is made with reactive effort schemes in decreasing the impacts of failure. With the application of resiliency in an AI-based cloud environment, the use of algorithms based on AI could be implemented in the cloud for better data protection and security in connected community.
This special issue intends to disseminate research results on the application of artificial intelligence in the cloud, as well as resilience approaches that may be able to mitigate the difficulties associated with the aforementioned coupling. As a result, high-quality storage of AI data may be produced without the risk of data loss or security breaches emerging in the system.
We solicit submissions from the following thematic areas:
- AI implementation in the cloud for data clustering, prediction and classification secured by resilience methods
- Advanced resiliency models for the AI-Cloud connected community models to manage Covid like situations
- An AI cloud-connected community implementation using fuzzy and computation logic
- Futuristic implementation of the collaborative methods in AI-edge implementations using resilience techniques
- Enhanced resilience ubiquitous system for Cloud-AI implementation
- Advanced intrusion resilient middleware implementation in AI-cloud environment
- Resilient survival method implementation for AI-cloud distribution systems
- A novel architecture for the implementation of AI-cloud using resilience techniques
- Ubiquitous framework for the design of resilience models in the AI cloud implementation
- Critical systems implementation for the resilience model implementation in cloud edge solutions.
Paper Submission Deadline: January 30, 2023
Author Notification: April 27, 2023
Revised Papers Submission: June 25, 2023
Final Acceptance: August 1, 2023
Muhammad Attique Khan (Managing Guest Editor), HITEC University, Taxila, Pakistan
Gaurav Dhiman (Co-Guest Editor), Chandigarh University, Gharuan, Mohali, India
Sathishkumar V. E. (Co-Guest Editor), Hanyang University, Seoul, Republic of Korea