Machine Learning and Artificial Intelligence for Context-Aware Network Optimization
Machine learning and Artificial Intelligence (AI) have the potential to revolutionize context-aware network optimization. AI can be used to analyze large amounts of data and detect patterns to optimize networks. For example, AI can be used to analyze traffic patterns, identify areas of congestion, and suggest dynamic routing solutions. AI can also be used to predict network outages and anticipate future network needs. AI can also be used to monitor network configurations and automatically adjust settings to maximize performance. Machine learning and artificial intelligence can be used to develop and deploy algorithms that can detect and respond to changes in the network environment in order to provide a more optimized network. It is a powerful tool for optimizing networks by utilizing the knowledge a machine has of its environment and the ability of an AI to make decisions based on that knowledge. This technology allows for networks to be automatically tweaked to their optimal settings based on the context of their environment. This can result in reduced latency, faster speeds, and improved reliability.By leveraging AI and machine learning for context-aware network optimization, organizations can ensure their networks are always running at optimal performance.
Machine learning and artificial intelligence have become increasingly important in the field of context-aware network optimization. This special issue focuses on the utilization of these techniques in the area of network optimization and their potential applications to improve network performance. We invite researchers to submit their original research results, reviews, perspectives and commentaries on the topics related to machine learning and artificial intelligence for context-aware network optimization. Topics of interest include, but are not limited to:
• Machine learning for network optimization
• Artificial intelligence for network optimization
• Context-awareness in network optimization
• Machine learning-based algorithms for network optimization
• Artificial intelligence-based algorithms for network optimization
• Applications of machine learning and artificial intelligence for network optimization
• Network optimization in IoT networks
• Network optimization for edge computing
• Network optimization for cloud computing
• Performance evaluation and benchmarking of machine learning and artificial intelligence-based network optimization
Editorial board
Dr Arvind Dhaka
Associate Professor, Manipal University Jaipur, India
Email: arvind.neomatrix@gmail.com
Profile Link: https://scholar.google.co.in/citations?user=IOQDpsAAAAAJ&hl=en
Dr Todor Ganchev
Technical University of Varna, Bulgaria
Email:tganchev@tu-varna.bg
Profile Link: https://scholar.google.com/citations?user=vbbsKBkAAAAJ&hl=en
Dr Edmar Candeia Gurjao
Federal University of Campina Grande, Brazil,
Email: ecg@dee.ufcg.edu.br
Profile Link: https://scholar.google.com/citations?user=aeUgkCMAAAAJ&hl=en
Biography Available at: https://ecandeia.dee.ufcg.edu.br/
Dr Dijana Capeska Bogatinoska
UIST, Ohrid, North Macedonia
dijana.c.bogatinoska@uist.edu.mk
Profile Link: https://scholar.google.com/citations?hl=en&user=QdhFIZAAAAAJ&view_op=list_works&sortby=pubdate