7th International Conference on Machine Learning for Networking (MLN'2024)
MLN 2024 is the seventh edition of the International Conference on Machine Learning for Networking. The goal of the conference is to provide a forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences and work-in-process on all aspects of Machine Learning and Networking. Each year, MLN attendees will appreciate and benefit from multidisciplinary exchanges on these hot topics. MLN 2024 will be held in Reims, France, from November 27th to November 29th, 2024.
Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal, to https://easychair.org/conferences/?conf=mln2024.
The accepted papers will be published as a post-proceedings in Springer's LNCS. Lecture Notes in Computer Science (LNCS) series is indexed by the ISI Conference Proceedings Citation Index - Science (CPCI-S), included in ISI Web of Science, EI Engineering Index (Compendex and Inspec databases), ACM Digital Library, dblp, Google Scholar, Scopus, etc.
Topics of interest include and are not limited to:
- Deep and Reinforcement learning
- Pattern classification for networks
- Machine learning for network slicing optimization
- Machine learning for 5G/6G and beyond
- Machine learning for user behavior prediction
- Innovative machine learning methods
- Optimization of machine learning methods
- Performance analysis of machine learning algorithms
- Experimental evaluations of machine learning
- Data mining in heterogeneous networks
- Machine learning for Internet of Things
- Machine learning for security and privacy
- Distributed and decentralized machine learning algorithms
- Intelligent cloud-support communications
- Intelligent resource allocation
- Intelligent energy-aware/green communications
- Intelligent software defined networks
- Intelligent cooperative networks
- Intelligent positioning and navigation systems
- Intelligent wireless communications
- Intelligent underwater sensor networks
- Large Language Models for Networking
Position papers are also welcome and should be clearly marked as such.