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6GNAI 2025 : IEEE WoWMoM 2025 Workshop on 6G Revolution: Unleashing the Power of Novel AI Techniques | |||||||||||||||
Link: https://sites.google.com/view/wowmom-6g-workshop/home | |||||||||||||||
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Call For Papers | |||||||||||||||
Dear Colleagues,
*** Please accept our apologies if you receive multiple copies of this CFP *** **************************************************************************************** Workshop on 6G Revolution: Unleashing the Power of Novel AI Techniques (May 27, 2025) in conjunction with IEEE WoWMoM 2025 (May 27-30, 2025, Fort Worth, Texas, USA) Workshop Link: https://sites.google.com/view/wowmom-6g-workshop/home Conference Link: https://wowmomconference2025.uta.edu/ **************************************************************************************** Important Dates: • Submission Deadline: March 1, 2025 • Notification of Acceptance: April 1, 2025 • Camera Ready: April 30, 2025 (Firm Deadline) • Workshop Date: May 27, 2025 Submission Link: https://edas.info/N33090 The need for 6G technology in the realm of novel AI techniques cannot be overstated. As we continue to witness remarkable advancements in artificial intelligence, it has become increasingly evident that existing network infrastructures are ill-equipped to handle the vast amounts of data and complex computations required by cutting-edge AI algorithms. 6G, with its unparalleled speed, ultra-low latency, and massive capacity, holds the key to unlocking the full potential of AI. By providing faster and more reliable connections, 6G will enable real-time analysis of enormous datasets, facilitating the training and deployment of more sophisticated AI models. Moreover, the seamless integration of 6G networks with edge computing will empower AI applications to operate at the network's edge, reducing latency and enabling autonomous decision-making in critical scenarios. In essence, 6G's transformative capabilities will pave the way for novel AI techniques that revolutionize industries, enhance human-machine collaboration, and drive innovation to unprecedented heights. The proposed workshop aims to explore the intersection of 6G technology and novel AI techniques, showcasing the transformative potential of this fusion in shaping the future of artificial intelligence. With a focus on specific areas of interest, including but not limited to real-time analytics, edge computing, autonomous systems, and massive data processing, this workshop will provide a platform for researchers, practitioners, and industry experts to exchange ideas, share insights, and discuss the latest advancements in 6G-enabled AI applications. Through engaging presentations, panel discussions, and interactive sessions, we will explore the opportunities, challenges, and implications of the 6G revolution in unlocking the true power of AI. To conclude, the 6G-Revolution workshop aims to provide a forum for researchers and professionals across academia, government, and industries, to exchange ideas, present new results, and provide future visions concerning 6G networks. If you have any questions regarding the workshop, please contact the chairs. • Real-time analytics and 6G: Applications and Innovations. • Edge computing and autonomous systems: Advancements and Challenges. • Massive data processing in 6G environments. • AI-powered network optimization in 6G networks. • Privacy and security considerations for AI in 6G. • Federated learning and distributed AI in 6G ecosystems. • AI-enabled IoT and sensor networks in 6G. • Explainable AI and transparency in 6G-enabled AI systems. • Machine learning algorithms and models for 6G networks. • Reinforcement learning and AI-driven decision-making in 6G scenarios. • Standardization efforts and regulatory implications for AI in 6G. • AI for drones, RIS, V2V, V2I, and V2X scenarios in 6G. We look forward to Your contributions! Sincerely, Sherief Hashima, Computational Learning Theory Team, RIKEN-AIP, Japan. Mostafa Fouda, Electrical and Computer Engineering Dept, Idaho State University, ID, USA. Kohei Hatano, Informatics Dept, Kyushu University, Japan. Zubair Md Fadllulah, Computer Science Dept, Western University, ON, Canada. Hamada Rizk, Information Science and Technology, Osaka University, Japan. Eiji Takimoto, Informatics Dept, Kyushu University, Japan. |
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