Chairs
Kasem Khalil, University of Mississippi, USA
Qinglin Yang, Sun Yat-sen University, China
The Embedded Machine Learning and Data Analytics track explores the innovative integration of machine learning algorithms and data analytics techniques within embedded systems. This track aims to highlight the transformative potential of deploying intelligent analytics directly on edge devices, enabling real-time decision-making and enhancing system performance. Participants will delve into state-of-the-art research, practical implementations, and emerging trends in the field, covering applications such as IoT, smart sensors, healthcare, and autonomous systems. Join us to uncover the future of embedded intelligence and its impact on various industries.
Topic of Interests:
- Machine learning and AI for big data
- Data Analytics
- Learning in knowledge-intensive systems
- Learning Methods and Analysis
- Learning Problems
- Smart city, Fog Computing, Cloud Computing, and IoT
- Computer vision
- Bayesian network and data mining
- Big Data techniques, models, algorithms, infrastructure, and platform
- Big Data search and mining, security, privacy, and trust
- Big Data applications, tools, and systems
- Big Data mining, and data management
- Grid and cloud computing for Data Analytics
- 5G and Networks for Data Analytics
Former Chairs
- Prof. Qinglin Yang, Sun Yat-sen University, China (16th IEEE MCSoC 2023)
- Prof. Kasem Khalil, University of Mississippi, USA (16th IEEE MCSoC 2023)
