ICMLC 2025 will be held in Guangzhou, China on February 14-17, 2025. The ICMLC 2025 organizing committees invite you to submit the papers to special session. Each special session will be arranged for around 2 hours on Feb. 15 or 16’s afternoon. Now the information on special session as following:

» Special Session 1

    Impact of Data Quality Improvement on Machine Learning

Session Chair: Prof. Jing Zhang, Southeast University, China
Co-chair: Prof. Ming Wu, Hohai University, China

In the real world, one of the main factors that hinders the widespread application of machine learning methods is that demanders cannot obtain high-quality data that can be used to train high-performance learning models. Therefore, improving data quality is often the most direct way to improve the generalization performance of machine learning models, and can even achieve better benefits than algorithm design and model tuning. This special session explores the impact of data quality improvement methods on machine learning performance. We welcome any submissions related to data quality improvement, including but not limited to data acquisition and data preprocessing, data completion, sample enhancement, noise identification and correction, few-shot and zero-shot learning, crowdsourcing learning, multimodal data fusion, alignment, and learning, learning with noisy data, transfer learning, and the application of pre-trained or large language models in data quality improvement.

S1-Submission

» Special Session 2

     Learning-based System Design and Verification Technologies for Complex Embedded Systems

Session Chair: Prof. Shuai Zhao, Sun Yat-sen University, China
Co-chair: Prof. Wenle Wang, Jiangxi Normal University, China

With ever-complex functionalities being integrated into embedded systems, the complexity of both their hardware and software has increased substantially, as exemplified by applications such as automobiles, avionics, and medical systems. These systems are often required to meet stringent requirements for predictability, performance, safety, reliability, and energy efficiency, making their design and verification ever-more challenging.
Existing design and verification methodologies heavily relied on static methods, such as fully-partitioned systems with resource isolations. However, as embedded systems become more intricate and dynamic, these approaches face significant limitations. The advent of machine learning (ML) and artificial intelligence (AI) offers new opportunities to address these challenges by introducing adaptive, data-driven methods that would enhance both the design and the verification of complex embedded systems.
The aim of this session is to explore the latest developments and innovations in applying AI/ML to the design, development, verification and testing of emerging embedded systems. Potential topics include but are not limited to the following:

• Learning Techniques for Design of Embedded Systems.
• Learning Technologies for Verification and Testing of Embedded Systems.
• Real-time AI/ML on Edge Devices.
• Learning-based Optimization on Resource-constrained Devices.
• Adaptive AI/ML Algorithms for Scheduling Dynamic Embedded Systems.
• Energy-efficient Training Strategies for Embedded AI Models.
• Security and Privacy in AI/ML-Driven Embedded Systems.
• Explainable AI for Embedded Systems

S2-Submission

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Welcome to submit more proposal on ICMLC 2025 special session, please download:
 Proposals Submission Guidelines.

Learn more details or submit the proposal, please contact us:

Ms. Allison Fung
Email: icmlc@vip.126.com
Tel: +86-13258-11111-7