The 2nd lntermational Conference on Intelligent Data Analvtics and Sustainable Systems (IDASS2025) will be held in Qingdao, China, on October 20-22th, 2025. Oct. 20th is the registration date, Oct. 21th to Oct. 22th are conference days.
The 2025 International Conference on Intelligent Data Analytics and Sustainable Systems aims to enhance the advanced technical development, bridge the gap between the two domains and foster discussions, collaborations and knowledge exchange among the experts with diverse backgrounds. We invite researchers, academicians, practitioners, policymakers and industry professionals to submit their original research papers, case studies and abstracts for consideration. This conference will create a multidisciplinary platform for participants to showcase and share their cutting-edge research findings, practical applications, experiences and insights related to intelligent data analytics and sustainable systems across a wide range of problem domains, theories, technologies and applications from software and hardware designs.
Key themes and topics to be covered in the conference include, but are not limited to:
Intelligent data analytics methods or tools, including theory, algorithm and emerging aspects of data science and computational intelligence as well as applications
Methods or tools for sustainable systems, such as data-driven strategies for climate change adaptation and mitigation, sustainable urban planning and smart cities, sustainable agriculture and food systems, energy-efficient data analytics and smart grids; cyber-security; communication networks
Sustainability challenges for health, business, education, economy, ocean and environment;
Advanced human-machine interaction technologies, including robotics, intelligent vehicles, virtual reality and augmented reality
Computer Vision and visualization technologies and their applications such as image-based recognition, data generation; data visualization, curation and management; cognition
Machine learning and AI technologies including model pruning, advanced deep learning technology, optimal neural network, statistical learning architectures and technology-enhanced learning; natural language processing