DaSET 2025
Computer Vision & Pattern Recognition Data Mining & Analysis Databases & Information Systems Engineering & Computer Science (General) Fuzzy Systems Game Theory and Decision Science Automation & Control Theory Real-time & Embedded Systems Software Systems
Call for Papers: The Rise of Autonomous Intelligence in the Era of Data Science
The fusion of Data Science and emerging technologies continues to reshape business operations, the workforce, and the broader landscape of economic and societal progress. As we navigate the era of Industry 4.0 and envision the advancements of Industry 5.0, the demand for sophisticated data science skills and the innovative application of enabling technologies like cloud computing, Blockchain, big data analytics, IoT, and 6G networks remains paramount for driving digital transformation towards a human-centric and sustainable future.
This year, DaSET focuses on the transformative power of The Rise of Autonomous Intelligence in the era of Data Science. Artificial Intelligence (AI) and frontier technologies are no longer just tools; they are evolving into autonomous agents capable of profound impact across industries and society. This conference aims to attract leading industry and academic researchers actively exploring the theoretical underpinnings and practical applications of data science in the context of this burgeoning field of Agentic AI.
DaSET 2025 invites paper submissions that address cutting-edge topics related to Data Science and Emerging Technologies, with a particular emphasis on the theories, methodologies, and applications of autonomous intelligence. We aim to provide a vibrant platform for experts and researchers to discuss the latest advancements and chart new directions for future research aligned with the Sustainable Development Goals for sustainable economic, social, and environmental progress in this era of intelligent autonomy.
Authors are invited to contribute to the conference by submitting articles in the following areas, but are not limited to the following:
Foundational Data Science