CONF-SPML 2022

The 2nd International Conference on Signal Processing and Machine Learning (CONF-SPML 2022) was held online from May 12 to 18, 2022. The accepted papers were published in CEUR Workshop Proceedings (ISSN: 1613-0073). Prof. Alex Siow from the National University of Singapore, Prof. David P. Woodruff from Carnegie Mellon University, and Prof. Naira Hovakimyan from UIUC have given keynote speeches on related topics of AI/ML in business, adversarially robust streaming algorithms, and intelligent agricultural management. Also, we invited authors of qualified papers to deliver oral presentations at the online conference. 2 authors have presented their studies of machine learning and convolutional neural network. Questions from the audience were collected and answered by the presenters. This conference provided the participants with good opportunities to exchange ideas and build networks, and it will lead to further collaborations between both universities and other societies.

Highlights

Title of Speech:AI/ML in Business - From Transformation to Sustainable Development
Presented by:Prof Alex Siow, Department of Information Systems and Analytics, School of Computing, National University of Singapore
Title of Speech: Adversarially Robust Streaming Algorithms
Presented by:Prof. David Woodruff, the Computer Science Department, Computer Science, Carnegie Mellon University
Title of Speech: Intelligent Agricultural Management for Soil Carbon Sequestration via Reinforcement Learning
Presented by:Prof. Naira Hovakimyan, Mechanical Science and Engineering, University of Illinois at Urbana-Champaign
Title of Speech:Machine Learning in Unmanned Swarm Technology
Presented by:Mr. Ian McAndrew, Capitol Technology University
Title of Speech: CNN-Based Audio Recognition in Open-set Domain
Presented by: Mr. Hitham Jleed, University of Ottawa

Publishing Information of CONF-SPML 2022

Title
Proceedings of the 2nd International Conference on Signal Processing and Machine Learning
Press
CEUR Workshop Proceedings, Germany
ISSN
1613-0073
Volume
3150