2023 International Workshop on Signal Processing and Machine Learning (WSPML 2023)

★2023 5th International Workshop on Signal Processing and Machine Learning (WSPML 2023)---Ei Compendex & Scopus—Call for paper  

September 22-24, 2023｜Hangzhou, China｜Website: www.wspml.org

★WSPML 2023 presents researchers, engineers, and academics with an unprecedented opportunity to associate and interact with some of the foremost experts in the field of Signal Processing and Machine Learning from around the globe. WSPML 2023 aims to be the must-visit conference of the year for the field, and will be the ideal forum for the exchange of ideas and innovations that promise to change the face of the industry. For researchers, an engineers, or industry professionals, this conference could mark an important point in your career.

★Publication and Indexing

All papers will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted and registered papers will be published in the Conference Proceedings by SPIE - The International Society for Optical Engineering (ISSN: 0277-786X), and submitted to EI Compendex, Scopus for indexing. Excellent Papers from WSPML 2023 will be recommended to be submitted for publication in journal.

★Keynote Speakers

Prof. Michalis Matthaiou——Queen’s University Belfast, United Kingdom

Prof. Chik Patrick Yue——The Hong Kong University of Science and Technology, China

★Program Preview/ Program at a glance

September 22: Registration + Icebreaker Reception

September 23: Opening Ceremony+ KN Speech+ Technical Sessions

September 24: Technical Sessions+ Half day tour/Lab tours

★Paper Submission

Submit Via CMT: https://cmt3.research.microsoft.com/WSPML2023

★CONTACT US

Ms. Riva H. W. Wong

Email: [email protected]

Website: www.wspml.org

★Topics of Interest

Signal, Image and Video Processing

Digital Signal Processing

Feature Extractions

Image and Multidimensional Signal Processing

Image/Video Processing and Coding

Mining Images and Texture

Mining Images in Computer Vision

Multimodal Data Fusion

Graphical Models and Kernel Methods

Deep Learning, Machine Learning, Pattern Recognition, Artificial Intelligence

Learning Theory

Machine Learning Methods

Learning of Action Patterns

Neural Methods

Classification Techniques

Data-Driven Adaptive Systems

Data-Driven Models