WSPML 2023 - 2023 5th 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: inquiry-AT-wspml.org
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