2024 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2024)

Memo

★2024 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2024)---Ei Compendex & Scopus—Call for paper   June 7-9, 2024｜online｜Website: www.wspml.org   ★WSPML 2024 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 2024 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, engineers, or industry professionals, this conference could mark an important point in your career.   ★Publication and Indexing All accepted and registered full papers will be published conjunctly with other conference proceeding in the form of digital conference proceeding and submitted to major citation databases like EI Compendex, SCOPUS, Google Scholar etc. for reviewing and indexing. Excellent Papers from WSPML 2024 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 June 7: Registration + Icebreaker Reception June 8: Opening Ceremony+ KN Speech+ Technical Sessions June 9: Technical Sessions+ Half day tour/Lab tours   ★Paper Submission Submit Via CMT: https://cmt3.research.microsoft.com/WSPML2024   ★CONTACT US Ms. Riva H. W. Wong Email: This email address is being protected from spambots. You need JavaScript enabled to view it. 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