International Conference on Machine Learning and Pattern Recognition on August 02-04, 2024 in Osaka, Japan

International Conference on Machine Learning and Pattern Recognition on August 02-04, 2024 in Osaka, Japan

Conference Proceedings:

Submitted papers will be peer reviewed by conference committees, and accepted papers after proper registration and presentation will be published in the International Conference Proceedings Series by ACM (ISBN: 979-8-4007-1000-1), which will be submitted for indexing by Ei Compendex, Scopus, etc.

 

Keynote & Invited Speakers:

Prof. Ce Zhu (IEEE Fellow), University of Electronic Science and Technology of China, China

Prof. Amir Hussain, Edinburgh Napier University, UK

Assoc. Prof. Guangyu Gao, Beijing Institute of Technology, China

Assoc. Prof. Porawat Visutsak, King Mongkut’s University of Technology North Bangkok, Thailand

Assoc. Prof. Zheng-Ming Gao, Jingchu University of Technology, China

Prof. Jiarong Yang, Shanghai Electric Group Co., Ltd., Central Academe, China

 

Conference Schedule:

Day 1 - Friday - August 2, 2024

10:00 - 17:00 Sign In and Conference Material Collection

16:00 - 18:00 Committee Conference

Day 2 - Saturday - August 3, 2024

09:00 - 12:00 Opening Ceremony and Keynote Speeches

12:00 - 13:30 Lunch

13:30 - 15:30 Invited Speeches and Parallel Oral Sessions

15:30 - 16:30 Poster Sessions

16:30 - 18:30 Invited Speeches and Parallel Oral Sessions

18:30 - 20:30 Dinner Banquet and Award Ceremony

Day 3 - Sunday - August 4, 2024

09:00 - 12:00 Invited Speeches and Parallel Oral Sessions

12:00 - 13:30 Lunch

13:30 - 19:00 City Tour

 

Contact:

Conference Secretary: Miss Chloe Jiang

Email: [email protected]

Tel.: +86-19180927671

 

Track 1: Machine Learning

▪ Active learning

▪ Dimensionality reduction

▪ Feature selection

▪ Graphical models

▪ Imitation learning

▪ Intelligent Business Computing

▪ Intelligent Systems

▪ Intelligent control system

▪ Intelligent human machine interface

▪ Intelligent robot

▪ Latent variable models

▪ Learning for big data

▪ Learning from noisy supervision

▪ Learning in graphs

▪ Multi-objective learning

▪ Multiple instance learning

▪ Multi-task learning

Track 2: Pattern Recognition

▪ Analysis and detection of singularities

▪ Animation image analysis

▪ Classification

▪ Cluster analysis

▪ Deformation analysis

▪ Descriptor of shapes

▪ Diagnosis of faults

▪ Document analysis

▪ Emotion computation

▪ Enhancement and restoration

▪ Feature extraction

▪ Hand gestures classification

▪ Human face recognition

▪ Image compression

▪ Image fusion

▪ Image indexing and retrieval

▪ Image recovery

 

Name: International Association of Computer Science and Information Technology
Website: http://www.iacsit.org/

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