2024 2nd Asia Conference on Machine Learning, Algorithms and Neural Networks (MLANN 2024)
Website: http://www.mlann.net/
Venue: Xiamen, China (both online and in-person)
Conference Date: April 26-28, 2024
2024 2nd Asia Conference on Machine Learning, Algorithms and Neural Networks (MLANN 2024) will be held in Xiamen, China during April 26-28, 2024. It is sponsored by The International Society for Applied Computing (ISAC) and The Technical Institute for Engineers (T.I.E.).
During the upcoming conference, the invited renowned professors will share with us the recent innovations in the fields of Machine Learning, Algorithms and Neural Networks. The conference will mainly feature on keynote speeches as well as peer-reviewed paper presentations. In addition, social program or academic visit will be arranged to encourage communication, discussion or cooperation among the researchers in this field.
We invite submissions of papers presenting an original high-quality research and development for the conference. All papers must be written in English and will be peer-reviewed by technical committees of the Conference and all accepted papers will be published in the conference proceedings.
*Conference Speakers:
Keynote Speaker I:
Prof. Nikola Kasabov, Auckland University of Technology, New Zealand (IEEE Life Fellow)
Keynote Speaker II:
Prof. CHEUNG, Yiu-ming, Hong Kong Baptist University, Hong Kong, China (IEEE Fellow)
Keynote Speaker III:
Prof. Xiongbiao Luo, Xiamen University, China
Keynote Speaker IV:
Prof. Zhao Zhang, Hefei University of Technology, China
*Call for papers:
Deep and Reinforcement Learning
Machine Learning for Network Slicing Optimization
Machine Learning for 5G system
Machine Learning for User Behavior Prediction
New Innovative Machine Learning Methods
Optimization of Machine Learning Methods
Performance Analysis of Machine Learning Algorithms
Experimental evaluations of machine learning
Machine learning for multimedia
Analysis of algorithms
Competitive analysis
Computability theory
Computational complexity theory
Embarrassingly parallel problem
Emergent algorithm
Computational Science and Engineering
Deep learning
Signal processing
Information extraction
Natural language reasoning
Pattern recognition
(for more topics: http://www.mlann.net/topic.html )
*Publication and indexing:
★All accepted and registered papers will be included in the Conference Proceedings and published by ACM (ISBN: 979-8-4007-1660-7), which will be archived in the ACM Digital Library, and indexed by Ei Compendex and Scopus.
★MLANN 2023 all accepted papers have been published by ACM (ISBN: 979-8-4007-0003-3) and indexed by Ei Compendex and Scopus successfully within 1 month after publication.
*Submission Methods:
1. Online Submission System: https://cmt3.research.microsoft.com/MLANN2024
2. Submission Email: [email protected]
Submission guidelines: http://www.mlann.net/submission.html
*Join the conference as:
Authors:
Authors are expected to submit full papers to the submission system for further review by our Technical Committees. All accepted papers after proper registration and presentation in the conference will be published and submitted for indexing.
Presenters Only:
The presenters are expected to submit abstracts only for presentation in the conference without paper publication in the conference proceedings.
Listeners:
Listeners are expected to attend the conference without paper presentation or publication.
Reviewers:
PhD-holders in the research fields of Machine Learning, Algorithms and Neural Networks are welcome to be our reviewers and a certificate can be issued.
Sponsors/Partners:
If you are interested in cooperating with us, such as sponsoring or being a partner of MLANN 2024, you are welcome to contact us at: [email protected]
*Contact us:
Website: http://www.mlann.net/
Conference Secretary: Ms. Annie Yang
Tel: +8613502461341
Email: [email protected]
If you have any question or request about our conference, no matter regarding submission, registration, participation or any further question, please send email to us and you will get feedback within 24 hours.
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