6th International Conference on Machine Learning & Trends (MLT 2025)
May 17 ~ 18, 2025, Zurich, Switzerland
6th International Conference on Machine Learning & Trends (MLT 2025)
May 17 ~ 18, 2025, Zurich, Switzerland
Scope & Topics
6th International Conference on Machine Learning & Trends (MLT 2025) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & its Trends. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to
Topics of interest include, but are not limited to, the following:
Applications
Bayesian Network
Computer Vision
Data Mining
Deep Learning
Learning in knowledge-intensive systems
Learning Methods and analysis
Learning Problems
Machine Learning Algorithms
Neural Networks
Predictive Learning
Reinforcement Learning
Supervised Machine Learning
Trends
Unsupervised Machine Learning
Paper Submission
Authors are invited to submit papers through the conference Submission System by Closed .
Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).
Selected papers from MLT 2025, after further revisions, will be published in the special issues of the following journals
Machine Learning and Applications: An International Journal (MLAIJ)
International Journal of Artificial Intelligence & Applications (IJAIA)
Proceedings
Hard copy of the proceedings will be distributed during the Conference. The softcopy will be available on AIRCC Digital Library