Data Mining and Machine Learning in the Era of Big Knowledge and Large Models
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 289
Special Issue Editors
Interests: trustworthy artificial intelligence; federated learning and graph representation learning; large models and content security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the fields of data mining and machine learning have undergone a transformative shift with the advent of big data and the development of increasingly complex models. The integration of advanced algorithms with vast amounts of data has enabled unprecedented insights and predictions across various domains including healthcare, finance, marketing, and more. This Special Issue aims to explore the latest advancements, challenges, and applications in data mining and machine learning within the context of big knowledge and large models.
We invite researchers to contribute original research articles, reviews, and short communications on topics related to data mining and machine learning, including but not limited to the following:
- Scalable algorithms for big data analysis;
- Deep learning architectures and techniques;
- Transfer learning and domain adaptation in large-scale models;
- Federated learning and distributed machine learning systems;
- Explainable AI and interpretable machine learning models;
- Privacy-preserving data mining and machine learning;
- Secure and trustworthy machine learning;
- Reinforcement learning in complex environments;
- Meta-learning and automated machine learning;
- Optimization techniques for large-scale models;
- Applications of data mining and machine learning in real-world scenarios.
We encourage submissions that present novel methodologies, theoretical insights, experimental results, and practical applications that push the boundaries of data mining and machine learning in the era of big knowledge and large models.
Prof. Dr. Jing Zhang
Dr. Chenyang Bu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- data mining
- deep learning
- big data analytics
- knowledge graph
- large language models
