Advanced Machine Learning Techniques for Big Data Challenges
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 428
Special Issue Editors
Interests: generative artificial intelligence; machine intelligence; big data; business intelligence
Interests: adverse drug reaction; social networking (online); social media
Special Issue Information
Dear Colleagues,
In the era of Big Data, advanced machine learning techniques are increasingly being leveraged to extract valuable insights from Big Data and drive decision-making in various fields. This Special Issue aims to collect papers that explore and leverage advanced machine learning techniques (e.g., generative artificial intelligence (GAI), large language models (LLM)、graph neural networks, reinforcement learning, federated learning, self-supervised learning, transfer learning, ensemble learning) to embrace valuable opportunities and to address multifaced challenges posed by Big Data.
The scope of this Special Issue encompasses a wide range of topics, including, but not limited to, the following: innovative machine learning algorithms designed for large-scale data processing; techniques for dimensionality reduction and feature selection in high-dimensional data; strategies for handling noisy, incomplete data, or data synthesis; approaches for bias mitigation and fairness in LLM, knowledge distillation in LLM, and AI agents; and applications of advanced machine learning in various domains (e.g., fintech, e-commerce, healthcare, recommendation, and intelligent manufacturing).
Prof. Dr. Gang Wang
Guest Editor
Dr. Jing Liu
Dr. Jingling Ma
Guest Editor Assistants
Manuscript Submission Information
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Keywords
- generative artificial intelligence (GAI)
- large language models (LLMs)
- graph neural networks
- reinforcement learning
- federated learning
- self-supervised learning
- transfer learning
- ensemble learning
- machine learning
- big data
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