Deep Learning Methods Applied to Big Data Analysis
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 October 2026 | Viewed by 1
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, deep learning has revolutionized our capacity to analyze and interpret complex, large-scale datasets. The exponential growth of data from sources like IoT devices, scientific sensors, healthcare records, financial transactions, and social media has created an urgent need for advanced analytical models. This Special Issue, "Deep Learning Methods Applied to Big Data Analysis," aims to address this need by bringing together cutting-edge research that leverages deep learning to extract meaningful insights from massive, heterogeneous, and high-dimensional data.
The scope of this Issue encompasses novel deep learning architectures and innovative methodologies designed to tackle key challenges in scalability, interpretability, efficiency, and real-time processing. We welcome contributions that explore emerging paradigms such as self-supervised and federated learning, techniques for multimodal data fusion, and frameworks for explainable AI (XAI). Furthermore, we strongly encourage submissions that focus on practical implementations and cross-disciplinary applications in fields including but not limited to computational biology, smart cities, cybersecurity, and industrial informatics.
By showcasing state-of-the-art research and compelling case studies, this Special Issue seeks to advance the theoretical foundations and practical applications of deep learning. Our ultimate goal is to illuminate how these powerful methods can transform complex big data into actionable knowledge, thereby driving innovation and intelligent decision-making across diverse sectors.
Prof. Dr. Zhong Xiang
Guest Editor
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Keywords
- deep learning
- big data analytics
- neural networks
- multimodal data fusion
- explainable artificial intelligence (XAI)
- self-supervised learning
- federated learning
- high-dimensional data analysis
- scalable machine learning
- intelligent data-driven systems
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