4th International Conference on Big Data and Machine Learning (BDML 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Big Data and Machine Learning. The Conference looks for significant contributions to all major fields of the Big Data and Machine Learning in theoretical and practical aspects. 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 areas of Big Data and Machine.
Prospective authors are invited to submit technical papers of their previously unpublished work. All submissions should be written in English and should have 8 pages at least, including figures, all of them in the standard format pages. Ready versions of accepted papers exceeding Ten (10) pages, up to Twenty (20), will be allowed pending payment of overlength charges (50$/¥300 for each additional page), click
here for more fee details. Topics of interest include, but are not limited to the list below.
Big Data Analytics
Data Science Models and Approaches
Algorithms for Big Data
Big Data Search and Information Retrieval Techniques
Data Mining and Knowledge Discovery Approaches
Machine Learning Techniques for Big Data
Big Data Acquisition, Integration, Cleaning, and Best Practices
Big Data and Deep Learning
Scalable Computing Models, Theories, and Algorithms
In-Memory Systems and Platforms for Big Data Analytics
Big Data and High Performance Computing
Cyber-Infrastructure for Big Data
Performance Evaluation Reports for Big Data Systems
Storage Systems (including file systems, NoSQL, and RDBMS)
Resource Management Approaches for Big Data Systems
Many-Core Computing and Accelerators
Big Data Applications for Internet of Things
Mobile Applications of Big Data
Big Data Applications for Smart City
Healthcare Applications such as Genome Processing and Analytics
Scientific Application Case Studies on Cloud Infrastructure
Data Streaming Applications
Fault Tolerance and Reliability
Scalability of Big Data Systems
Energy-Efficient Algorithms
Big Data Privacy and Security
Big Data Archival and Preservation
Visual Analytics Algorithms and Foundations
Graph and Context Models for Visualization
Analytics Reasoning and Sense-making on Big Data
Visual Representation and Interaction
Big Data Transformation, and Presentation
Deep and Reinforcement Learning
Pattern Recognition and Classification for Networks
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
Data mining in heterogeneous networks
Machine learning for multimedia
Machine learning for Internet of Things
Machine learning for security and protection
Distributed and decentralized machine learning algorithms
Intelligent cloud-support communications
Intelligent ressource allocation
Intelligent energy-aware/green communications
Intelligent software defined networks
Intelligent cooperative networks
Intelligent positioning and navigation systems
Intelligent wireless communications
Intelligent wireless sensor networks
Intelligent underwater sensor networks
