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Machine Learning
Publishing model:
Hybrid

Machine Learning - Call for Papers: DSAA 2024 Journal Track with Machine Learning Journal

We invite submissions to the journal track of the 2024 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2024) with Machine Learning Journal. The DSAA conference provides an international forum for the discussion on the latest high-quality research results in all theoretical and practical areas related to data science and advanced analytics, which span across various disciplines including statistics, learning, analytics, computing, and informatics, and domains from business to government, health and medicine, and social problems. The DSAA'2024 Journal Track with Machine Learning Journal (MLJ) seeks original, unpublished high-quality submissions, where the accepted papers will be published in MLJ with an extended abstract included in the DSAA'2024 proceedings.

Topics of Interest

We welcome original and well-grounded research papers on all aspects of foundations of data science, analytics, and machine learning, including but not limited to the following topics:

  • Analytical and learning foundation
  • Mathematical foundations for data science and analytics
  • Statistical foundations for data science and analytics
  • Physics-informed modeling, analytics and learning
  • Automated analytics, learning, and inference
  • Feature engineering, embedding and mining
  • Representation learning
  • Non-IID learning, nonstationary, coupled and entangled learning
  • Heterogeneous, mixed, multimodal, multi-view and multi-distributional learning
  • Online, streaming, dynamic and real-time learning
  • Causality and causal learning
  • Reinforcement learning including with human feedback
  • Multi-instance, multi-label, multi-class and multi-target learning
  • Unsupervised, semi-supervised and weakly supervised learning
  • Learning complex interactions, couplings, and relations
  • Deep learning theories and models
  • Large multimodal modeling
  • Learning from network and graph data
  • Learning from data with domain and web knowledge
  • Autonomous learning and optimization systems
  • Open world (object, domain, set, task etc) learning
  • Impactful analytical and learning applications
  • Learning to fuse data/information from disparate sources
  • Understanding data characteristics and complexities
  • Complex data preprocessing, manipulation and augmentation
  • Social, economic, financial and cultural analytics
  • Graph and network embedding, analysis, learning and mining
  • Machine learning for recommendation, marketing and online business
  • Data-driven computer vision and image processing
  • Cybersecurity and information disorder, misinformation/fake detection
  • Analytics and learning for IoT, smart city, smart home, telecommunications, 5G and mobile services
  • Government and enterprise data science
  • Analytics and learning for transportation, manufacturing, procurement, and Industry 4.0
  • Analytics and learning for energy, smart grids and renewable energies
  • Agricultural, environmental, climate and spatio-temporal analytics
  • Human-centered and domain-driven analytics and learning
  • Fairness, explainability and algorithm bias
  • Risk, compliance, regulation, anomaly, debt, failure and crisis analysis
  • Privacy, ethics, transparency, accountability, responsibility, trust, reproducibility and retractability
  • Green and energy-efficient, scalable, cloud/distributed and parallel analytics


Important Dates
Paper submission due: April 15, 2024, and May 19, 2024
Final paper notification due: July 24, 2024
MLJ revision submission due: August 21, 2024
DSAA'2024 extended abstract due: August 21, 2024

Guest Editors
Longbing Cao, Macquarie University, Australia
David C. Anastasiu, Santa Clara University, USA
Qi Zhang, Tongji University, China
Xiaolin Huang, Shanghai Jiaotong University, China

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