International Conference on
Big Data, Machine Learning and Applications
(BigDML 2021)

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The BigDML solicits outstanding original research papers to be submitted in the following tracks but not limited to:


Track I: Big Data

Track-II: Machine Learning

Track III- Applications

  • Foundational Models for Big Data
  • Machine Learning
  • Data Mining
  • Algorithms and Programming Techniques for Big Data Processing
  • Nonlinear Dimensionality Reduction and Manifold Learning
  • Big data in biology
  • Big Data Analytics and Metrics
  • Boosting and Ensemble Methods
  • Recommender Systems
  • Cloud Computing Techniques for Big Data
  • Classification and Clustering
  • Social Network Analysis
  • Big Data as a Service
  • Collaborative Filtering
  • Computer Vision
  • Big Data Open Platforms
  • Shallow Learning and Unlearning
  • Image Segmentation
  • Big Data Persistence and Preservation
  • Missing Data
  • Information Retrieval
  • Big Data Quality and Provenance Control
  • Adaptive Data Analysis
  • Cryptography and Network Security
  • Big Data Storage and Retrieval
  • Regression
  • Blockchain
  • Big Data System Security and Integrity
  • Semi-Supervised Learning
  • Video Segmentation
  • Big Data Information Security
  • Spectral Methods
  • Tracking and Motion in Video
  • Privacy Preserving Big Data Analytics
  • Stochastic Methods
  • Audio and Speech Processing
  • Usable Security and Privacy for Big Data
  • Structured Prediction
  • Natural Language Processing
  • Big Data Service Performance Evaluation
  • Unsupervised Learning
  • Natural Scene Statistics
  • Big Data Service Reliability and Availability
  • Link prediction
  • Networking
  • Real-Time Big Data Services
  • Deep Learning
  • Privacy, Anonymity, and Security
  • Usage of Big Data Science for Optimization
  • Optimization Techniques
  • AI & Robotics
  • Usage of Optimization for Big Data Science
  • Bayesian Model
  • Signal Processing
  • Big Data Science for Operational Research
  • Belief Propagation
  • Text Analysis
  • Algorithms and Systems for Big Data Search
  • Causal Inference
  • Time Series Analysis
  • Distributed, and Peer-to-peer Search
  • Distributed Inference
  • Web semantic
  • Machine Learning based on Big Data
  • Gaussian Processes
  • Biomedical knowledge discovery
  • Visualization Analytics for Big Data
  • Hierarchical Models
  • Bioimaging
  • Big Social Media Mining
  • Latent Variable Models
  • Neuroscience and Cognitive Science
  • Real-Time Big Data Analytics
  • Hierarchical Models
  • Genomics
  • Large-scale experimentations
  • Topic Models
  • Drug design and discovery
  • Big Data Integration for Healthcare
  • Markov Decision Processes
  • Biological sequence analysis
  • Big Data Sets
  • Deep Reinforcement Learning
  • Immunoinformatics
  • Big Data Application Benchmarks
  • Game Theory
  • Bio-ontology and semantics
  • Big Data for Enterprise, Government, and Society
  • Machine Unlearning
  • Bioinformatics models, methods & algorithms
  • Big Data for Smart Cities.
  • Deep Metric Learning
  • Genetics of cell differentiation & reprogramming
  • Big Data Analytics for Smart City
  • Faster-RCNN, Spike Neural Networks
  • Microarray analysis
  • Big Data Analysis for IoT
  • Graph Neural Networks
  • Next Generation Sequencing
  • Big Data for Science and Engineering Research
  • Tiny Machine Learning
  • Medical and Health informatics
  • Big Data Sciences
  • Generative Adversarial Deep Learning
  • Workflow and Knowledge Management
  • Datacenter
  • Deep Learning Applications: Biomedical, Agriculture, IoT, Edge Computing, Security, etc.
  • Systems biology
  • Multimodal Learning
  • IoT and Edge Computing
  • Precision Medicine and Precision Agriculture
  • Smart city, Smart Agriculture, and Smart Systems

    • Please visit author's guideline before submission.

    Please click here to submit your paper via EasyChair.