Important Dates
Last Date to Submit the Paper | July 31, 2017 |
Notification of Review Outcomes | October 01, 2017 |
Submission of Camera Ready Papers Copyright Form Author Registration Deadline | October 31, 2017 |
Registration Deadline for Delegates | November 30, 2017 |
Conference Dates | 14-15 December 2017 |
Registration Charges
Category | Authors (From India) | IEEE and ACM Members |
Authors from India | 12000 INR | 9600 INR |
Student Authors from India | 8000 INR | 6400 INR |
Authors from Other Countries | 200 USD | 160 USD |
Student Authors from Other Countries | 100 USD | 80 USD |
Delegates/Poster Presentation from India | 5000 INR | 4000 INR |
Delegates/Poster Presentation from Other Countries | 100 USD | 80 USD |
Workshop and Tutorials | 1000 INR or 20 USD | 1000 INR or 20 USD |
All Payment has to be Online Only.
Author Instructions
Authors are requested to submit their file in the format specified in theIEEE Paper TemplateProspective authors are invited to submit original technical papers for publication in the ICMLDS 2017
Important IEEE Policy Announcement The IEEE reserves the right to exclude a paper from distribution after the conference (including its removal from IEEE Xplore) if the paper is not presented at the conference.
Papers are reviewed on the basis that they do not contain plagiarised material and have not been submitted to any other conference at the same time (double submission). These matters are taken very seriously and the IEEE will take action against any author who engages in either practice. Follow these links to learn more:
IEEE Policy on Plagiarism
IEEE Policy on Double Submission
To be submitted in IEEE Xplore for consideration with Catalog and ISBN Number as XXX-YYY, an author of an accepted paper is required to register for the conference and present the paper at the conference. Non-refundable registration fees must be paid prior to the due date of registration. For authors with multiple accepted papers, one registration for each paper is required.
Paper Submission
Prospective authors are invited to submit papers of four (4) to eight (8) A4 pages (including tables, figures and references) in standard IEEE double-column format (it is absolutely necessary to respect the Styleguide for Papers). A blind peer-review process will be used to evaluate all submitted papers. Each full registration for the conference will cover a maximum of one paper; each student registration will cover a single paper only. Extra paper, 2nd paper and onwards, must be registered separately.
The format instructions in the template must be followed, it is notably important to use the right paper format: A4
to have the right margins
not to use page numbering (page footer must be empty)
The IEEE Citation Reference may help you with the references in your paper. Get the list of IEEE recommended keywords. (E-mail link: if it doesn't work directly from your browser, send an empty e-mail to keywords@ieee.org with "IEEE Keywords" in the subject line.)
All submissions should be written in English with a maximum paper length of eight (8) printed pages including figures, without incurring additional page charges. One (1) additional page is allowed with a charge of USD 20 or INR 500, if accepted
Topics and Scope of the Conference
Machine Learning
Model Selection
Evolutionary Parameter Estimation
Graphs and Social Networks
Non-parametric models for sparse networks
Large scale machine learning
Learning Paradigms
Deep Learning
Recommender Systems
Applications
Evaluation of Learning Systems
Data Science
Machine Learning
Model Selection
Learning using Ensemble and boosting strategies
Active Machine Learning
Manifold Learning
Fuzzy Learning
Kernel Based Learning
Genetic Learning
Hybrid models
Evolutionary Parameter Estimation
Fuzzy approaches to parameter estimation
Genetic optimization
Bayesian estimation approaches
Boosting approaches to Transfer learning
Heterogeneous information networks
Recurrent Neural Networks
Influence Maximization
Co-evolution of time sequences
Graphs and Social Networks
Social group evolution – dynamic modelling
Adaptive and dynamic shrinking
Pattern summarization
Graph embeddings
Graph mining methods
Structure preserving embedding
Non-parametric models for sparse networks
Forecasting
Nested Multi-instance learning
Large scale machine learning
Large scale item categorization
Machine learning over the Cloud
Anomaly detection in streaming heterogeneous datasets
Signal analysis
Learning Paradigms
Clustering, Classification and regression methods
Supervised, semi-supervised and unsupervised learning
Algebra, calculus, matrix and tensor methods in context of machine learning
Reinforcement Learning
Optimization methods
Parallel and distributed learning
Deep Learning
Inference dependencies on multi-layered networks
Recurrent Neural Networks and its applications
Tensor Learning
Higher-order tensors
Graph wavelets
Spectral graph theory
Self-organizing networks
Multi-scale learning
Unsupervised feature learning
Recommender Systems
Automated response
Conversational Recommender systems
Collaborative deep learning
Trust aware collaborative learning
Cold-start recommendation systems
Multi-contextual behaviours of users
Applications
Bioinformatics and biomedical informatics
Healthcare and clinical decision support
Collaborative filtering
Computer vision
Human activity recognition
Information retrieval
Cybersecurity
Natural language processing
Web search
Evaluation of Learning Systems
Computational learning theory
Experimental evaluation
Knowledge refinement and feedback control
Scalability analysis
Statistical learning theory
Computational metrics
Data Science
Algorithms
Novel Theoretical Models
Novel Computational Models
Novel Programming Models
Data and Information Quality
Data Integration and Fusion
Cloud/Grid/Stream Computing
High Performance/Parallel Computing
Energy-efficient Computing
Software Systems
Search and Mining
Data Acquisition, Integration, Cleaning
Data Visualizations
Semantic-based Data Mining
Data Wrangling, Data Cleaning, Data Curation, Data Munching
Data Analysis, , Statistical Insights
Decision making from insights, Hidden patterns
Data Science technologies, tools, frameworks, platforms and APIs
Link and Graph Mining
Efficiency, scalability, security, privacy and complexity issues in Data Science
Labelling, Collecting, Surveying, Interviewing and other tools for Data Collection
Applications in Mobility, Multimedia, Science, Technology, Engineering, Medicine, Healthcare, Finance, Business, Law, Transportation, Retailing, Telecommunication