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