Conference Information

ICMLDS 2018: International Conference on Machine Learning and Data Science

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Submission Date:
2018-09-08 Extended
Notification Date:
2018-10-05
Conference Date:
2018-12-21
Location:
Hyderabad, India
Viewed: 20640   Tracked: 6   Attend: 1
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Conference Partner Index (CP-I)

44.0 / 100
Ranked #3,285 of 5,693 conferences · Top 58%

#189 of 337 in Data Mining & Databases #396 of 742 in Artificial Intelligence & Machine Learning

Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
27
Public record completeness (15%)
25

Inputs used: Researchers following it here: 6 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-06

Call For Papers

ICMLDS 2018 (International Conference on Machine Learning and Data Science) is an academic conference held in Hyderabad, India on 2018-12-21. The paper submission deadline is 2018-09-08 (extended). Acceptance notifications are sent on 2018-10-05.

The International Conference on Machine Learning and Data Science will focus on topics that are of interest to computer and computational scientists and engineers. MLDS-2018 will bring together researchers and practitioners from academia, industry and government to deliberate on the algorithms, systems, applied, and research aspects of Machine Learning and Data Science. The conference will be held in Hyderabad - Telangana, India, and will feature multiple eminent keynote speakers, and presentation of peer reviewed original research papers and exhibits. 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 Modelsp Novel Computational 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
Last updated by Dou Sun on

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