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ICMLBDA 2023 : 3rd International Conference on Machine Learning and Big Data Analytics

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Link: https://icmlbda2023.iaasse.org/
 
When May 29, 2023 - May 30, 2023
Where Jote, Arunachal Pradesh, India
Submission Deadline Mar 31, 2023
Notification Due Apr 25, 2023
Categories    machine learning   big data analytics   reinforcement learning   neural information processing
 

Call For Papers

Held at the National Institute of Technology, Arunachal Pradesh, India in collaboration with:
* North Eastern Regional Institute of Science and Technology (NERIST), India
* California State University-San Bernardino, USA
* Emlyon Business School, France

The 3rd International Conference on Machine Learning and Big Data Analytics (ICMLBDA 2023) aims to provide a forum for researchers from both academia and industry to share their latest research contributions, future vision in the field and potential impact across industries of ML and Big Data Analytics.

This conference is basically focused on advanced automation, computational optimization of Machine Learning in all engineering-based applications as well as include specific plenary sessions, invited talks and paper presentations focusing on the applications of ML and BDA in the fields of computer/ electronics/ electrical/ mechanical/chemical/Textile engineering, Healthcare and Agriculture, Business and Social Media and other relevant domains.

The ICMLBDA 2023 will provide its attendees with an uncommon opportunity to expand their network beyond their immediate professional environment. It is a unique chance to work with other accomplished individuals from diverse areas towards the common goal of shaping the future of communication, computing and society.


Conference Tracks

Machine Learning

Foundations
Applications of deep learning in various engineering streams
Neural information processing systems and architectures
Training schemes, GPU computation and paradigms
Reinforcement Learning
Natural Language Processing
GANs and other neural generative methods
Representation embedding spaces
Deep Belief Networks and Statistical Learning
Advance Optimization techniques
Autonomous Computing
Extreme Learning Machines
Hybrid Intelligent Systems

Big Data Analytics

Big Data Analytics Adoption
Benefits of Big Data Analytics
Volume Growth of Analytic Big Data
Managing Analytic Big Data
Big data storage architecture
GEOSS clearing house
Data Science Models and Approaches
Big Data Acquisition, Integration, Cleaning, and Best Practices
Big Data and High Performance Computing
Scalable Computing Models, Theories, and Algorithms
Performance Evaluation Reports for Big Data Systems
Many-Core Computing and Accelerators
Analytics Reasoning and Sense-making on Big Data



Submission Guidelines

Papers reporting original* and unpublished research results pertaining to the related topics are solicited. *(papers with plagiarism more than 30% will be outrightly rejected).

Full paper manuscripts must be in English of up to 7 pages as per format.

For authors convenience, Springer has summarized in the Author Guidelines document how a proceedings paper should be structured, how elements (headings, figures, references) should be formatted using our predefined styles, etc. The PDF of the Authors Guidelines can be downloaded from the given link or as part of the zip files containing the complete sets of instructions and templates for the different text preparation systems.

Springer has developed LaTeX style files and Word templates to help prepare paper. LaTeX is the preferred format for texts containing several formulae, but Word templates are also available on the following link:

https://www.springer.com/gp/authors-editors/conference-proceedings/conference-proceedings-guidelines

Submissions should NOT include the author(s), affiliation(s), e-mail address(es), and postal address(es) in the manuscripts. Papers will be selected based on their originality, timeliness, significance, relevance, and clarity of presentation. Paper submission implies the intent of at least one of the authors to register and present the paper, if accepted.

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