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4th International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2024
09-11 May 2024
 
Conference Proceedings
ALL ACCEPTED & PRESENTED papers will be published in SCOPUS indexed SPRINGER PROMS.
Springer PROMS

A virtual meeting platform may be made available for all registered authors who intend to submit their work but are unable to attend the conference.

National Institute of Technology, Kurukshetra

Emlyon Business School, France

CSUSB, USA

ICMLBDA 2024 Call for Papers

The proposed 4th International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2024 provides a platform for researchers and professionals to share their research and reports of new technologies and applications in ML and Big Data Analytics like biometric Recognition Systems, medical diagnosis, industries, telecommunications, AI Petri Nets Model-Based Diagnosis, gaming, stock trading, Intelligent Aerospace Systems, robot control, law, remote sensing and scientific discovery agents and multiagent systems; and natural language and Web intelligence. The ICMLBDA 2024 aims to bridge the gap between these non-coherent disciplines of knowledge and foster unified development in next-generation computational models for machine intelligence.

All accepted and presented papers will be reviewed for possible publication in SPRINGER’ s PROMS.

Please consider submitting your works to this conference. We are interested in the entire range of concepts from theory to practice, including case studies, works-in-progress, and conceptual explorations.

Important Dates / Deadlines

  • Submission (Final Extension)                                             March 15, 2024

  • Notification of Acceptance                                                April 05, 2024

  • Early Bird Registration                                                        April 10, 2024

  • Oral presentation of selected works                              May 09-11, 2024

  • Camera Ready papers                                                        June 05, 2024

  • Publication (Tentative)                                                        December, 2024

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Papers reporting original* and unpublished research results pertaining to the related topics are solicited.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 10 pages as per Springer 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.

The ONLINE submission site is

Springer EquinOCS
 

Presentation through Virtual Platform

A virtual meeting platform is to be made available for all registered authors who intend to submit their work but are unable to attend the conference. Authors may register and present their paper through the virtual platform.

Authors shall be sent a Certificate of Participation, Conference Proceedings link and relevant literature by mail.

Acceptance & Publication

After a double-blind peer review, qualifying Regular Papers may be accepted as either Full Papers or Short Papers.

  • All accepted and presented papers of the conference will be reviewed for possible publication in SPRINGER PROMS.
  • The papers must be part of the worldwide scholarly discourse in the field covered by the library. The reviews will be done to make sure papers are relevant for the chosen classifications to ensure subscribers receive relevant content.
  • NO extra fee is charged from authors for inclusion in the PROMS.
  • Authors will grant a non-exclusive, revocable license that allows providing services to users.

Indexing

Post-conference, proceedings will be made available to the following indexing services for possible inclusion:

  • ISI Conference Proceedings Citation Index - ISI Web of Science
  • Google Scholar
  • Scopus
  • DBLP

Depending on the focus of the particular indexing services, they may decide to include or not. If included one can expect it in 10-12 months. DBLP and Google Scholar are fast.

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