Introduction

IEEE ICMLA'22 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML).
The conference provides a leading international forum for the dissemination of original research in ML, with emphasis on applications as well as novel algorithms and systems. Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, games, are especially encouraged. 
Accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements.
It is planned that IEEE ICMLA'22 will be organized as in pre-pandemics time, with participants attending the conference in person (not remotely), unless Covid rules will change towards the end of the year and this will not be possible.

Sponsor Type:1; 9

Committee

Conference Co-Chairs
Mehmed Kantardzic, University of Louisville, USA
Vasile Palade, Coventry University, UK

Program Committee Co-Chairs
Daniel Neagu, University of Bradford, UK
Longzhi Yang, University of Northumbria, UK 

Special Sessions and Workshop Chair
Kit Yan Chan, Curtin University, Australia 

Tutorials Chair
Yi (Joy) Li, Kennesaw State University, USA 

ICMLA Challenge Chair
Wenbin Zhang, Carnegie Mellon University, USA 

Publicity Chair
Khan Muhammad, Sejong University, Republic of Korea 
Uche Onyekpe, York St. John University, UK 

Local Organization Chair
Andrew Karem, University of Louisville, USA 
 

Call for paper

Important date

2022-05-03
Abstract submission deadline

The conference provides a leading international forum for the dissemination of original research in ML, with emphasis on applications as well as novel algorithms and systems. Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, games, are especially encouraged.

Contributions describing applications of machine learning (ML) techniques to real-world problems, interdisciplinary research involving machine learning, experimental and/or theoretical studies yielding new insights into the design of ML systems, and papers describing development of new analytical frameworks that advance practical machine learning methods are especially encouraged.

Submission Topics

The technical program will consist of, but is not limited to, the following topics of interest:

  • statistical learning
  • neural network learning
  • learning through fuzzy logic
  • learning through evolution (evolutionary algorithms)
  • reinforcement learning
  • multi-strategy learning
  • cooperative learning
  • planning and learning
  • multi-agent learning
  • online and incremental learning
  • scalability of learning algorithms
  • inductive learning
  • inductive logic programming
  • Bayesian networks
  • support vector machines
  • case-based reasoning
  • machine learning for bioinformatics and computational biology
  • multi-lingual knowledge acquisition and representation
  • grammatical inference
  • knowledge acquisition and learning
  • knowledge discovery in databases
  • knowledge intensive learning
  • knowledge representation and reasoning
  • machine learning and information retrieval
  • machine learning for web navigation and mining
  • learning through mobile data mining
  • text and multimedia mining through machine learning
  • distributed and parallel learning algorithms and applications
  • feature extraction and classification
  • theories and models for plausible reasoning
  • computational learning theory
  • cognitive modeling
  • hybrid learning algorithms

Applications of machine learning in:

  • medicine, health, bioinformatics and systems biology
  • industrial and engineering applications
  • security applications
  • smart cities
  • game playing and problem solving
  • intelligent virtual environments
  • economics, business and forecasting applications, etc.

Guidlines

High quality papers in all Machine Learning areas are solicited. Papers that present new directions in ML will receive careful reviews. Authors are expected to ensure that their final manuscripts are original and are not appearing in other publications. Paper should be limited to 8 pages and submitted in IEEE format (double column). Papers will be reviewed by the Program Committee on the basis of technical quality, originality, significance and clarity. All submissions will be handled electronically. Accepted papers will be published in the conference proceedings, as a hardcopy. Authors of the accepted papers need to present their papers at the conference. A selected number of accepted papers will be invited for possible inclusion, in an expanded and revised form, in some journal special issues.

ICMLA'22 Best Paper Award and ICMLA'22 Best Poster Award will be conferred at the conference to the authors of the best research paper and best poster presentation, respectively, based on the reviewers and Programme Committee recommendations.

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Important Date

  • Conference Date

    Dec 12

    2022

    to

    Dec 15

    2022

  • May 03 2022

    Abstract Submission Deadline

  • Dec 15 2022

    Registration deadline

Sponsored By
Association for Machine Learning and Applications - AMLA
IEEE Systems, Man, and Cybernetics Society
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