It is planned to publish the proceedings with Springer in their Communications in Computer and Information Science (CCIS) series (final approval pending)
We invite authors to participate in the conference by submitting articles that showcase their research findings, projects, surveys, and industry experiences, which highlight the significant progress made in the fields of machine learning for data analytics, modernization and automation.
By sharing their work with the community, authors will contribute to the advancement of these fields and foster collaborative efforts towards addressing the most pressing challenges and opportunities in the domain.
To ensure that the conference features novel and high-quality research, all submissions must be original and not have been published previously or be under consideration for publication elsewhere while undergoing review for this conference. We expect authors to uphold the highest ethical standards in their research and to adhere to proper citation and attribution practices. We encourage authors to submit their work within the designated time frame to ensure timely review and consideration.
To ensure fairness and impartiality in the review process, the Technical Programme Committee (TPC) will subject all papers submitted to the conference to anonymous review. This means that authors will not be aware of the identity of the reviewers assigned to evaluate their work. The TPC comprises experts in the field of machine learning for data analytics, modernization and automation, who will assess the submissions based on their quality, relevance, originality, and significance to the field. By maintaining anonymity in the review process, we aim to uphold the highest standards of objectivity and integrity, and ensure that each submission receives a fair and rigorous evaluation.
To ensure that the accepted papers are of the highest quality, all submissions will be evaluated based on the following criteria:
Relevance and timeliness: The paper should address a relevant and timely problem or research question in the field of machine learning for data analytics, modernization and automation.
Technical content and scientific rigor: The paper should demonstrate a strong technical foundation and scientific rigor in its methodology, analysis, and results.
Novelty and originality: The paper should introduce new ideas, concepts, or approaches that represent a significant contribution to the field.
Quality of presentation: The paper should be well-written, clear, and easy to understand, with appropriate use of visuals and references.
Based on these criteria, each submission will be given an overall recommendation by the Technical Programme Committee (TPC). Authors whose papers receive positive reviews and final approval from SpringerNature will be invited to present their work at the conference. A notification of acceptance will be sent to the authors. The Organizing Committee may only communicate with the corresponding author who submitted the paper, and not with all authors.