International Conference on Applied Machine Learning, Sustainable Infrastructure, and Network Resilience (AML-SINR 2027)

Authors must submit full papers through the official conference submission system. Manuscripts must follow the prescribed template and formatting guidelines, and submissions must be original and not under consideration elsewhere.

Each submission is screened by the Technical Program Committee for scope relevance, formatting compliance, plagiarism using similarity detection software, and ethical compliance. Papers failing to meet minimum standards may be desk-rejected without external review.

Each paper is assigned to at least two independent expert reviewers. Reviewers are selected based on subject expertise and conflicts of interest are strictly avoided.

Authors do not know the reviewers' identities, reviewers do not know the authors' identities, and authors must remove identifying information from manuscripts.

Reviewers evaluate originality and novelty, technical and scientific quality, methodological rigor, clarity of presentation, relevance to conference themes, significance of results, and references and literature review quality. Each criterion is scored according to the conference review rubric.

Papers may receive Accept, Accept with Minor Revisions, Accept with Major Revisions, or Reject. The final decision is made by the Program Chair or Technical Program Committee, considering reviewer comments and scores.

Authors receiving revision decisions must submit a revised manuscript and a response-to-reviewers document. Revised papers may undergo re-review, and failure to address reviewer comments may result in rejection.

At least one author of each accepted paper must register and present the paper. Failure to present may result in exclusion from proceedings.

All submitted manuscripts are treated as confidential documents. Reviewers must not share, discuss, or use unpublished material, and review materials are not disclosed outside the review process. A conflict of interest exists when the reviewer and author are from the same institution, have recent collaboration, or have a personal, financial, or professional conflict. Reviewers must declare conflicts and recuse themselves.

Authors must submit original work. Proper citation and acknowledgment are mandatory. Plagiarism, self-plagiarism, and duplicate publication are strictly prohibited, and similarity above the acceptable threshold may result in rejection.

Simultaneous submission to multiple conferences or journals is prohibited. Previously published work cannot be submitted unless it is substantially extended and properly cited.

Data must be accurate and honestly presented. Fabrication, falsification, or manipulation of data is unethical, and authors must retain raw data if requested.

Authorship should be limited to individuals who made significant intellectual contributions, participated in drafting or revising the paper, and approved the final version. Gift authorship and ghost authorship are unethical.

All sources must be appropriately cited. Use of AI tools must be disclosed where applicable.

Reviewers must treat manuscripts as confidential documents and must not use unpublished material for personal advantage.

Reviews must be constructive and unbiased. Personal criticism of authors is inappropriate.

Reviewers must disclose conflicts and decline review if a conflict exists.

If misconduct is suspected, the process includes initial investigation by the Program Chair, author clarification request, review of evidence, and a decision by the Ethics Committee.

Possible actions include immediate rejection, paper withdrawal, notification to the authors' institutions, a ban from future submissions, or removal from proceedings.