Abstract Guidelines

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING CONFERENCE ABSTRACT GUIDELINES

Abstracts should be no longer than one A4 page in portrait layout.
Title: Should be concise, in bold, sentence case, and centered.
Co-authors and Affiliations: Listed below the title in italics. The name of the main author should be underlined, and the presenting author should be marked with an asterisk (*).
Language and Length: The abstract must be written in English, with a maximum character count of 300 words.
Formatting: The main body of the abstract should be written in Times New Roman, font size 12, justified alignment, with 1.5 line spacing.
References: Cite references in superscript numbers within the text. Full references should be listed at the end of the abstract in the following format: YEAR, VOLUME, PAGE, using standard Chemical Abstracts Source Service Index terminology.
Figures, Graphs, and Schemes: The inclusion of figures, graphs, and schemes is encouraged where they aid in the understanding of the abstract.
Submission Format: Abstracts should be submitted in either MS Word or PDF format. Submissions can be made via email attachment to the conference email with the subject line: "Conference Abstract" or through the online submission method via the conference website.
Submission Details: When submitting by email, include the full details of the main author and presenting author, and specify if the presentation will be Oral or Poster.
File Size Limit: Abstracts submitted online should not exceed 1 MB. If your file is larger than this, please submit it via email attachment.
Post-Conference Paper Submission: After the conference, speakers may submit full-length papers for publication in related journals, with an additional fee. Papers will be published within two months of submission.
Confirmation: A confirmation email will be sent upon receiving your abstract. If you do not receive a confirmation within 24 hours, please contact the respective conference

Topics

Artificial Intelligence and Machine Learning: Fundamental Science and MechanismsArtificial Intelligence and Machine Learning: Diagnostics, Biomarkers, and ImagingArtificial Intelligence and Machine Learning: Therapeutics, Devices, and InterventionsArtificial Intelligence and Machine Learning: Clinical Studies, Trials, and Real-World EvidenceArtificial Intelligence and Machine Learning: Prevention, Screening, and Public HealthArtificial Intelligence and Machine Learning: Technology, AI, and Data-Driven MethodsArtificial Intelligence and Machine Learning: Ethics, Equity, and PolicyArtificial Intelligence and Machine Learning: Multidisciplinary and Team-Based CareArtificial Intelligence and Machine Learning: Education, Training, and Workforce DevelopmentArtificial Intelligence and Machine Learning: Global Health and Cross-Border CollaborationArtificial Intelligence and Machine Learning: Quality, Safety, and Implementation ScienceArtificial Intelligence and Machine Learning: Patient and Community EngagementArtificial Intelligence and Machine Learning: Emerging Paradigms and Future DirectionsArtificial Intelligence and Machine Learning: Industry Translation and PartnershipsArtificial Intelligence and Machine Learning: Standards, Guidelines, and Best PracticesArtificial Intelligence and Machine Learning: Comorbidities and Integrated CareArtificial Intelligence and Machine Learning: Environmental and Social DeterminantsArtificial Intelligence and Machine Learning: Novel Methodologies and Protocols

Academic Integrity

We strongly believe that honesty is the most important value in academics. Therefore, plagiarism is not allowed, and any unfair behavior will not be accepted.