HYBRID EVENT: You Can Participate In Person At Melbourne, Australia Or Virtually From Your Home Or Work
ICMLMDA · Registering as Listener

International Conference on Machine Learning Models for Data Analytics

11–12 Nov 2026 Melbourne, Australia Standard / Physical Participation
Listener Registration
$135
virtual · $195 in person
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1. Policy on Cancellations & Refunds :

  • A full refund is possible only if the cancellation request is submitted at least 70 days before the conference, with formal paperwork completed at least 60 days prior.
  • If cancellation occurs between 60 and 30 days before the event, partial refunds may be granted based on administrative costs incurred.
  • Cancellations made within 30 days of the event are non-refundable, though participants will receive a credit valid for one year.
  • Registrations made less than 30 days before the event are not eligible for refunds, but may be transferred to another ISIT conference.
  • Since this conference is hybrid format, the organizer reserves the right to conduct the event either in-person or virtually. Please note that no refunds will be issued due to changes in the event format.
  • Virtual registration fees are non-refundable. A credit will instead be issued for use at a future conference event.

2. Participation & Registration Requirements :

  • To attend an ISIT event, participants must complete the registration process within the stipulated time.
  • The event schedule, venue, and format are subject to changes at the discretion of the organizers, with prior notice sent via email.
  • ISIT is not liable for any financial losses resulting from changes in event details.
  • Fees paid for registration are strictly non-refundable.
  • If the primary author cannot participate, a co-author may attend instead, but refunds will not be granted for non-attendance.

3. Submission & Publication Norms :

  • Researchers submitting papers to ISIT must ensure their institution or supervisor is aware of their submission.
  • Each submission is subject to a rigorous peer review before being accepted for presentation.
  • Only papers linked to a completed registration will be included in conference proceedings.
  • The submitting author is considered the primary author; ISIT does not verify individual authorship claims.
  • If any concerns regarding authorship arise and are validated, the paper will be withdrawn without reconsideration.
  • Once a paper is removed, it cannot be reintroduced into any ISIT publication.

4. Travel & Accommodation Responsibilities :

  • Attendees are responsible for making their own travel and lodging arrangements.
  • ISIT does not provide logistical assistance for travel or accommodation.
  • The organization bears no responsibility for expenses incurred due to conference modifications or rescheduling.

5. Visa & Invitation Letter Policy :

  • Attendees are responsible for making their own travel and lodging arrangements.
  • ISIT does not provide logistical assistance for travel or accommodation.
  • The organization bears no responsibility for expenses incurred due to conference modifications or rescheduling.
  • ISIT does not engage directly with consulates or embassies on behalf of attendees.
  • Participants must handle their own visa applications and processes.
  • Important Information Regarding Invitation Letters:
    • The invitation letter is provided solely for conference attendance and does not serve as a document for immigration, employment, or residency purposes.
    • The letter assists in visa applications but does not guarantee visa approval.
    • ISIT holds no responsibility for visa denials or processing delays, and all related costs are borne by the applicant.
    • Any alterations or unauthorized use of the invitation letter will result in its invalidation and possible cancellation of conference participation.
    • Legal action may be pursued if the document is misused.
    • By accepting the invitation letter, attendees agree to comply with international travel regulations and ethical participation standards.

6. Registration Transfers :

  • Registrations may be transferred to another individual from the same institution if the original participant cannot attend.
  • Transfer requests must be made via email to [email protected] with necessary details and supporting documents.
  • Transfers must be requested at least 14 days before the event; otherwise, they will not be accommodated.
  • Transferred registrations are not eligible for refunds.

7. General Considerations :

  • Any modifications or cancellations must be communicated in writing to [email protected].
  • Registration confirms acknowledgment and acceptance of these policies.
  • ISIT does not initiate automatic transactions; all payments are voluntarily completed by registrants.
  • Once registered, participants must submit a conference registration form within three days for confirmation.
  • Travel plans should only be finalized after receiving the official conference itinerary, which will be shared 15 days before the event.

8. Conference Programme and Participation Policy :

  • Presentations from different but relevant academic areas may be combined by the Organiser within an interdisciplinary or multidisciplinary programme to encourage cross-disciplinary understanding, interaction, and future research partnerships.
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What you'll hear about

Session tracks covered across the event.
View all 11 tracks →
This track focuses on the latest advancements in machine learning techniques for predictive modeling in business contexts. Researchers are invited to present their findings on innovative algorithms and methodologies that enhance predictive accuracy and decision-making.
This session will explore the application of neural networks and deep learning in various business scenarios. Contributions should highlight case studies and novel approaches that demonstrate the effectiveness of these technologies in data analytics.
This track emphasizes the importance of feature engineering in extracting meaningful insights from complex datasets. Participants are encouraged to share techniques and frameworks that improve model performance through effective feature selection and transformation.
This session aims to discuss the integration of artificial intelligence in decision support systems within business environments. Papers should focus on AI-driven methodologies that facilitate informed decision-making and strategic planning.
This track addresses the challenges and opportunities presented by big data in the realm of business intelligence. Researchers are invited to present innovative solutions and frameworks that leverage big data analytics for enhanced organizational performance.
This session will delve into statistical analysis methods and model optimization techniques that improve data analytics outcomes. Contributions should focus on quantitative approaches that enhance model reliability and efficiency.
This track explores the role of pattern recognition in identifying trends and anomalies in business data. Participants are encouraged to present research that demonstrates the application of pattern recognition techniques in various sectors.
This session will investigate the intersection of artificial intelligence and cognitive computing in business analytics. Papers should highlight innovative applications that enhance cognitive capabilities and support complex decision-making processes.
This track focuses on the application of reinforcement learning techniques in developing effective business strategies. Researchers are invited to share insights on how reinforcement learning can optimize decision-making and operational efficiency.
This session will cover the role of automation and data mining in extracting actionable insights from large datasets. Contributions should focus on methodologies that streamline data mining processes and enhance analytical capabilities.
This track emphasizes the significance of data visualization in communicating complex analytical results. Participants are encouraged to present innovative visualization techniques that facilitate better understanding and interpretation of data analytics findings.