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ICEAIE · Registering as Listener

International Conference on Explainable AI in Engineering

5th Feb – 6th Feb 2027 Budapest, Hungary Standard / Physical Participation
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$180
standard · $180
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Terms & Conditions

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.

➤ If a participant is unable to attend the conference due to personal reasons and cancels within 30 days of the event, the registration fee is non-refundable. However, the paid fee will be retained as a credit, allowing participation in any of EFSTM’s international conferences within one year from the date of registration.

➤ Registrations made less than 30 days before the event are not eligible for refunds, but may be transferred to another EFSTM conference.

➤ If the conference format is changed from in-person to virtual, refunds will not be provided.

➤ A basic cancellation charge of 50 USD will be deducted from all refunds.

➤ Virtual registration refunds are not applicable, but credit will be issued for future conferences.

➤ If the organizer cancels the event, 100% credit will be issued for future conferences within one year. The registrant must provide the next conference details within 6 months.


2. Participation & Registration Requirements:

➤ To attend an EFSTM event, participants must complete the registration process within the stipulated time.

➤ If cancellation occurs between 60 and 30 days before the event, partial refunds may be granted based on administrative costs incurred.

➤ The event schedule, venue, and format are subject to changes at the discretion of the organizers, with prior notice sent via email.

➤ EFSTM 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.

➤ Registrations with concessions are not refundable.

➤ Registrants must contact the team for updates after registration. Failure to do so may invalidate registration.

➤ Failure to respond to communications within 15 days or submit the registration form within 7 days will invalidate registration.


3. Submission & Publication Norms:

➤ Researchers submitting papers to EFSTM 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; EFSTM 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 EFSTM publication.

➤ All Authors and Co-authors must inform their respective Dept. Head/Principal/Guide about the paper submission.

➤ All papers will undergo review by two internal or external reviewers.

➤ Only registered papers will be published.

➤ Suspended papers will not be republished or distributed.


4. Travel & Accommodation Responsibilities:

➤ Attendees are responsible for making their own travel and lodging arrangements.

➤ EFSTM does not provide logistical assistance for travel or accommodation.

➤ The organization bears no responsibility for expenses incurred due to conference modifications or rescheduling.

➤ No refunds will be provided due to travel or accommodation unavailability.

➤ Registration fees do not include travel or accommodation.


5. Visa & Invitation Letter Policy:

➤ EFSTM does not engage directly with consulates or embassies on behalf of attendees.

➤ Participants must handle their own visa applications and processes.

➤ 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.

➤ EFSTM 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.

➤ If the delegate receives the conference invitation letter but cannot attend the conference, no refund will be provided.

➤ Registrations made near or after the registration deadline will be considered for virtual participation, with eligibility for a future conference.


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.

➤ If the invitation letter is received, transfer must be completed within 7 days or 20 days prior to the conference.

➤ If there is a change in the conference, the registrant must inform the team at least 25 days prior to the conference date.

➤ Fully paid registrations are transferable to others from the same organization.

➤ Registration can be transferred to another conference within one year.


7. General Considerations:

➤ Any modifications or cancellations must be communicated in writing to [email protected]

➤ Registration confirms acknowledgment and acceptance of these policies.

➤ EFSTM 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:

➤ To facilitate a diverse and coherent programme, the Organiser may integrate related research subjects across disciplines into interdisciplinary or multidisciplinary sessions focused on wider learning, academic discussion, and research networking.

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Registration summary

ConferenceICEAIE
ModeStandard / Physical
ParticipationListener
Registration fee$180.00
Bank charges (5.8%)$10.44
Total payable $190.44
Includes all bank processing charges — the amount above is exactly what will be charged.

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Benefits of Registering as Listener

Access to Conference Sessions
Networking Opportunities
Certificate of Participation
Invitation Letter Support
Conference Kit / Materials
Access to Keynote Sessions
• CONFERENCE SESSION TRACKS •
EFSTM conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.
SDG Wheel

SDG-Aligned Research Themes

This conference aligns its research sessions with the United Nations Sustainable Development Goals.

SDG 4 SDG 9 SDG 11 SDG 12 SDG 16 SDG 17
01 Advancements in Explainable AI for Predictive Maintenance

This track focuses on the integration of explainable AI techniques in predictive maintenance applications within engineering contexts. Participants will explore methodologies that enhance model interpretability and transparency in maintenance decision-making processes.

02 Interpretable Models in Supervised Learning

This session will delve into the development and application of interpretable models in supervised learning frameworks. Researchers will present novel approaches that balance model accuracy with the need for transparency and understanding.

03 Unsupervised Learning and Anomaly Detection

This track addresses the challenges and innovations in unsupervised learning techniques for anomaly detection in engineering systems. Discussions will center on the interpretability of models and their practical implications in real-world scenarios.

04 Feature Importance and Model Evaluation Techniques

This session will explore various methods for assessing feature importance in machine learning models. Participants will discuss the implications of these techniques on model evaluation and their role in enhancing explainability.

05 Deep Learning Interpretability Frameworks

This track focuses on the latest frameworks and methodologies developed to enhance the interpretability of deep learning models. Researchers will share insights on bridging the gap between complex model architectures and human comprehension.

06 Human-in-the-Loop AI Systems

This session will investigate the role of human-in-the-loop approaches in the development of explainable AI systems. Emphasis will be placed on how human feedback can improve model transparency and trustworthiness.

07 Explainable AI in Industrial IoT Applications

This track will examine the application of explainable AI methodologies in the context of industrial IoT. Participants will discuss case studies that highlight the importance of model interpretability in enhancing operational efficiency and safety.

08 Decision Support Systems Leveraging Explainable AI

This session will explore the integration of explainable AI in decision support systems across various engineering domains. The focus will be on how interpretability can enhance user trust and facilitate better decision-making.

09 Challenges in AI Trustworthiness and Transparency

This track will address the critical challenges surrounding AI trustworthiness and model transparency in engineering applications. Participants will engage in discussions about ethical considerations and the societal implications of AI deployment.

10 Feature Extraction Techniques for Explainable Models

This session will focus on innovative feature extraction techniques that enhance the interpretability of machine learning models. Researchers will present their findings on how effective feature selection contributes to model clarity and performance.

11 Case Studies in Explainable Predictive Modeling

This track will showcase case studies that highlight the practical applications of explainable predictive modeling in engineering. Participants will analyze real-world examples where interpretability has led to improved outcomes and insights.

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