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ICPABD · Registering as Listener
International Conference on Predictive Analytics and Big Data
3–4 Nov 2026
San Francisco, USA
Standard / Physical
Participation
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$
135
virtual · $195 in person
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Connect with researchers across 30+ countries
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$195
On-site keynotes, talks, workshops, networking, conference kit and certificate.
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$135
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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 →
01
Innovative Approaches in Predictive Analytics
This track focuses on novel methodologies and frameworks in predictive analytics that enhance decision-making processes. Contributions should explore innovative algorithms and their applications in various engineering domains.
02
Machine Learning Techniques for Big Data
This session will delve into advanced machine learning techniques specifically designed for handling large datasets. Papers should highlight the effectiveness of these techniques in extracting meaningful insights from big data.
03
AI-Driven Insights for Engineering Applications
This track invites research on the application of artificial intelligence in generating actionable insights within engineering contexts. Submissions should demonstrate how AI methodologies can optimize engineering processes and outcomes.
04
Data Mining Strategies for Enhanced Decision-Making
This session aims to explore data mining strategies that facilitate improved decision-making in engineering practices. Contributions should present case studies or theoretical advancements that showcase the impact of data mining.
05
Intelligent Systems and Their Impact on Industry
This track examines the role of intelligent systems in transforming industrial processes through big data analytics. Papers should discuss the integration of intelligent systems and their implications for efficiency and innovation.
06
Data Visualization Techniques for Complex Data
This session focuses on innovative data visualization techniques that enhance the interpretation of complex big data. Contributions should demonstrate how effective visualization can lead to better insights and understanding.
07
Forecasting Models in Engineering Applications
This track invites research on the development and application of forecasting models in various engineering fields. Papers should highlight the accuracy and reliability of these models in predicting future trends and behaviors.
08
Data Integration Challenges and Solutions
This session addresses the challenges associated with data integration in big data environments. Contributions should propose solutions that enhance the interoperability and usability of diverse data sources.
09
Optimization Techniques in Data-Driven Systems
This track explores optimization techniques that leverage big data for system performance enhancement. Papers should focus on methodologies that improve efficiency and effectiveness in engineering systems.
10
Innovation Strategies in Data-Driven Decision-Making
This session examines innovative strategies that utilize data-driven decision-making in engineering contexts. Contributions should showcase how these strategies can lead to significant advancements and competitive advantages.
11
Scalable Computing Solutions for Big Data Challenges
This track focuses on scalable computing solutions that address the challenges posed by big data. Papers should discuss architectures and technologies that enable efficient processing and analysis of large datasets.