International Conference on Edge Computing with Machine Learning

ICECML

26th May – 27th May 2027 Male, Maldives

Official Invitation Letter Available

An official invitation letter will be provided upon successful registration for your participation in the conference.

Benefits of Registering as Listener

Access to All Conference Sessions

Plenary, keynote and parallel sessions

Networking Opportunities

Connect with global educators & researchers

Certificate of Participation

Digital certificate of participation

Invitation Letter Support

Official invitation letter after successful registration

Conference Kit / Digital Materials

E-proceedings & resource materials

Access to Keynote Sessions

Learn from leading experts & scholars

Credit Card

Debit Card

Net Banking

Cash Card

Unified Payments

Select Registration Mode

Standard / Physical Participation USD

Join us on-site for keynotes, talks, workshops, networking, conference kit and certificate.

Virtual Participation USD

Participate online via live streaming. Includes live access to all sessions, digital materials and certificate.

Participant Details

Full Name *

Email Address *

Mobile Number with Country Code *

City *

State *

Country *

Zip / Postal Code *

Address *

Coupon Code

Terms & Condition

I agree to the Terms & Conditions and Refund & Cancellation Policy.

Read the full Terms & Conditions

Registration Summary

Conference ICECML

Date 26th May – 27th May 2027

Location Male, Maldives

Participation Type Listener

Organizer Science Leagues

Registration Mode Standard / Physical

Registration Fee USD 0.00

Bank Convenience Charges (5.8%)i USD 0.00

Calculated based on 5.8% of the registration fee

Discount USD 0.00

Total Payable USD 0.00

100% Secure Payment

Your payment information is encrypted and processed securely.

Conference Session Tracks

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

01 Edge AI Architectures +

This track focuses on the design and implementation of architectures that facilitate edge AI applications. Discussions will include frameworks that optimize resource allocation and enhance computational efficiency at the edge.

SDG 9 SDG 11

02 Real-Time Analytics in Edge Computing +

This session will explore methodologies and technologies that enable real-time data analytics at the edge. Emphasis will be placed on case studies demonstrating the impact of low-latency processing on decision-making.

SDG 9

03 Predictive Modeling Techniques for Edge Devices +

This track delves into innovative predictive modeling techniques tailored for edge computing environments. Participants will discuss the challenges and solutions in deploying these models on resource-constrained devices.

SDG 9 SDG 12

04 IoT Integration with Edge Intelligence +

This session addresses the integration of IoT systems with edge intelligence to enhance data processing capabilities. Topics will include interoperability, data fusion, and the role of edge computing in IoT ecosystems.

SDG 9 SDG 11

05 Supervised and Unsupervised Learning at the Edge +

This track examines the application of supervised and unsupervised learning algorithms in edge computing scenarios. The focus will be on their effectiveness in real-time data processing and analytics.

SDG 9

06 Anomaly Detection in Edge Environments +

This session will cover advanced techniques for anomaly detection specifically designed for edge computing. Participants will share insights on the challenges of detecting anomalies in distributed sensor networks.

SDG 16

07 Deep Learning Applications at the Edge +

This track focuses on the deployment of deep learning models in edge computing contexts. Discussions will include model optimization, compression techniques, and the trade-offs involved in edge deployment.

SDG 9 SDG 11

08 Resource Optimization Strategies for Edge Computing +

This session explores strategies for optimizing resource utilization in edge computing environments. Topics will include load balancing, energy efficiency, and adaptive resource management.

SDG 7 SDG 12

09 Distributed Learning Approaches for Edge AI +

This track investigates distributed learning methodologies that leverage edge computing capabilities. Participants will discuss federated learning and its implications for privacy and data security.

SDG 16

10 Sensor Data Processing Techniques +

This session will focus on innovative techniques for processing sensor data at the edge. Emphasis will be placed on real-time processing, data reduction, and feature extraction methodologies.

SDG 9 SDG 11

11 AI Deployment Strategies at the Edge +

This track examines best practices and strategies for deploying AI solutions in edge computing environments. Discussions will include deployment frameworks, scalability, and performance evaluation.

SDG 9 SDG 12