Important Dates
Early Bird Registration :17th January 2027
Paper Submission Deadline :22nd January 2027
Last Date of Registration : 1st February 2027
Date of Conference : 16th - 17th February 2027
Conference Session Tracks
Aligned with
UN Sustainable Development Goals
This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals.
SDG 4 — Quality Education
SDG 8 — Decent Work and Economic Growth
SDG 9 — Industry, Innovation and Infrastructure
SDG 11 — Sustainable Cities and Communities
SDG 12 — Responsible Consumption and Production
SDG 16 — Peace, Justice and Strong Institutions
SDG 17 — Partnerships for the Goals
Track 01
Advancements in Cloud-Based Machine Learning
This track focuses on the latest innovations in machine learning techniques specifically designed for cloud environments. It aims to explore how cloud infrastructure enhances the scalability and efficiency of machine learning applications.
Track 02
Big Data Analytics in Cloud Computing
This session will delve into the methodologies and technologies for processing and analyzing large datasets in cloud settings. Participants will discuss the challenges and solutions associated with big data analytics in distributed computing environments.
Track 03
Deep Learning Architectures in the Cloud
This track will investigate the implementation of deep learning models within cloud infrastructures. Emphasis will be placed on the optimization of neural network architectures for improved performance and resource utilization.
Track 04
Cloud Security and Machine Learning
This session addresses the intersection of cloud security and machine learning, focusing on techniques to enhance data protection in cloud environments. Discussions will include anomaly detection and threat modeling using machine learning algorithms.
Track 05
Feature Selection and Data Preprocessing Techniques
This track will cover advanced methods for feature selection and data preprocessing in machine learning workflows. Participants will explore how these techniques can improve model accuracy and reduce computational costs in cloud-based applications.
Track 06
Supervised and Unsupervised Learning in Cloud Environments
This session will examine the application of both supervised and unsupervised learning techniques within cloud computing frameworks. The focus will be on practical implementations and case studies demonstrating their effectiveness.
Track 07
Model Optimization and Deployment Strategies
This track will explore best practices for optimizing machine learning models for deployment in cloud settings. Discussions will include resource allocation, performance tuning, and strategies for real-time analytics.
Track 08
Hybrid Cloud Solutions for Machine Learning
This session will investigate the use of hybrid cloud architectures to enhance machine learning capabilities. Emphasis will be placed on the integration of on-premises and cloud resources for improved flexibility and scalability.
Track 09
Cloud AI Services and Their Applications
This track will focus on the various AI services offered by cloud providers and their applications in machine learning. Participants will discuss how these services can accelerate development and deployment of intelligent applications.
Track 10
Real-Time Analytics in Cloud Computing
This session will explore techniques for implementing real-time analytics in cloud environments using machine learning. The focus will be on the challenges and solutions for processing streaming data efficiently.
Track 11
Resource Allocation Strategies for Machine Learning
This track will examine effective resource allocation strategies for optimizing machine learning workloads in cloud infrastructures. Discussions will include dynamic resource management and cost-effective scaling solutions.