Call for Papers

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A sustainable society is driven by the principle of realizing peace and prosperity for all people and the planet with an inclusiveness that “leaves no one behind.” This conference takes paper submissions on research and development of ML techniques in the context of sustainable societies. International conference on machine learning for society can encompass a wide range of topics and tracks that address the societal impact and applications of machine learning Topics may include, but are not limited to.

Publication and Indexing:

It is planned to publish the proceedings with Springer series (final approval pending)

Track 1 : Machine Learning Algorithms and Models:

• Novel machine learning algorithms

• Deep learning architectures

• Reinforcement learning

• Transfer learning

• Explainable AI

Track 2 : Security in Machine Learning:

• Adversarial machine learning

• Privacy-preserving machine learning

• Robustness and reliability of machine learning models

• Threat detection and mitigation in ML systems

• Secure federated learning

Track 3: Cloud Computing and Machine Learning Integration:

• Cloud-based machine learning platforms

• Scalability and performance in cloud-based ML

• Serverless computing for ML applications

• Edge computing and machine learning

• Hybrid cloud architectures for ML

Track 4 : Cybersecurity and Threat Intelligence:

• Cybersecurity analytics using machine learning

• Threat detection and response

• Anomaly detection in security logs

• Machine learning for malware detection

• Security in IoT and connected devices

Track 5 : Privacy and Ethical Considerations:

• Ethical issues in machine learning

• Fairness and bias in ML algorithms

• Responsible AI practices

• Legal and regulatory aspects of ML and security

• Privacy-preserving techniques in ML

Track 6: Secure Cloud Architectures:

• Cloud security best practices

• Identity and access management in the cloud

• Encryption and data protection in the cloud

• Compliance and auditing in cloud environments

• Security automation and orchestration in the cloud

Track 7: Case Studies and Applications:

• Real-world applications of machine learning in security

• Cloud-based solutions for specific industries

• Success stories and challenges in implementing ML for security

• Industry-specific use cases in the intersection of ML, security, and cloud computing