Workshop on Cognitive Assistants and Data Fusion (CogniBlend)

Scope

CogniBlend is a workshop that will bring together experts from academia to discuss the latest advances in intelligent systems. Topics to be covered include, among others, Federated Learning, Robotics in Healthcare, Precision Agriculture, Classification using advanced image analysis techniques, and the emerging concept of Digital Twins.

Participants will gain insights into the challenges, opportunities, and realworld applications of these technologies. The workshop will also address Perceptual Comparison, Uncertainty, and Aggregation Theory, providing a comprehensive perspective on cutting-edge advancements in learning and decision-making frameworks.

Join us at CogniBlend to explore the landscape of intelligent systems and their practical implications in various sectors.

Key Themes

• Cognitive Assistants: Explore the frontiers of AI-powered cognitive assistants designed to augment human capabilities, enhance decision-making processes, and seamlessly integrate with various domains. From healthcare to smart environments, delve into the applications reshaping the way we interact with intelligent systems. 
• Data Fusion: Examine the details of data fusion, where diverse data sources converge to improve understanding of complex scenarios. The discussions will cover techniques, challenges, and breakthroughs in integrating and interpreting information, acknowledging inherent uncertainty.

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Topics

Possible areas of relevance for the workshop encompass not only specific applications but also theoretical approaches grounded in, though not restricted to:

• Federated Learning and Privacy-preserving machine learning
• Digital Twins and Applications
• Perceptual Comparison and computational models of perception
• Robotics and Healthcare
• Cognitive Assistant
• Precision Agriculture
• Uncertainty and Aggregation Theory (Handling uncertainty in decisionmaking)
• Applications in Image, Classification and Explainable AI
• Swarm Intelligence and Collective decision-making
• Applications in disaster response and environmental monitoring
• Reinforcement Learning in Multi-Agent Environments
• Experiences and Case Studies in Real-world implementations