The International Conference on Collaborative Technologies and Data Science in Smart City Applications (CODASSCA 2026), co-sponsored by IEEE, invites the submission of original research papers addressing key challenges in the design, analysis and deployment of smart and sustainable IoT systems. Contributions are expected to encompass novel methodologies and system-level solutions tailored to smart environments. The conference serves as a focused academic forum for in-depth and constructive discourse on cutting-edge technologies and system architectures supporting intelligent, secure and human-centered IoT ecosystems.

Accepted and presented papers will be submitted for inclusion in the IEEE Xplore® Digital Library.

The Conference will be held at the American University of Armenia (AUA), from September 7-9, Yerevan, Armenia. 

Topics of interest include, but are not limited to, the following:

Collaborative Technologies and Smart Applications: The theme focuses on collaborative systems and platforms for intelligent interaction, coordination and shared decision-making in smart cities and digital twin-enabled systems:

• Collaborative mobile information systems
• Digital twins for collaborative museums and laboratories and lifecycle management
• Collaborative mobile virtual environments for learning and knowledge management
• Collaborative visual analytics for shared situational awareness
• Collaborative technologies for smart city applications.

Data Science and Information-Theoretic Approaches for Smart Systems: The theme emphasizes analytical foundations for smart systems including data-driven intelligence, optimization and modeling of security and data privacy:

• Security and privacy modeling from data science perspectives
• Optimization and performance analysis of smart systems using theoretical and data-driven methods
• Security and privacy modeling from information-theoretic perspectives.

Technical Challenges for Smart Environments: This field invites research papers addressing system-level challenges in Ambient Intelligence (AmI) and Smart Environments (SmE) focusing on reliability, scalability and system accountability:

• Internet of Things technologies for smart environments
• Recommender systems for AmI and SmE applications
• Middleware architectures with emphasis on security and privacy
• Reliable and explainable visual analytics in AmI and SmE systems.

Artificial Neural Networks and Deep Learning: The emphasis is placed on advances in AI and machine learning techniques that support intelligent perception, reasoning and decision-making in IoT-enabled systems:

• Language models, information theory, and deep learning
• Embedded and resource-efficient deep learning
• AI and machine learning methods for IoT systems
• Applications in language, vision, and multimodal processing
• Neural network models and advanced architectures.

Smart Human-Centered Computing: This theme focuses on IoT and AI systems backed on human needs, behavior and societal impact emphasizing usability, personalization and well-being:

• Human-centered AI and machine learning
• Social computing and collective intelligence
• Intelligent recommender systems
• Ambient assisted living systems
• E-health and digital healthcare solutions
• Geographic information systems (GIS) and location-aware applications.

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