Conference Tracks
ICCI 2027 brings together researchers advancing intelligent and data-driven computing, from computational methods and knowledge discovery to intelligent systems and their security, reliability, and trustworthiness.
Scientific Scope
The conference focuses on original, technically substantive contributions to the methods, algorithms, architectures, and evaluation of intelligent and data-driven computing systems. Its four tracks form a connected technical program spanning computational foundations, data-centric methods, system design, and trustworthy operation.
Authors should select the track that best matches their paper's principal scientific contribution. Topics below indicate the intended scope rather than an unrestricted invitation to all areas of computer science.
Track 1: Computational Intelligence and Machine Learning
Novel computational models and learning methods for intelligent inference, prediction, perception, and decision-making.
Learning Methods and Models
- Machine learning and deep learning methods
- Representation, self-supervised, and transfer learning
- Generative and foundation models
- Reinforcement and continual learning
- Learning under limited or noisy data
Computational Intelligence and Perception
- Evolutionary and swarm-based optimization
- Neuro-symbolic and hybrid intelligent methods
- Computer vision and pattern recognition
- Natural language and multimodal learning
- Intelligent agents and decision algorithms
Track 2: Data Science and Knowledge Discovery
Computational techniques for extracting reliable knowledge from complex data and supporting data-driven inference and decisions.
Data-Centric Computing
- Data mining and knowledge discovery algorithms
- Scalable data processing for intelligent analytics
- Graph learning and knowledge representation
- Data quality, integration, and representation for learning
- Streaming and evolving data analytics
Computational Analytics
- Predictive and prescriptive analytics methods
- Time-series and spatiotemporal modeling
- Uncertainty-aware data analysis
- Information retrieval and semantic analytics
- Evaluation and reproducibility of data-driven methods
Track 3: Intelligent Software and Computing Systems
Architectures, algorithms, and engineering methods for building and evaluating computing systems with substantive intelligent or data-driven capabilities.
Intelligent System Architectures
- Edge and distributed AI systems
- Resource-aware model deployment and inference
- Adaptive and self-managing software systems
- Intelligent orchestration and scheduling
- AI-enabled cyber-physical systems
System Design and Evaluation
- Software architectures for data-driven systems
- Performance and energy optimization of AI workloads
- Intelligent sensing and IoT analytics
- Data-driven digital twins and autonomous systems
- Testing and verification of intelligent software
Track 4: Trustworthy and Secure Intelligent Computing
Methods and mechanisms that improve the security, privacy, reliability, transparency, and robustness of intelligent and data-driven computing.
Trustworthy Learning and Inference
- Explainable and interpretable AI methods
- Robustness and adversarial machine learning
- Fairness, accountability, and model auditing
- Uncertainty estimation and calibration
- Reliability and safety evaluation of AI systems
Privacy and Security of Intelligent Systems
- Privacy-preserving and federated learning
- Security of machine-learning pipelines and models
- AI-driven threat detection and response methods
- Secure deployment of intelligent edge systems
- Trustworthy data sharing for intelligent analytics
Application-Oriented Contributions
Application-oriented research in healthcare, agriculture, industry, transportation, and smart environments is welcome when it demonstrates a substantive contribution to intelligent or data-driven computing. Submissions should present rigorous technical evaluation and findings that extend beyond the specific application domain.