Artificial Intelligence – Computer Science (AIC)
Section Information
Artificial Intelligence – Computer Science focuses on the theories, algorithms, models, and systems that enable machines to learn, reason, perceive, interact, and make decisions. It covers foundational AI research as well as applied methods that support automation, data-driven analysis, and intelligent system development across scientific and industrial domains.
Modern research in this field includes machine learning, deep learning, natural language processing, computer vision, robotics, multi-agent systems, human–AI interaction, knowledge representation, optimization, and trustworthy AI. Advances in large-scale computing, data analytics, neural architectures, reinforcement learning, and explainability continue to expand AI capabilities.
This section publishes theoretical contributions, algorithmic developments, computational models, system implementations, empirical studies, and reviews addressing intelligent algorithms, automated reasoning, perception, decision-making, and emerging AI applications in science, engineering, and society.
Scope
- Machine Learning and Statistical Learning
- Supervised, unsupervised, and semi-supervised learning
- Deep learning architectures and representation learning
- Model selection, generalization, and optimization
- Applications in prediction, classification, and pattern discovery
- Natural Language Processing
- Language models, text mining, and semantic analysis
- Machine translation, speech recognition, and conversational AI
- Information extraction, summarization, and sentiment analysis
- Multimodal language–vision systems
- Computer Vision and Image Understanding
- Image recognition, segmentation, and object detection
- Visual tracking, 3D reconstruction, and scene understanding
- Video analytics, gesture recognition, and multimodal perception
- Applications in medical imaging, remote sensing, and robotics
- Robotics, Autonomous Systems, and Control
- Robot perception, motion planning, and navigation
- Autonomous vehicles, drones, and intelligent agents
- Human–robot interaction and collaborative robotics
- Control systems, manipulation, and sensor fusion
- Knowledge Representation and Reasoning
- Ontologies, logic-based reasoning, and knowledge graphs
- Automated planning, scheduling, and decision-making
- Constraint satisfaction and symbolic computation
- Hybrid symbolic–neural models
- Multi-Agent Systems and Distributed AI
- Agent communication, coordination, and cooperation
- Game theory, negotiation, and mechanism design
- Swarm intelligence and collective behavior
- Applications in networks, simulation, and resource allocation
- Explainable, Trustworthy, and Ethical AI
- Model interpretability and explainable AI frameworks
- Robustness, fairness, privacy, and safety in AI systems
- Risk assessment and trustworthy algorithm design
- Ethical implications of AI deployment in society
- AI Systems, Applications, and Emerging Technologies
- AI engineering, scalable architectures, and cloud-based AI
- Applied AI in healthcare, finance, agriculture, and industry
- Edge AI, embedded intelligence, and IoT integration
- AI-driven scientific discovery and computational modeling