Contributed papers are solicited describing original works in Artificial Intelligence, Cloud Computing and Distributed Systems. Topics and technical areas of interest include but are not limited to the following:
Track 1: Artificial Intelligence and Machine Learning Foundations
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Deep learning, reinforcement learning, and transfer learning
Generative AI, large language models (LLMs), and diffusion models
Computer vision, natural language processing, and speech processing
Explainable, fair, and trustworthy AI
Probabilistic models, Bayesian inference, and causal inference
Optimization algorithms for machine learning
Representation learning and self-supervised learning
AI theory, computational complexity, and learning theory
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Track 2: Cloud Computing and Distributed Systems |
Cloud architectures: IaaS, PaaS, SaaS, serverless, and cloud-native systems
Virtualization, containers, Docker, Kubernetes, and orchestration
Edge, fog, multi-cloud, and hybrid cloud computing
Distributed systems: consistency, replication, consensus, and fault tolerance
Resource management, scheduling, load balancing, and elasticity
Scalability, resilience, availability, and performance modeling
Distributed storage, file systems, and data management
High-performance and parallel computing in cloud environments
Cloud security, privacy, and trust
Benchmarking and evaluation of cloud and distributed systems
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Track 3: AI for Cloud and Distributed Systems |
Federated learning, decentralized training, and split learning
Distributed optimization and parallel algorithms for machine learning
Scalable training of large models in cloud and distributed environments
Cloud-based AI services, MLOps, model serving, and workflow orchestration
Edge AI, distributed inference, and on-device intelligence
Multi-agent systems and distributed reinforcement learning
Privacy-preserving and secure distributed learning
Communication-efficient distributed AI
Serverless computing for AI workloads
AI model deployment in multi-cloud and hybrid cloud environments
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Track 4: AI-Enhanced Cloud and Distributed Systems |
AI-driven resource management and scheduling in cloud systems
Machine learning for fault detection, diagnosis, and self-healing
AIOps, intelligent operations, and automation
AI for network optimization, traffic engineering, and load balancing
AutoML and neural architecture search for cloud/distributed environments
AI for cloud/edge orchestration and service placement
Reinforcement learning for adaptive system control
AI-based performance prediction and anomaly detection
Intelligent data placement and caching in distributed storage
Trustworthy AI for cloud and distributed systems
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Track 5: Applications and Emerging Technologies |
AI, cloud, and distributed systems for healthcare, finance, and smart cities
Large-scale data analytics and stream processing
Blockchain, distributed ledger, and decentralized AI
Quantum computing and quantum-assisted machine learning in cloud
Serverless computing and cloud-native applications
Green cloud and energy-efficient distributed systems
Digital twins, simulation, and high-fidelity modeling
Internet of Things (IoT) and cyber-physical systems
Autonomous systems and robotics with cloud/distributed intelligence
Edge-cloud continuum and next-generation network applications
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