Contributed papers are solicited describing original works in Artificial Intelligence, Big Data and Cloud Computing. Topics and technical areas of interest include but are not limited to the following:

Track 1: Foundations of Artificial Intelligence

*AI Theories and Models: Machine learning, deep learning, reinforcement learning, and transfer learning.
*Computational Intelligence: Evolutionary computation, fuzzy logic, Bayesian networks, and swarm intelligence.
*Knowledge Representation: Knowledge graphs, automated reasoning, and expert systems.
*Natural Language Processing: Large Language Models (LLMs), machine translation, sentiment analysis, and question-answering systems.
*Computer Vision: Image/video recognition, object detection, 3D vision, and multimodal understanding.

Track 2: Big Data Technologies and Analytics

*Big Data Frameworks: Distributed data processing (Hadoop, Spark, Flink), stream processing, and real-time analytics.
*Data Mining and Knowledge Discovery: Pattern recognition, association rules, classification, and clustering algorithms.
*Data Management: Database technologies, data integration, data warehousing, and data governance.
*Graph Analytics: Large-scale social network analysis, graph neural networks, and recommendation systems.

Track 3: Cloud Computing and Infrastructure

*Cloud Architectures: Microservices, serverless computing, service-oriented architecture (SOA), and multi-cloud/hybrid cloud strategies .
*Virtualization and Containerization: Docker, Kubernetes, and software-defined networking (SDN).
*Performance and Optimization: Cloud resource scheduling, load balancing, and energy-efficient computing.
*Edge and Fog Computing: Integration of cloud with edge devices for low-latency processing.

Track 4: AI, Big Data, and Cloud Convergence

*AI on the Cloud: MLOps, AI model training/deployment at scale, and cloud-based AI services.
*Big Data for AI: Data-centric AI, data labeling automation, and synthetic data generation.
*Cloud-Native Big Data: Elastic data analytics and cloud-native data lakes/warehouses.
*Intelligent Edge: On-device AI (TinyML) and edge-cloud collaborative intelligence

Track 5: Security, Privacy, and Trust

*Data Security: Encryption, differential privacy, and secure multi-party computation.
*AI Security: Adversarial machine learning, model poisoning, and ethical AI.
*Cloud Security: Identity management, access control, and compliance in cloud environments.
*Blockchain and Distributed Ledger: Secure data sharing and decentralized AI.

Track 6: Applications and Interdisciplinary Research

*Industry 4.0/5.0: Smart manufacturing, digital twins, and predictive maintenance.
*Smart Cities: Intelligent transportation, energy grids, and urban computing.
*Healthcare Informatics: AI for diagnostics, personalized medicine, and health data analytics .
*Business Intelligence: Financial modeling, customer analytics, and decision support systems.
*Emerging Technologies: Quantum machine learning, 6G networks, and green computing.