WorldCompAI – World Congress on Computing, AI, and Machine Learning [WorldCompAI 2025]
About Conference:
The WorldCompAI – World Congress on Computing, AI, and Machine Learning is a prestigious international forum dedicated to bringing together researchers, innovators, industry leaders, and academics from around the globe to explore the latest advancements and emerging trends in computing technologies, artificial intelligence, and machine learning. Scheduled for 2025, this congress serves as a dynamic platform to showcase groundbreaking research, real-world applications, and transformative ideas that are shaping the future of intelligent systems. WorldCompAI promotes interdisciplinary collaboration across AI subfields, computing systems, data science, robotics, quantum computing, cybersecurity, and more—driving innovation that addresses global challenges and opportunities. With a focus on cutting-edge topics such as generative AI, large language models, edge intelligence, ethical AI, and sustainable technology, WorldCompAI 2025 offers an unparalleled opportunity for participants to connect, share insights, and contribute to the evolution of next-generation digital technologies.
Scope:
The scope of WorldCompAI 2025 encompasses the latest advancements and interdisciplinary innovations in computing, artificial intelligence, and machine learning. The conference welcomes original contributions across a wide spectrum of topics, including deep learning, large language models, generative AI, computer vision, natural language processing, data science, and intelligent systems. It also emphasizes emerging areas such as edge AI, quantum computing, ethical AI, and AI for sustainability. With a strong focus on bridging academic research and real-world applications, WorldCompAI provides a global platform for collaboration among researchers, industry professionals, and policymakers, fostering transformative solutions to current and future technological challenges.
Topics:
Researchers are encouraged to submit papers on topics including, but not limited to:
Large Language Models & Foundation Models
- GPT, Gemini, Claude, and open-source LLMs
- Prompt engineering, fine-tuning, and deployment
- Multimodal AI systems (text, image, video integration)
Generative AI
- Text-to-image/video generation (e.g., DALL·E, Runway)
- Generative Adversarial Networks (GANs)
- AI-generated content verification and ethics
Cloud Computing & IoT
- Serverless Computing & FaaS.
- Cloud-native Machine Learning Platforms.
- Federated IoT and Fog Computing.
- Digital Twin Integration with IoT.
- Secure Multi-Cloud Environments.
Trustworthy, Explainable & Ethical AI
- Explainable AI (XAI) and interpretability
- Fairness, accountability, and transparency
- Bias mitigation and global AI regulation (e.g., EU AI Act)
Edge AI & TinyML
- AI on microcontrollers and edge devices
- Federated learning for distributed intelligence
- Real-time, privacy-preserving inference
AI for Sustainability & Climate Action
- Smart agriculture, water systems, and energy grids
- AI for environmental forecasting and climate modeling
- Carbon-aware and energy-efficient AI systems
AI in Industry 5.0 & Smart Automation
- Human-AI collaboration in manufacturing
- Robotics integration with deep learning
- Digital twins and predictive maintenance
AI for Cybersecurity
- Anomaly detection and threat prediction
- AI-powered fraud detection
- Adversarial learning and secure AI systems
Quantum Machine Learning (QML)
- Variational quantum circuits and QNNs
- Quantum-enhanced optimization and classification
- Noise-resilient quantum algorithms
Neurosymbolic AI & Hybrid Intelligence
- Merging logic and neural networks
- Causal AI and reasoning-driven learning
- Cognitive AI architectures
Indexed By:
