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About ICDA 2027
The 4th International Conference on Intelligent Computing and Data Analysis (ICDA 2027) will be held in Shenzhen, China, from August 20 to 22, 2027. With the theme of "Intelligent Computing Drives, Data Empowers Innovation", the conference focuses on intelligent computing and data analysis, promotes cutting-edge interdisciplinary research and industrial integration of artificial intelligence and data science, and aims to build a global collaborative platform for academia, research institutes and industry.
The conference will bring together distinguished scholars, industry experts and technical researchers in the fields of intelligent computing, big data analysis, machine learning and industrial intelligence. Through keynote speeches, thematic seminars, technical demonstrations, and academia-industry dialogue sessions, participants will discuss theoretical innovations, technical routes and implementation schemes for next-generation intelligent systems. Focusing on frontier directions including large model optimization, edge intelligence, trustworthy artificial intelligence and scientific computing, the conference fosters cross-disciplinary idea exchanges and joint development of technical solutions, so as to facilitate the continuous iteration of intelligent computing and data analysis technologies and empower industrial development.
We sincerely invite experts, scholars, research teams and industrial practitioners from universities, research institutes, high-tech enterprises and relevant organizations worldwide to join us in Shenzhen and explore new trends of intelligent computing and data analysis in the intelligent era.
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Topics
The topics of interest for submission include, but are not limited to:
Data Analysis and Visualization Technologies
Data Mining
Knowledge Discovery
Data Analysis Methods
Process Optimization
Predictive Modeling
High-Frequency Data Analysis
Time Series
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Big Data Technologies and Platform Applications
Big Data Platforms
System Architecture
Big Data Storage
High-Performance Computing
Data Fusion
Data Cleaning
Big Data Visualization
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Machine Learning and Intelligent Algorithms
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Deep Learning
Neural Network Optimization
Reinforcement Learning
Adaptive Intelligent Systems
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Intelligent Systems and Applied Modeling
Intelligent Perception
Pattern Recognition
Intelligent Control Systems
Model Predictive Control
Multi-Agent Systems
Distributed Intelligence
Decision Support Systems
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Computational Mathematics and Scientific Computing
Numerical Computation
Optimization Algorithms
Parallel Computing
Distributed Computing
Scientific Computing Software
Large-Scale Simulation
Graph Algorithms
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Interdisciplinary and Frontier Technologies of Intelligent Computing
Quantum Computing
Quantum Machine Learning
Biological Computing
Neuromorphic Systems
Natural Language Processing
Language Intelligence
Computer Vision
……