Conference Information

BDNNDL 2026: Symposium on Big Data, Neural Networks, and Deep Learning

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Submission Date:
2026-08-28 Due in 23 days
Notification Date:
2026-08-28
Conference Date:
2026-09-11
Location:
Changji, China
Years:
3
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Call For Papers

BDNNDL 2026 (Symposium on Big Data, Neural Networks, and Deep Learning) is an academic conference held in Changji, China on 2026-09-11. The paper submission deadline is 2026-08-28. Acceptance notifications are sent on 2026-08-28.

The topics of interest for submission include, but are not limited to: ◕ Track 1: Big Data Analysis and Intelligent Computing Big Data Processing and Analysis Data Mining Technology Data Visualization Data-Driven Decision Distributed Computing and Storage Cloud Computing and Big Data Platform High Performance Computing Data Management and Optimization Real-time Data Analysis Intelligent Data System, etc. ◕ Track 2:Computer Vision and Pattern Recognition DImage Processing and Analysis Object Detection and Recognition Image Classification Video Analysis Pattern Recognition Technology Face Recognition and Authentication Medical Image Analysis Intelligent Sensing System 3D Vision and Reconstruction Multimedia Information Processing, etc. ◕ Track 3: Neural Networks and Intelligent Computing Artificial Neural Networks Deep Neural Networks Convolutional Neural Networks Recurrent Neural Networks Graph Neural Networks Reinforcement Learning Model Optimization and Training Intelligent Optimization Algorithms Neural Computing Methods Bio-inspired Computing, etc. ◕ Track 4: Intelligent Systems and Industry Applications Intelligent Manufacturing Smart Healthcare Smart Transportation Smart City Financial Intelligent Analysis Industrial Intelligent System Intelligent Recommendation System Intelligent Monitoring and Prediction Internet of Things and Intelligent Applications Digital Transformation Technology, etc. ◕ Track 5:Deep Learning and Intelligent Algorithm Deep Learning Methods Representation Learning Transfer Learning Federated Learning Generative Artificial Intelligence Multimodal Learning Intelligent Prediction and Classification Feature Learning and Extraction Model Compression and Acceleratio, etc.. ◕ Track 6: Data Security and Trustworthy Intelligence Data Security and Privacy Protection Network Security Intelligent System Security Trustworthy Artificial Intelligence Data Governance Explainable Artificial Intelligence Blockchain and Data Trust Robust Learning and Adversarial Techniques Privacy Computing Intelligent Risk Assessment, etc. l Other related topics
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