2026 6th International Conference on Advanced Algorithms and Neural Networks (AANN 2026) will be held on August 7th-9th, 2026 in Qingdao, China.

AANN 2026 is to bring together innovative academics and industrial experts in the field of advanced algorithms and Neural Networks to a common forum. The primary goal of the conference is to promote research and developmental activities in advanced algorithms and Neural Networks. And another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world.The conference will be held every year to make it an ideal platform for people to share views and experiences in advanced algorithms and Neural Networks and related areas.

The conference committee invites submissions of applied or theoretical research as well as of application-oriented papers on all the topics of AANN 2026. Accepted and presented papers of AANN 2026 will be published in international conference proceedings.

The topics of interest for submission include, but are not limited to:

◕Advanced Algorithms

· Reinforcement Learning Algorithms
· Federated Learning Optimization
· Evolutionary Algorithm Improvement
· Swarm Intelligence Optimization
· Bayesian Inference
· Multi-Objective Optimization
· Fuzzy Logic Algorithms
· Quantum-Inspired Algorithms
· Semi-Supervised Learning
· Few-Shot Learning
· Transfer Learning Strategies
· Adversarial Learning
· Constrained Optimization
· Simulated Annealing
· Particle Swarm Optimization
· Ant Colony Optimization
· Differential Evolution
· Anomaly Detection
· Graph Optimization Algorithms
· Time-Series Prediction

◕Neural Networks

· Convolutional Neural Networks
· Recurrent Neural Networks
· Transformer Architecture
· Graph Neural Networks
· Generative Adversarial Networks
· Autoencoders
· Attention Mechanisms
· Deep Residual Networks
· Long Short-Term Memory
· Multimodal Fusion Networks
· Lightweight Neural Networks
· Explainable Neural Networks
· Federated Neural Networks
· Quantum Neural Networks
· Spiking Neural Networks
· Deep Belief Networks
· Attention-Enhanced Networks
· Contrastive Learning Networks
· Multi-Task Neural Networks
· Neural Architecture Search

Publication
All papers will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers will be published by IEEE (ISBN: 979-8-3195-1977-1) and will be submitted to IEEE Xplore, EI Compendex, Scopus for indexing.