The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023.   The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions.  The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

Table of Contents:
​Theory and Algorithms.- Multi-model smart contract vulnerability detection based on BiGRU.- Time-warp-invariant Processing with Multi-spike Learning.- ECOST: Enhanced CoST Framework for Fast and Accurate Time Series Forecasting.- Adaptive CNN-Based Image Compression Model for Improved Remote Desktop Experience.- LCformer Linear Convolutional Decomposed Transformer for Long-Term Series Forecasting.- Fashion Trend Forecasting Based on Multivariate Attention Fusion.- On Searching for Minimal Integer Representation of Undirected Graphs.- Multi-Scale Multi-Step Dependency Graph Neural Network for Multivariate Time Series Forecasting.- Q-Learning Based Adaptive Scheduling Method For Hospital Outpatient Clinics.- Temporal Task Graph based Dynamic Agent Allocation for Applying Behavior Trees in Multi-agent Games.- A Distributed kWTA for Decentralized Auctions.- Leveraging Hierarchical Similarities for Contrastive Clustering.- Reinforcement Learning-Based Consensus-Reaching in Large-Scale Social Networks.- Lateral Interactions Spiking Actor Network for Reinforcement Learning.- An Improved YOLOv5s for Glass Tube Defect Detection.- Label Selection Approach to Learning from Crowds.- New Stability Criteria for Markov Jump Systems under DoS Attacks and Packet Loss via Dynamic Event-triggered Control.- Image Blending Algorithm with Automatic Mask Generation.- Design of Memristor-based Binarized Multi-layer Neural Network with High Robustness.- FEDEL: Frequency Enhanced Decomposition and Expansion Learning for Long-Term Time Series Forecasting.- On the use of persistent homology to control the generalization capacity of a neural network.- On the use of persistent homology to control the generalization capacity of a neural network.- Genetic Programming Symbolic Regression with Simplification-Pruning Operator for Solving Differential Equations.- Explainable Offensive Language Classifier.- Infrared Target Recognition Technology Based On Small Sample Learning.- An Approach to Mongolian Neural Machine Translation Based on RWKV Language Model and Contrastive Learning.- Event-Triggered Constrained $H_\infty$ Control Using Concurrent Learning and ADP.- Three-dimensional rotation knowledge representation learning based on graph context.- TextBFA: Arbitrary Shape Text Detection with Bidirectional Feature Aggregation.- Bias Reduced Methods to Q-learning.- A neural network architecture for accurate 4D vehicle pose estimation from monocular images with uncertainty assesment.- PSO-enabled Federated Learning for Detecting Ships in Supply Chain Management.- Federated Learning using the Particle Swarm Optimization model for the Early Detection of COVID-19.- Domain Generalization via Implicit Domain Augmentation.- Unconstrained Feature Model and Its General Geometric Patterns in Federated Learning: LocalSubspace Minority Collapse.- Mutually Guided Dendritic Neural Models.- Social-CVAE: Pedestrian Trajectory Prediction using Conditional Variational Auto-Encoder.- Distributed Training of Deep Neural Networks: Convergence and Case Study.- Chinese Medical Intent Recognition Based on Multi-feature Fusion.- Multi-Mobile Object Motion Coordination with Reinforcement Learning.- Comparative Analysis of the Linear Regions in ReLU and LeakyReLU Networks.- Heterogeneous Graph Prototypical Networks for Few-shot Node Classification.- Improving Deep Learning Powered Auction Design.- Link Prediction with Simple Path-Aware Graph Neural Networks.- Restore Translation Using Equivariant Neural Networks.- SDPSAT: Syntactic Dependency Parsing Structure-guided Semi-Autoregressive Machine Translation.-