Neural Information Processing 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part IV

The eleven-volume set LNCS 15286-15296 constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024.
The 318 regular papers presented in the proceedings set were carefully reviewed and selected from 1301 submissions. They focus on four main areas, namely: theory and algorithms; cognitive neurosciences; human-centered computing; and applications.

Multimodal Emotion Recognition by Fusing Video Semantics in Video Learning Scenarios.- STGCN-DHD: Spatio-Temporal Graph Convolutional Network for EEG-Based Driving Hazard Detection.- Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis.- Granular-ball Representation Learning for Deep CNN on Learning with Label Noise.- Computational Intelligence for Optimizing UAV Positioning and Task Scheduling in UAV-assisted MEC Systems.- Feed-Forward Optimization With Delayed Feedback for Neural Network Training.- Fusion of Multi-level Information: Solve Large-scale Traveling Salesman Problem with an Efficient Framework.- A Novel Elitism-Based Genetic Algorithm with Gradient-based Local Search for Seeking Local Nash Equilibrium in Non-Cooperative Game.- Massive Multi-Agent Mean-Field Game Using Online Federated Adaptive Critic-Density Learning.- Combining Explicit Priors and Set Attention Driven Implicit Priors for Demonstration-Guided Reinforcement Learning.- Mixed Time-State Dependent Distributed Event-Triggered Consensus Protocol of a DC Microgrids Cluster.- Computing Approximate Nash Equilibrium in Two-Team Zero-Sum Games by NashConv Descent.- Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory.- GATE: Guided Contrastive State Space for Multi-Agent Reinforcement Learning.- A Leaky Wang kWTA.- Semantic Mapping and Reconstruction from Brain Activation to Natural Images Using LDM and LLM.- Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging.- Critical roles of Contours in Intermediate-Level Neural Representation: Comparative study between Primate V4 and DCNN.- Generalized knowledge-enhanced framework for biomedical entity and relation extraction.- CPG: Channel Pruning with DFS Guided Grouping for Efficient Medical Image Segmentation.- Attention based multi-scale feature conservation network for medical image segmentation.- DSNet: A Decoupled Siamese Network for ECG Classification.- A Lightweight Multi-Scale Efficient Model for Breast Cancer Detection and Classification in Mammograms.- HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affnity Prediction.- Graph-Augmented Sparse Attention for Medical Image Segmentation.- Active Learning by Feature Perturbation for Medical Image Classification.- A Multi-Encoder Pyramid U-Net for Multimodal Brain Tumor Segmentation.- MambaFuse: Fusing Multi-Scale Mamba and CNN Features for Seizure Prediction.- An Encoder-Decoder Based Approach for ECG Delineation.