Mahmud / Doborjeh / Wong Neural Information Processing
Erscheinungsjahr 2025
ISBN: 978-981-966588-4
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part V
E-Book, Englisch, 481 Seiten
Reihe: Lecture Notes in Computer Science
ISBN: 978-981-966588-4
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
cPER2P: Parameter-Efficient Single-cell LLM for Translated Proteome Profiles.- CRAFT: Consistent Representational Fusion of Three Molecular Modalities.- AMPCL: Adaptive Meta-Path Selection and Contrastive Learning for miRNA-Disease Prediction.- Video-Driven Comprehensive 3D Hip Joint Motion Model for FAI Auxiliary Diagnosis.- LungCANet: A Novel Deep Co-Attention Convolutional Neural Network Architecture for High-Precision Lung Cancer Morphological Analysis and Classification.- ATFN: An Efficient Multi-Modal Depression Assistance Diagnostic Model Based on Multi-Channel Attention Mechanism.- Domain Knowledge Based Temporal-spatial Graph Convolution Network for ECG Recognition.- Adaptive Constrained ICABMGGMM: application to ECG blind source separation.- CRA-Eformer: Cross-scale Residual Attention-based Edge-guide Transformer for Low-Dose CT Denoising.- Improving Text Representation for Disease Detection From Social Media via Self-augmentation and Contrastive Learning.- Improving Healthcare Outcomes by Identifying Populations with Higher Risk of Lung Cancer from Primary Care Data.- Split Learning on Multi-source Cross-streams.- Seizure Prediction based on Multi-scale Fusion-attention Transformer.- Dynamic Self-Attention Gated Spatial-Temporal Graph Convolutional Network for Skeleton-based Human Activity Recognition.- G-SwinHAR: Swin Transformer for Smartphone-Based Human Activity Recognition Using Gramian Angular Field.- Cross-feature Interactive Fusion for Speech Emotion Recognition.- Temporal-contextual Event Learning for Pedestrian Crossing Intent Prediction.- PoseRAC: Enhancing Repetitive Action Counting with Salient Poses.- Spatio-Temporal Graph Convolutional Networks for Pedestrian Trajectory Prediction.- Dual-branch StarNet with Mutual Attention and U-Net Denoising for Simultaneously Recognizing Keywords and Speakers.- Unsupervised Personalized Deep Learning for Wearable Human Activity Recognition.- The Role of AI in Optimizing Human-centered Complex Systems.- A Global Interactive and Bottleneck Fusion Model for Multi-Intent Spoken Language Understanding.- GloveTyping: A Hand Gesture Recognition System for Text Input Using a Hierarchical Framework with Attention Mechanism.- Impacts of Prompt Perturbation on Reducing Bias and Hallucination of Large Language Models.- A Multi-task Emotion Recognition Model based on Continuously Labeled EEG Signals.- MUR: Multimodal Unified Refinement for Multimedia Recommendation.- Identifying Misaligned Features for Cross-Domain Cold-start Recommendation.- Temporal Semantic Scoring Path aware Multi-Embedding Sequential Recommendation.- Online Labor Market Task Recommendation via Time-weighted Diffusion Model.- Multi-Pattern Joint Denoising Sequential Recommendation with Diffusion Model.- ProFetch: Accelerate Deep Recommendation System Training with Proactively Designed Data Layout and Dynamic Prefetching.
