Neural Information Processing : 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part V / edited by Mufti Mahmud, Maryam Doborjeh, Kevin Wong, Andrew Chi Sing Leung, Zohreh Doborjeh, M. Tanveer.

Format:

Book

Author/Creator:

Mahmud, Mufti.

Contributor:

Doborjeh, Maryam.

Huang, Dejiang.

Leung, Andrew Chi Sing.

Doborjeh, Zohreh.

Tanveer, M.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 15290

Language:

English

Subjects (All):

Pattern recognition systems.

Data mining.

Machine learning.

Automated Pattern Recognition.

Data Mining and Knowledge Discovery.

Machine Learning.

Local Subjects:

Automated Pattern Recognition.

Data Mining and Knowledge Discovery.

Machine Learning.

Physical Description:

1 online resource (794 pages)

Edition:

1st ed. 2025.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2025.

Summary:

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9665-88-4

OCLC:

1525619871