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

Communications in Computer and Information Science, 1865-0937 ; 2296

Language:

English

Subjects (All):

Pattern recognition systems.

Data mining.

Machine learning.

Social sciences--Data processing.

Social sciences.

Automated Pattern Recognition.

Data Mining and Knowledge Discovery.

Machine Learning.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Automated Pattern Recognition.

Data Mining and Knowledge Discovery.

Machine Learning.

Computer Application in Social and Behavioral Sciences.

Physical Description:

1 online resource (639 pages)

Edition:

1st ed. 2025.

Place of Publication:

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

Summary:

The sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.

Contents:

Utilizing Deep Learning to address Temporal and Spatial Dependencies in Weather Forecasting

Imagined Digits Recognition Based on Masked Electroencephalography Modeling

THGCN:Temporal Hypergraph Convolutional Network for Subject Independent EEG Emotion Recognition

ANN-Based Pollution Forecasting Through Short-Term Spatio-Temporal Analysis: A North Island, New Zealand Case Study

Detection of Animal Movement from Weather Radar using Self-Supervised Learning

From Concrete to Abstract: A Multimodal Generative Approach to Abstract Concept Learning

Analysis on Artificial Representations of a Trained AlexNet Model Using the CIFAR-10 Dataset

Modelling the influence of temperature and rainfall on the spread of African swine fever in Australia

An EEG-based Spatial-Temporal Hybrid Architecture for Cognitive Load Detection

Decoding Psychological Stress during Laparoscopic Surgery Training: Insights from EEG

A Comparison between baseline models and a transformer network for SOC prediction of lithium-ion batteries

Insights into Long-term Electrical Load Forecasting: Explainable AI approach on Multivariate LSTM

Artificial Intelligence and Climate Change: A Review of Causes and Opportunities

Towards a machine learning model to predict cognitive ability using EEG data and virtual spatial navigation task scores in intellectually disabled adults

HyPeFL: Tackling Data Heterogeneity via Hypernetwork in Personalized Federated Learning

NeuroGeMS: An open-source GUI software for multimodal modelling in biomedical research and applications

Multimodal Multiview Graph Convolution Network for the Diagnosis of Alzheimer’s Disease

DNA-PRIME: Advanced DNA Sequence Compression through Enhanced Feature Fusion and Weight Hashing

SnE-VNet: A Deep Learning Model with Squeeze and Excitation for Improved 3D Stroke Lesion Segmentation

Morphology-Guided 3D Skull Gender Identification with Point-BERT

Cuffless Blood Pressure Measurement From Photoplethysmography through High and low Frequency Information Fusion Attention Mechanism

Hybrid EEG-fNIRS decoding for fine joint motor imagery of Unilateral Upper Limb with Two-Stage Hybrid Training

A Neural Network-Augmented Case-Based Reasoning Framework for Weather Risk Modeling using Remote Sensing Data

Autonomous Design of Floor Plan Based on Architectural Drawings Example without Neighbour Relation

Using ensemble learning algorithms to integrate multisource remote sensing data for mapping regional forest canopy height

MTDS: Meta-Path Context Enhanced Drug Combination Synergy Prediction

A Federated Learning Approach for Genomic Selection in Pigs

TOP-EEG: a robust software to predict the outcomes of therapies for depression using EEG signals in DGMD domain

Neural Network as Surrogate Model for Sleep EEG Trajectories and Insomnia Disorder Classification.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9670-33-0

OCLC:

1525621614