Neural Information Processing 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part VI /
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 selecte...
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| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
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Singapore :
Springer Nature Singapore : Imprint: Springer,
2025.
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| Vydání: | 1st ed. 2025. |
| Edice: | Lecture Notes in Computer Science,
15291 |
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| On-line přístup: | https://doi.org/10.1007/978-981-96-6591-4 |
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| 245 | 1 | 0 | |a Neural Information Processing |h [electronic resource] : |b 31st International Conference, ICONIP 2024, Auckland, New Zealand, December 2–6, 2024, Proceedings, Part VI / |c edited by Mufti Mahmud, Maryam Doborjeh, Kevin Wong, Andrew Chi Sing Leung, Zohreh Doborjeh, M. Tanveer. |
| 250 | |a 1st ed. 2025. | ||
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| 490 | 1 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 15291 | |
| 505 | 0 | |a Ranking Region-based OD-Betweenness Centrality in Road Networks -- Mining Fuzzy Partial Periodic Frequent Patterns in Very Large Temporal Databases -- Style Miner: Find Significant and Stable Factors in Time Series with Constrained Reinforcement Learning -- Ensemble Learning Prediction Based on Comprehensive Factors for Portfolio Optimization -- TDAT: A Real-time Two-stage DDoS Attacks Detector Based on Anomaly Transformer -- Unified Mask Graph Modeling for Incomplete Tabular Learning -- Learning Granularity Representation for Temporal Knowledge Graph Completion -- MPLinear: Multiscale Patch Linear Model for Long-Term Time Series Forecasting -- Residual Broad Learning System with Variational Autoencoder for Robust Regression -- STEncoder: Robust Decomposition for Time Series Forecasting -- Fine-Grained Common Knowledge Learning for Domain Adaptive Few-shot Relation Extraction -- DMGCL: Denoising Multi-View Graph Contrastive Learning for Robust Recommendation -- STMGFN: Spatio-Temporal Multi-Graph Fusion Network for Traffic Flow Prediction -- Refined Sentiment Analysis Using POS Features and LDA: Mitigating Polysemy and Sparsity with BERT Contextual Embedding -- Table-Based Two-Stage Relation Classification Method for Trigger-Free Document-Level Event Extraction -- CDIG: Customizable Dual Interaction Graph module for News Recommendation -- VEBiLSTM: A Neural Network for Field-road Classification using Enhanced Spatiotemporal Features -- Seq-LSTM-Conv: Multi-sequence Aggregated Forecasting Using LSTM and Convolutional Neural Networks -- Test-time Adaptation with Angular Distance-based Prediction -- FedAKD:Heterogeneous Graph Federated Learning Framework based on Data Augmentation and Knowledge Distillation -- TSIV: A Two-Stage Approach for Identifying Encrypted Video Traffic in Unstable Network -- Who is the Writer?Identifying the Generative Model by Writing Style -- RAEDiff: Diffusion Models Enable Self-Generation and Self-Recovery of Reversible Adversarial Examples -- OKey: Towards More Controllable, Secure and Robust Diffusion Model Image Steganography Using Optimized Key -- Automated Mining of Multi-Dimensional Information from APT Malware for Effective Feature Analysis and Threat Actor Attribution -- PaPa: Propagation Pattern Enhanced Prompt Learning for Zero-shot Rumor Detection. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Pattern recognition systems. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 1 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Machine Learning. |
| 700 | 1 | |a Mahmud, Mufti. |e editor. |0 (orcid)0000-0002-2037-8348 |1 https://orcid.org/0000-0002-2037-8348 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Doborjeh, Maryam. |e editor. |0 (orcid)0000-0003-4953-0662 |1 https://orcid.org/0000-0003-4953-0662 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Wong, Kevin. |e editor. |0 (orcid)0000-0001-8767-1031 |1 https://orcid.org/0000-0001-8767-1031 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Leung, Andrew Chi Sing. |e editor. |0 (orcid)0000-0003-0962-6723 |1 https://orcid.org/0000-0003-0962-6723 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Doborjeh, Zohreh. |e editor. |0 (orcid)0000-0003-1431-8258 |1 https://orcid.org/0000-0003-1431-8258 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Tanveer, M. |e editor. |0 (orcid)0000-0002-5727-3697 |1 https://orcid.org/0000-0002-5727-3697 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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