Artificial Neural Networks and Machine Learning – ICANN 2023

TRAMA

The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023.The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.  

SOMMARIO

Advancing Brain Tumor Detection with Multiple Instance Learning on Magnetic Resonance Spectroscopy Data.- An Echo State Network-Based Method for Identity Recognition with Continuous Blood Pressure Data.- Analysis and Interpretation of ECG Time series through Convolutional Neural Networks in Brugada Syndrome Diagnosis.- Analysis of Augmentations in Contrastive Learning for Parkinson’s Disease Diagnosis.- BF-Net: A Fine-Grained Network for Identify Bacterial and Fungal Keratitis.- Bilateral Mammogram Mass Detection Based On Window Cross Attention.- Boundary Attentive Spatial Multi-Scale Network For Cardiac MRI Image Segmentation.- Clinical pixel feature recalibration module for ophthalmic image classification.- CopiFilter: An Auxiliary Module Adapts Pre-trained Transformers for Medical Dialogue Summarization.- IESBU-Net: A lightweight skin lesion segmentation UNet with inner-module extension and skip-connection bridge.- Molecular Substructure-based Double-Central Drug-Drug Interaction prediction.- Prediction of cancer drug sensitivity based on GBDT-RF algorithm.- Risk stratification of malignant melanoma using neural networks.- Symmetry-Aware Siamese Network: Exploiting Pathological Asymmetry for Chest X-Ray Analysis.- The Optimization and Parallelization of Two-Dimensional Zigzag Scanning on the Matrix.- Tooth segmentation from Cone-Beam CT Images through boundary refinement.- Transformer Based Prototype Learning for Weakly-Supervised Histopathology Tissue Semantic Segmentation.- A Balanced Relation Prediction Framework for Scene Graph Generation.- A Graph Convolutional Siamese Network for Assessment and Recognition Physical Rehabilitation Exercises.- A Graph Neural Network-based Smart Contract Vulnerability Detection Method With Artificial Rule.- Adaptive Randomized Graph Neural Network based on Markov Diffusion Kernel.- Adaptive Weighted Multi-View Evidential Clustering.- An untrained neural model for fast and accurate graph classification.- BGEK: External Knowledge-enhanced Graph Convolutional Networks for Rumor Detection in Online Social Networks.- BIG-FG: A Bi-directional Interaction Graph Framework with Filter Gate Mechanism for Chinese Spoken Language Understanding.- Co-RGCN: A Bi-path GCN-based Co-Regression model for Multi-intent Detection and Slot Filling.- DNFS: a Digraph Neural Network with the First-order and the Second-order Similarity.- Efficient Question Answering Based on Language Models and Knowledge Graphs.- Event association analysis using graph rules.- Fake Review Detection via Heterogeneous Graph Attention Network.- GatedGCN with GraphSage to Solve Traveling Salesman Problem.- GNN Graph Classification Method to Discover Climate Change Patterns.- GNN-MRC: Machine Reading Comprehension based on GNN Augmentation.- Graph Convolutional Network Semantic Enhancement Hashing for Self-supervised Cross-Modal Retrieval.- Heterogeneous Graph Neural Network Knowledge Graph Completion Model Based on Improved Attention Mechanism.- Hierarchical Diachronic Embedding of Knowledge Graph combined with Fragmentary Information Filtering.- K-DLM: A Domain-Adaptive Language Model Pre-Training Framework with Knowledge Graph.- Label Enhanced Graph Attention Network for Truth Inference.- LogE-Net: Logic evolution network for temporal knowledge graph forecasting.- LTNI-FGML: Federated Graph Machine Learning on Long-Tailed and Non-IID Data via Logit Calibration.- Multi-Granularity Contrastive Learning for Graph with Hierarchical Pooling.- Multimodal Cross-Attention Graph Network for Desire Detection.- Negative Edge Prediction for Attributed Graph Clustering.- One-Class Intrusion Detection with Dynamic Graphs.- Sequence-based Modeling for Temporal Knowledge Graph Link Prediction.- Structure-Enhanced Graph Neural ODE Network for Temporal Link Prediction.- Supervised Attention Using Homophily in Graph Neural Networks.- Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783031442155
• Collana: Lecture Notes in Computer Science
• Dimensioni: 235 x 155 mm
• Formato: Brossura
• Illustration Notes: XXXV, 603 p. 173 illus., 158 illus. in color.
• Pagine Arabe: 603
• Pagine Romane: xxxv