• Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
Band 15023 - 11%

Artificial Neural Networks and Machine Learning – ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part VIII

11% sparen

65,99 € UVP 74,89 €

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.10.2024

Abbildungen

XXXIV, 463 p. 128 illus., 123 illus. in color.

Herausgeber

Michael Wand + weitere

Verlag

Springer

Seitenzahl

463

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

750 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72352-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.10.2024

Abbildungen

XXXIV, 463 p. 128 illus., 123 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

463

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

750 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72352-0

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)

  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024

  • .- Biosignal Processing in Medicine and Physiology.


    .- A deep learning multi-omics framework to combine microbiome and metabolome profiles for disease classification.


    .- CapsDA-Net: A Convolutional Capsule Domain Adversarial Neural Network for EEG-Based Attention 



    Recognition.


    .- ComplicaCode: Enhancing Disease Complication Detection in Electronic Health Records through



    ICD Path Generation.


    .- Depression detection based on multilevel semantic features.


    .- Depression Diagnosis and Analysis via Multimodal Multi-order Factor Fusion.


    .- Identify Disease-associated MiRNA-miRNA Pairs through Deep Tensor Factorization and Semi-supervised Learning.


    .- Interpretable EHR Disease Prediction System Based on Disease Experts and Patient Similarity



    Graph (DE-PSG).


    .- Meteorological Data based Detection of Stroke using Machine Learning Techniques.


    .- OFNN-UNI: Enhanced Optimized Fuzzy Neural Networks based on Unineurons for Advanced Sepsis



    Classification.


    .- ProTeM: Unifying Protein Function Prediction via Text Matching.


    .- SnoreOxiNet: Non-contact Diagnosis of Nocturnal Hypoxemia Using Cross-domain Acoustic Features.


    .- Unveiling the Potential of Synthetic Data in Sports Science: A Comparative Study of Generative Methods.


    .- Medical Image Processing.


    .- Adaptive Fusion Boundary-Enhanced Multilayer  Perceptual Network (FBAIM-Net) for Enhanced Polyp  Segmentation in Medical Imaging.


    .- Advancing Free-breathing Cardiac Cine MRI: Retrospective Respiratory Motion Correction Via Kspace-and-Image Guided Diffusion Model.


    .- Blood Cell Detection and Self-attention-based Mixed Attention Mechanism.


    .- CellSpot: Deep Learning-Based Efficient Cell Center Detection in Microscopic Images.


    .- Classification of dehiscence defects in titanium and zirconium dental implants.


    .- CurSegNet: 3D Dental Model Segmentation Network Based on Curve Feature Aggregation.


    .- DBrAL: A novel uncertainty-based active learning based on deep-broad learning for medical image classi cation.


    .- EDPS-SST: Enhanced Dynamic Path Stitching with Structural Similarity Thresholding for Large-Scale Medical Image Stitching under Sparse Pixel Overlap.


    .- Hop-Gated Graph Attention Network for ASD Diagnosis via PC-Based Graph Regularization



    Sparse Representation.


    .- MISS: A Generative Pre-training and Fine-tuning Approach for Med-VQA.


    .- MSD-HAM-Net: A Multi-modality Fusion Network of PET/CT Images for the Prognosis of



    DLBCL Patients.


    .- Multi-Modal Multi-Scale State Space Model for Medical Visual Question Answering.


    .- Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling.


    .- Point-based Weakly Supervised 2.5D Cell Segmentation.


    .- Relative Local Signal Strength: the Impact of Normalization on the Analysis of Neuroimaging Data with Deep Learning.


    .- SCANet: Dual Attention Network for Alzheimer’s Disease Diagnosis Based on Gated Residual and



    Spatial Asymmetry Mechanisms.


    .- SCST: Spatial Consistent Swin Transformer for Multi-Focus Biomedical Microscopic Image



    Fusion.


    .- KnowMIM: a self-supervised pre-training framework based on knowledge-guided masked



    image modeling for retinal vessel segmentation.


    .- Transferability of Non-Contrastive Self-Supervised Learning to Chronic Wound Image Recognition.


    .- Two-stage Medical Image-text Transfer with Supervised Contrastive Learning.