Buch, Englisch, 388 Seiten, Format (B × H): 155 mm x 235 mm
14th International Conference, CSoNet 2025, Danang, Vietnam, December 14–16, 2025, Proceedings Part II
Buch, Englisch, 388 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Computer Science
ISBN: 978-981-9246-45-8
Verlag: Springer
This is the preferred LNCS proceedings of the14th International Conference, on Computational Data and Social Networks, CSoNet 2025, held in Danang, Vietnam, December 14–16, 2025.
The 32 full appers and 16 short papers were carefully reviewed and submitted from 139 submissions.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
Weitere Infos & Material
Fairness-aware Analysis of Centrality Measures in Social Networks.- Towards Likelihood-Based Detection of Anomalous Node Subsets in Random Graphs.- Applications of Lambert-W Function in Industrial Technology Problems.- Audio-Visual Deepfake Detection via Triple-Branch Cross-Modal Transformers.- Speech Region and Gender Recognition on a Vietnamese Accent Dataset.- Spatial Point-Process Modeling of Event Dynamics in the Sumatra Megathrust: Integrating Poisson Intensity and Weibull Survival-Hazard Analysis.- An Enhanced Approach for Kidney Pathology Segmentation.- EEG-STGNN: Unified Spatial–Temporal Modeling for Alzheimer’s and FTD Detection.- Shrimp Feeding Sound Recognition Using Spectrogram Images and EfficientNet Deep Learning.- Advancing 3D Reconstruction: Integrating AI-Driven Neural Rendering with Geometric Techniques for High-Fidelity Model Generation from RGB Video.- Federated Learning with Homomorphic Encryption and Adversarial Robustness for Privacy-Preserving Anomaly Detection in Digital Forensics.
