Buch, Englisch, 141 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g
13th EAI International Conference, ICCASA 2024, Dalat City, Vietnam, October 15-16, 2024, Proceedings
Buch, Englisch, 141 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g
ISBN: 978-3-032-12940-6
Verlag: Springer
This book constitutes the refereed post proceedings of 13th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2024, held in Dalat City, Vietnam, during October 15–16, 2024.
The 9 full papers presented in this volume were carefully reviewed and selected from 26 submissions. The papers cover a wide spectrum of modern approaches and techniques for smart computing systems and their applications.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
Weitere Infos & Material
.- Toward Modeling Linguistic Fuzzy Network Flow Based on Hedge Algebra.
.- Application of Statistics in Geography to Construct Earthquake Risk Zoning Maps: A Case study in Kon Tum Province, Vietnam.
.- Prediction of the change of human blood glucose from Raman scattering by polynomial data preprocess method.
.- Relationship Between RTL Automata (ASARTL) and Looping Algorithm Specification (ASASPEC) Based on Algebraic Semantics in Synthesis of Stream Calculus-Based Computing Big Data in Livestream.
.- From Pixels to Graphs: Evaluating Deep Learning Architectures for Advanced Image Classification Tasks.
.- Enhancing Industrial And Security Applications Through AI-Integrated Iot Systems: Design And Implementation.
.- Towards Modeling Linguistic Fuzzy Neural Units Based on Hedge Algebra.
.- Detecting ships and boats on satellite images with machine learning approach.
.- Correlation Distance t-Test Collaborative Filtering Recommendation (t-TestCF).
