Buch, Englisch, 451 Seiten, Format (B × H): 155 mm x 235 mm
Select Proceedings of the 3rd International Conference, AICTA 2025
Buch, Englisch, 451 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Lecture Notes in Networks and Systems
ISBN: 978-981-959819-9
Verlag: Springer
This book presents the proceedings of the 3rd International Conference on Artificial Intelligence, Computing Technologies, Internet of Things (IoT), and Data Analytics. The proceedings highlight research in distributed, parallel, and cloud computing, image processing and computer vision, machine learning and deep learning, the Internet of Things (IoT) and data analytics, cybersecurity, and related interdisciplinary domains. The book discusses recent innovations, practical challenges, and future research directions in computer science and engineering. These proceedings reflect this integration, offering a comprehensive platform for advancing theory, applications, and interdisciplinary research across these key domains.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
- Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
A Debiasing Framework for Graph Neural Networks Using Contrastive Learning.- AI-Powered Decision Optimization for Dynamic Resource Management.- Optimized Decision-Making in Multi-Level Trade Networks.- Envision of Satellite LOS/NLOS using TLBO powered Scaled Conjugate Gradient Neural Network.- Training optimization issues in detecting rip waves, high tides, and low tides in Beach using the ResNet101 deep learning algorithm.- Attention-Based LSTM for Robust Water Quality Prediction in Fish-Rice Co-Cropping Systems.- Real Time Small Object in Dynamic Wartime Environments: A Survey of Deep Learning Techniques, Challenges and Deployment Strategies.- Evaluating the Fault-Proneness Score: Empirical Insights and Practical Implications.- A Comparative Study of Hyperparameter Tuning Strategies for Machine Learning Models in Breast Cancer Classification.
