- Neu
Buch, Englisch, 543 Seiten, Format (B × H): 155 mm x 235 mm
Proceedings of the 13th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA 2025), Volume 2
Buch, Englisch, 543 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Smart Innovation, Systems and Technologies
ISBN: 978-3-032-18973-8
Verlag: Springer
The book presents the proceedings of the 13th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2025), held at Intelligent Systems Research Group (ISRG), London Metropolitan University, London, United Kingdom, during June 6–7, 2025. Researchers, scientists, engineers and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. This book is divided into four volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This book is a valuable resource for postgraduate students in various engineering disciplines.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
Weitere Infos & Material
Distinct between AI generated images and actual images using Swin Transformer.- Bayesian Modeling of Autoregressive Hurdle INGARCHX Models.- TinyML Applications Airwriting System Applied with TinyML for Vietnamese Handwriting Recognition.- NeuroViT Net A Study on Performance Improvement of a Transformer Based Brain Tumor Classification Model.- Evaluating NLP Strategies and Machine Learning Approaches for Detecting Rumors Across Multiple Sources.- Optimized Machine Learning Models for Efficient Protein Classification Using Sequence Composition Features.




