E-Book, Englisch, 448 Seiten
Reihe: Springer Nature Proceedings excluding Computer Science
Kodad / Moussaoui The 3rd International Conference on Artificial Intelligence and Smart Applications (AISA’25), Volume 1: Artificial Intelligence, IoT, and Smart Applications
Erscheinungsjahr 2026
ISBN: 978-3-032-18716-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 448 Seiten
Reihe: Springer Nature Proceedings excluding Computer Science
ISBN: 978-3-032-18716-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book presents a selection of peer-reviewed papers from the Third International Conference on Artificial Intelligence and Smart Applications (AISA’25), focusing on research topics related to artificial intelligence and data-driven technologies.
This book covers a broad range of themes, including artificial intelligence, machine learning, deep learning, data analysis, smart applications, healthcare technologies, and Internet of Things (IoT) systems. The chapters address both theoretical approaches and applied solutions, highlighting innovative methods and real-world applications in intelligent and connected environments.
Bringing together work from international researchers and practitioners, this book offers a comprehensive overview of current advances and emerging trends in AI-based systems. It is intended for researchers, engineers, professionals, and graduate students seeking insights into intelligent technologies and their applications in healthcare and smart environments.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
An Analytical Study of Fine-Tuning Approaches for LLMs in Software Engineering.- Towards a New Evaluation Protocol for Grey Sheep User Detection Approaches.- The Role of Reasoning in LLMs: A Comparative Study on Machine Reading Comprehension using the SQuAD 2.0 and AdversarialQA Datasets.- Optimization of Deep Neural Networks for Complex Multivariate Data: An Overview.- Predicting VMAT Modulation Complexity Before Planning: A Comparative Analysis of Machine Learning Classifiers.- Specialized Approaches for Visual Instruction Grounding in GUI Automation.- Inspired-Logic Loss Functions for Deep Learning Using Sugeno-Weber Connective Systems.- Dual-Image-Guided Grounding of User Interfaces with MLLMs.- Image Segmentation using Covariance matrix and Dominant sets.




