Distributed Computing and Artificial Intelligence, Special Sessions III, 22nd International Conference

Nyhet

Häftad, Engelska, 2026

Av Esteban Jove Pérez, Francisco Zayas-Gato, Antonio Díaz-Longueira, José Luis Casteleiro Roca

3 959 kr

Kommande

This book of LNNS contains accepted papers presented at DCAI conference in the Special Session on Intelligent Systems Applications (ISA), Salamanca (Spain), June 2024. ISA is a major forum for the presentation of the development and applications of innovative techniques in the fields of building management, assistive technologies, applied engineering, and industry. The scientific committee of the ISA has selected eight full papers for its publication. This year, ISA Special Session has teamed up with “Logic Journal of the IGPL” (Oxford University Press) for one special issue, including selected papers from ISA Special Session 2025. We would like to thank all the contributing authors, the members of the program committee, and the organizing committee of the University of Salamanca for their hard and highly valuable work, which has been essential for the success of ISA 2025.

Produktinformation

Utgivningsdatum2026-01-05
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieLecture Notes in Networks and Systems
Antal sidor79
FörlagSpringer Nature Switzerland AG
ISBN9783032055033

Tillhör följande kategorier

Artificiell intelligens inom Data och it
Nätverk och kommunikation inom Data och it

Application of AI Models for Predicting Level in Sewerage Networks: A Case Study of a Wastewater Pumping Station.- Detection of occupational diseases using a semi-supervised approach.- Comparative performance analysis for modelling photovoltaic solar panels including their temperature.- Friction Stir Welding of Aluminum Alloys Defect Detection based on Convolutional Autoencoders.- Security in Constrained Application Protocol Networks: Evaluating Supervised Classification Techniques for DoS Attack Detection.- Predicting hydrogen consumption in a fuel cell using intelligent techniques.- Prediction of Titanium Dioxide (TiO₂) Concentration Using Machine Learning and Spectral Analysis.- Monitoring the Dynamics of Water Consumption from Time Series Using Machine Learning Techniques.