Distributed Computing and Artificial Intelligence, 21st International Conference

by Ravikumar Chinthaginjala (Editor), Pawel Sitek (Editor), Nasro Min-Allah (Editor), Kenji Matsui (Editor), Sascha Ossowski (Editor)

Overview

This book serves as a forum to present applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing. This edition brings together experience, current work, and promising future trends related to distributed computing, artificial intelligence, and their applications to provide efficient solutions to real-world problems. The technical program this year is both high in quality and diverse, featuring contributions from well-established and evolving research areas. Specifically, 138 papers were submitted by authors from 43 different countries, representing a truly global network of research activity. The DCAI’24 technical program has selected 36 full papers for the main track, and as in previous editions, there will be special issues in ranked journals. This symposium is organized by the University of Salamanca (Spain). The authors would like to thank all the contributing authors, the program committee members, National Associations (AEPIA, APPIA, LASI), and the sponsors (AIR Institute).

Product Details

ISBN-13:	9783031820731
Publisher:	Springer-Verlag New York, LLC
Publication date:	03/14/2025
Series:	Lecture Notes in Networks and Systems , #1259
Sold by:	Barnes & Noble
Format:	eBook
File size:	39 MB
Note:	This product may take a few minutes to download.

Table of Contents

.- 1 Adaptive Traffic Light Control through Reinforcement Learning based on Sensor Integration.

.- 2 Improvement of anomaly detection through enhanced feature extraction in TimesNet.

.- 3 An Explainable AI framework for comparative analysis of the model explanations in Breast Cancer Prediction.

.- 4 Enhancing Power Forecasting Through Contextual Awareness With C-Means and K-Means Approaches.

.- 5 Development and Assessment of a System to Help Students Improve Self-Compassion.

.- 6 Leveraging Machine Learning and Deep Learning Models for Enhanced Stock Price Prediction: A State-of-the-Art Analysis.

.- 7 Type-Theory of Algorithms with Chain-Free Memory, etc.