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Distributed Computing and Artificial Intelligence, 21st International Conference [Mīkstie vāki]

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  • Formāts: Paperback / softback, 368 pages, height x width: 235x155 mm, 97 Illustrations, color; 21 Illustrations, black and white; XVI, 368 p. 118 illus., 97 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Networks and Systems 1259
  • Izdošanas datums: 18-Feb-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 303182072X
  • ISBN-13: 9783031820724
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Distributed Computing and Artificial Intelligence, 21st International ConferencePalielināt
  • Formāts: Paperback / softback, 368 pages, height x width: 235x155 mm, 97 Illustrations, color; 21 Illustrations, black and white; XVI, 368 p. 118 illus., 97 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Networks and Systems 1259
  • Izdošanas datums: 18-Feb-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 303182072X
  • ISBN-13: 9783031820724
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031820724.html

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).

.- 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.