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Advances in Information, Computing and Technology : Proceedings of the International Conference on Information, Computing and Technology (ICICT2025), Volume 2 / edited by Witold Pedrycz, John Wang, Jianqi Li.
Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2026 Available online
View online- Format:
- Book
- Author/Creator:
- Pedrycz, Witold.
- Series:
- Lecture Notes in Networks and Systems, 2367-3389 ; 1735
- Language:
- English
- Subjects (All):
- Computational intelligence.
- Artificial intelligence.
- Engineering--Data processing.
- Engineering.
- Computational Intelligence.
- Artificial Intelligence.
- Data Engineering.
- Local Subjects:
- Computational Intelligence.
- Artificial Intelligence.
- Data Engineering.
- Physical Description:
- 1 online resource (686 pages)
- Edition:
- 1st ed. 2026.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2026.
- Summary:
- This book delivers a practical, forward-looking guide to cutting-edge developments at the intersection of information science, computing, and technology, equipping readers to bridge theoretical advancements with real-world application. By prioritizing actionable insights over abstract theory, it addresses the gap between rapid tech evolution and the need for accessible, applicable knowledge for professionals and learners alike. Rather than organizing content by traditional subdisciplines, this book adopts an interdisciplinary, problem-centric approach This novelty ensures readers grasp how technologies interact to solve complex challenges, a perspective often missing in more siloed texts. Intended for a broad audience, it serves as a go-to resource for tech professionals seeking to update their skills, upper-level undergraduate/graduate students in related fields, and researchers needing a concise overview of cross-domain progress.
- Contents:
- Prediction and Analysis of the Trend of Economic Management Strategy of Tourism Industry by Neural Network Model
- Research on the Application of Big Data-Driven Multi-Agent Reinforcement Learning in Autonomous Driving Traffic Control
- Low-Carbon Green Indicator System for Transportation Logistics and Energy Efficiency Optimization Methods.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-032-11957-X
- 9783032119575
- OCLC:
- 1569917872
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