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Intelligent and Fuzzy Systems

Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings...

Cengiz Kahraman Selcuk Cebi Basar Oztaysi Sezi Cevik Onar

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English
Springer International Publishing AG
26 July 2025
Artificial Intelligence in Human-Centric, Resilient & Sustainable Industries

This book focuses on benefiting artificial intelligent tools in our business and social life under emerging conditions. Human-centric, resilient, and sustainable industries are built on ideals like human-centricity, ecological advantages, or social benefits. The mission of human-centric artificial intelligence is to improve people’s lives by offering solutions that boost productivity, accessibility to resources, security, well-being, and general quality of life. The latest intelligent methods and techniques on human-centric, resilient, and sustainable industries are introduced by theory and applications. This book covers the chapters of world-wide known experts on machine learning, medical image processing, process intelligence, process mining, and others. The intended readers are intelligent systems researchers, lecturers, M.

Sc. and Ph.D. students trying to develop approaches giving human needs, values, and viewpoints top priority through artificial intelligent systems.
Edited by:   , , , ,
Imprint:   Springer International Publishing AG
Country of Publication:   Switzerland
Volume:   1529
Dimensions:   Height: 235mm,  Width: 155mm, 
ISBN:   9783031979910
ISBN 10:   3031979915
Series:   Lecture Notes in Networks and Systems
Pages:   820
Publication Date:  
Audience:   Professional and scholarly ,  College/higher education ,  Undergraduate ,  Further / Higher Education
Format:   Paperback
Publisher's Status:   Active
Deep Learning Assisted Optimization for End-effector Position and Orientation.- Multi Modal Deep Learning for Earthquake Damage Detection Integrating SAR and Optical Imagery.- AirQ ResUNet  A Residual U Net Based Deep Learning Surrogate for High Resolution PM2.5 Prediction in Urban Environments.- Deep Learning Based Earthquake Prediction  Magnitude and Depth Estimation Using ConvLSTM.- A Deep Learning Approach to Sentiment Classification Insights from Product Review Analysis.

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