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New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence: The DITTEt 2025 Collection [Mīkstie vāki]

  • Formāts: Paperback / softback, 497 pages, height x width: 235x155 mm, 92 Illustrations, color; 100 Illustrations, black and white; XIII, 497 p. 192 illus., 92 illus. in color., 1 Paperback / softback
  • Sērija : Advances in Intelligent Systems and Computing 1465
  • Izdošanas datums: 04-Sep-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031994736
  • ISBN-13: 9783031994739
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 180,78 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Standarta cena: 212,69 €
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  • Formāts: Paperback / softback, 497 pages, height x width: 235x155 mm, 92 Illustrations, color; 100 Illustrations, black and white; XIII, 497 p. 192 illus., 92 illus. in color., 1 Paperback / softback
  • Sērija : Advances in Intelligent Systems and Computing 1465
  • Izdošanas datums: 04-Sep-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031994736
  • ISBN-13: 9783031994739
Citas grāmatas par šo tēmu:

This book explores the latest advances in artificial intelligence, big data, the Internet of Things, and other disruptive technologies, while also addressing the ethical and social challenges they pose. It brings together peer-reviewed research presented at an international conference, offering a multidisciplinary perspective that combines technical innovation with critical reflection. The volume highlights new developments in AI applications, intelligent systems, and data-driven solutions, alongside analyses of their real-world impact. It is designed for researchers, postgraduate students, developers, and decision-makers interested in both cutting-edge technological progress and its broader implications. By connecting technical achievements with ethical and societal considerations, the book provides a comprehensive view of how innovation can be guided responsibly in diverse fields such as healthcare, climate science, politics, and cybersecurity.

.- Benchmarking DINOv2 on Large-Scale Multiclass Classification Tasks: a
Flowers Case Study.- AI-Driven Personalization in Mobile Health Applications:
An Elderly-Focused Approach to Health Monitoring and Prediction.- Assisted
Development of an Urban Planning Ontology via HumanAI Collaboration: A Case
Study in Algodre (Zamora).- Are the graph neural networks better than
standard classifiers? - Classification of skin cell structure in optical
microscope images using transfer learning and watershed segmentation, etc.