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New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence : The DiTTEt 2025 Collection / edited by Daniel H. de la Iglesia, Juan F. de Paz Santana, Alfonso J. López Rivero.
Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2025 Available online
View online- Format:
- Book
- Author/Creator:
- de la Iglesia, Daniel H.
- Series:
- Advances in Intelligent Systems and Computing, 2194-5365 ; 1465
- Language:
- English
- Subjects (All):
- Computational intelligence.
- Artificial intelligence.
- Computational Intelligence.
- Artificial Intelligence.
- Local Subjects:
- Computational Intelligence.
- Artificial Intelligence.
- Physical Description:
- 1 online resource (698 pages)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
- Summary:
- 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.
- Contents:
- 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 Human–AI 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.
- ISBN:
- 3-031-99474-4
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