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NanoMind: Exploring Synergies in Nanotechnology and Machine Learning (eBook)

Artikelnummer: 978-3-031-77296-2
Einband: PDF
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NanoMind" serves as a captivating exploration at the crossroads of nanotechnology and machine learning, revealing the profound synergies between these two groundbreaking fields. At its core, the book is a guided tour through the intricate realms of nanoscale materials and artificial intelligence, illuminating their combined potential to reshape the landscape of innovation.

The journey begins with an introductory chapter, setting the stage for the convergence of nanotechnology and machine learning. Readers are introduced to the fundamental principles and applications of nanoscale materials, laying the groundwork for a deeper understanding of the nano-world. Simultaneously, the basics of machine learning are demystified, providing a comprehensive overview of the methodologies and concepts that underpin AI.

"Nanoscale Sensing and Imaging" takes readers into the realm of advanced technologies, showcasing how machine learning enhances our ability to sense and image materials at the nanoscale. The narrative then seamlessly transitions to the creation and optimization of materials with nano-enhancements in "Nano-Enabled Materials." This section demonstrates how the marriage of nanotechnology and machine learning can lead to the development of materials with unprecedented properties, fostering innovation in diverse industries.

The exploration extends into the field of healthcare with "Machine Learning in Nanomedicine," unveiling how AI at the nanoscale is transforming diagnostics, drug delivery, and personalized medicine. "Quantum Computing and Nanotechnology" delves into the cutting-edge intersection of quantum computing, nanotech, and machine learning, showcasing the potential for revolutionary advancements in computation and problem-solving.

However, the journey isn't without its challenges. The book devotes a section to "Challenges and Ethical Considerations," addressing the complexities and ethical dimensions associated with the convergence of nanotechnology and machine learning. This discussion ensures that readers are equipped to navigate the ethical landscape responsibly.

NanoMind" serves as a captivating exploration at the crossroads of nanotechnology and machine learning, revealing the profound synergies between these two groundbreaking fields. At its core, the book is a guided tour through the intricate realms of nanoscale materials and artificial intelligence, illuminating their combined potential to reshape the landscape of innovation.

The journey begins with an introductory chapter, setting the stage for the convergence of nanotechnology and machine learning. Readers are introduced to the fundamental principles and applications of nanoscale materials, laying the groundwork for a deeper understanding of the nano-world. Simultaneously, the basics of machine learning are demystified, providing a comprehensive overview of the methodologies and concepts that underpin AI.

"Nanoscale Sensing and Imaging" takes readers into the realm of advanced technologies, showcasing how machine learning enhances our ability to sense and image materials at the nanoscale. The narrative then seamlessly transitions to the creation and optimization of materials with nano-enhancements in "Nano-Enabled Materials." This section demonstrates how the marriage of nanotechnology and machine learning can lead to the development of materials with unprecedented properties, fostering innovation in diverse industries.

The exploration extends into the field of healthcare with "Machine Learning in Nanomedicine," unveiling how AI at the nanoscale is transforming diagnostics, drug delivery, and personalized medicine. "Quantum Computing and Nanotechnology" delves into the cutting-edge intersection of quantum computing, nanotech, and machine learning, showcasing the potential for revolutionary advancements in computation and problem-solving.

However, the journey isn't without its challenges. The book devotes a section to "Challenges and Ethical Considerations," addressing the complexities and ethical dimensions associated with the convergence of nanotechnology and machine learning. This discussion ensures that readers are equipped to navigate the ethical landscape responsibly.

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VerlagSpringer Nature Switzerland
EinbandPDF
Erscheinungsjahr2025
Seitenangabe289 S.
AusgabekennzeichenEnglisch
AbbildungenXIII, 289 p. 63 illus., 51 illus. in color.
Masse8'994 KB
PlattformPDF
ReiheInformation Systems Engineering and Management; Intelligent Technologies and Robotics; Intelligent Technologies and Robotics
AutorMahajan, Shubham (Hrsg.) / Das, Susanta (Hrsg.) / Rocha, Alvaro (Hrsg.) / Roy, Debanjali Barman (Hrsg.) / Chawla, Paras (Hrsg.)

Alle Bände der Reihe "Information Systems Engineering and Management; Intelligent Technologies and Robotics; Intelligent Technologies and Robotics (R0)"

Über den Autor Shubham (Hrsg.) Mahajan

Shubham Mahajan, PhD is a Post Doctoral Reseacher in the Department of Applied Data Science at Noroff Univeristy College in Kristiansand, Norway. He holds eleven Indian patents, one Australian and one German patent and has published more than 70 articles in peer-reviewed journals and conferences. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication. Kamal Upreti, PhD is an Associate Professor in the Department of Computer Science at Christ University, Ghaziabad, India. He has published more than 50 patents, 32 magazine issues, 110 research papers in journals and international conferences, and 45 books. His areas of interest include modern physics, data analytics, cyber security, machine learning, healthcare, and embedded systems. Rashmi Agrawal, PhD is a Professor and Associate Dean at the School of Computer Applications at the Manav Rahna International Institute of Research and Studies, Faridabad, India with more than 23 years of experience. She has published more than 100 research papers in esteemed national and international journals and conferences and authored and co-authored books with distinguished publishers. Her research focuses on machine learning, classification, and pattern recognintion.

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