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Innovations in Computer Vision and Data Classification

From Pandemic Data Analysis to Environmental and Health Monitoring

  • Book
  • Sep 2024

Overview

  • Explores advancements in data classification, including FPGA acceleration and computer vision-based diagnosis
  • Presents data classification with real-world examples from healthcare, environmental science, and energy conversion
  • Includes how to apply complex concepts with ease through a didactic approach and hands-on guidance

Part of the book series: EAI/Springer Innovations in Communication and Computing (EAISICC)

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About this book

This book delves into the dynamic realm of data classification, focusing on its real-world applications. Through an insightful journey, readers are introduced to the practical applications of reconfigurable hardware, machine learning, computer vision, and neuromorphic circuit design across diverse domains. The author explores topics such as the role of Field-Programmable Gate Arrays (FPGAs) in expediting pandemic data analysis and the transformative impact of computer vision on healthcare. Additionally, the book delves into environmental data classification, energy-efficient solutions for deep neural network applications, and real-time performance analysis of energy conversion algorithms. With the author's guidance, readers are led through practical implementations, ensuring a comprehensive grasp of each subject matter. Whether a seasoned researcher, engineer, or student, this book equips readers with the tools to make data-driven decisions, optimize systems, and innovate solutions across various fields, from healthcare to environmental monitoring. 

Keywords

  • Data classification
  • Computer vision
  • Hardware accelerators
  • Reconfigurable hardware
  • Neural networks

Authors and Affiliations

  • School of Engineering, American University of Ras al Khaimah, Ras Al Khaimah, United Arab Emirates

    Arfan Ghani

About the author

Dr. Arfan Ghani currently serves as an Associate Professor in Computer Science and Engineering at the American University of Ras al Khaimah, UAE. He attained academic qualifications and gained valuable experience from UK institutions, including Ulster, Coventry, and Newcastle. Dr Ghani's industrial research and development expertise spans various roles at Intel Research, the University of Cambridge, and Microchip Denmark. With extensive applied research experience, he has made significant contributions to leading journals and conferences and successfully secured substantial collaborative funding from prestigious entities such as EPSRC, EU, Innovate UK, the Royal Academy of Engineering, and the German Aerospace Centre. Dr. Ghani actively engages in scholarly activities, serving as an Associate Editor for Elsevier Neurocomputing, Guest Editor, and Technical Programme Committee member for numerous IEEE/IET conferences. His contributions to the field have been acknowledged with several awards, including the Best Paper award from the European Neural Network Society in 2007. Dr. Ghani specializes in Computer Vision-based healthcare diagnostics, AI chip design, and reconfigurable hardware accelerators for machine learning and deep neural network architectures. His expertise in these areas has led to groundbreaking advancements in applying technology to solve critical healthcare challenges. Dr. Ghani is a distinguished member of the Institution of Engineering and Technology (IET), a Chartered Engineer (CEng), and a Fellow of the Higher Education Academy in the UK. 

Bibliographic Information

  • Book Title: Innovations in Computer Vision and Data Classification

  • Book Subtitle: From Pandemic Data Analysis to Environmental and Health Monitoring

  • Authors: Arfan Ghani

  • Series Title: EAI/Springer Innovations in Communication and Computing

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: European Alliance for Innovation 2024

  • Hardcover ISBN: 978-3-031-60139-2Due: 18 September 2024

  • Softcover ISBN: 978-3-031-60142-2Due: 18 September 2024

  • eBook ISBN: 978-3-031-60140-8Due: 18 September 2024

  • Series ISSN: 2522-8595

  • Series E-ISSN: 2522-8609

  • Edition Number: 1

  • Number of Pages: XIV, 149

  • Number of Illustrations: 25 b/w illustrations, 75 illustrations in colour

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