Innovations in Computer Vision and Data Classification: From Pandemic Data Analysis to Environmental and Health Monitoring (EAI/Springer Innovations in Communication and Computing) - Hardcover

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Dr. Arfan Ghani currently serves as an 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. 

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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. 

• Explores advancements in data classification, encompassing FPGA acceleration, neuromorphic hardware, and computer vision-based diagnosis;
• Presents data classification through real-world examples from healthcare, environmental science, and energy conversion, employing applied machine learning and deep neural networks;
• Includes guidance on the application of complex concepts with ease through a didactic approach and hands-on instruction