04348nam a22005415i 4500 978-3-031-60140-8 DE-He213 20251030112839.0 cr nn 008mamaa 240806s2024 sz | s |||| 0|eng d 9783031601408 978-3-031-60140-8 10.1007/978-3-031-60140-8 doi TK5101-5105.9 TJK bicssc TEC041000 bisacsh TJK thema Ghani, Arfan. author. aut http://id.loc.gov/vocabulary/relators/aut 81970 Innovations in Computer Vision and Data Classification [electronic resource] : From Pandemic Data Analysis to Environmental and Health Monitoring / by Arfan Ghani. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIV, 148 p. 100 illus., 75 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda EAI/Springer Innovations in Communication and Computing, 2522-8609 Introduction -- Accelerating the classification of pandemic data using reconfigurable hardware (FPGA) and machine learning -- Computer vision based automated diagnosis for skin cancer detection -- Design and development of an integrated analytics platform for environmental data classification -- Design and development of multimodal healthcare data sensing and classification using Deep Neural Networks (DNNs) -- Low-power analogue design with Spiking Neural Networks (SNN) -- Full custom design of a sustainable, low-power environmental monitoring node -- Real-time performance analysis of Maximum-Power-Point Tracking (MPPT) algorithm for energy conversion on hardware platform (FPGA) -- Computer-vision based real data generation for object classification -- Conclusion. 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. Telecommunication. Artificial intelligence Data processing. Computational intelligence. 64052 Communications Engineering, Networks. Data Science. Computational Intelligence. 64052 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031601392 Printed edition: 9783031601415 Printed edition: 9783031601422 EAI/Springer Innovations in Communication and Computing, 2522-8609 65695 https://doi.org/10.1007/978-3-031-60140-8 ZDB-2-ENG ZDB-2-SXE 62267 62267