Krishna Pratap Singh – författare
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8 produkter
8 produkter
Inbunden, Engelska, 2023
2 680 kr
Skickas inom 10-15 vardagar
This book is a collection of the high-quality research articles in the field of computer vision and robotics which are presented in International Conference on Computer Vision and Robotics (ICCVR 2022), organized by BBD University Lucknow India, during 21 – 22 May 2022.
Engelska, 2023
625 kr
Skickas inom 5-8 vardagar
Häftad, Engelska, 2024
2 680 kr
Skickas inom 10-15 vardagar
This book is a collection of the high-quality research articles in the field of computer vision and robotics which are presented in International Conference on Computer Vision and Robotics (ICCVR 2022), organized by BBD University Lucknow India, during 21 – 22 May 2022.
Inbunden, Engelska, 2024
2 466 kr
Skickas inom 10-15 vardagar
This edited book is a compilation of the chapters on the recent advances made in the field of disease management in various field crops.
Inbunden, Engelska, 2025
2 359 kr
Skickas inom 10-15 vardagar
This book is dealing with the crop protection in soybean covering all the major insects’ pests, diseases and weeds infesting soybean.
Inbunden, Engelska, 2025
2 466 kr
Skickas inom 10-15 vardagar
This contributed volume offers a comprehensive overview of the physiology, production, and processing of soybean, focusing on the latest advancements in soybean production technology. Updated knowledge on key topics related to soybean production is crucial for adopting sustainable crop production strategies.
Inbunden, Engelska, 2024
2 359 kr
Skickas inom 10-15 vardagar
This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency.
Häftad, Engelska, 2025
2 359 kr
Skickas inom 10-15 vardagar
This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency.