Big data analytics digital (47 Ergebnisse)

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Taschenbuch. Zustand: Neu. Neuware - The book is about use of AI in Big Data Analytics and has the potential for positive impact and global implications in healthcare. Future details on the use of Big Data in medical facilities, the definition of strategies adopted by medical facilities to promote and implement such solutions, as well as the benefits they gain from the use of Big Data analysis and how the perspectives in this area are seen. The interesting part in the book is How AI-powered facial emotion recognition device redefines patient care which is a device that is developed by the author which is going to be ground breaking in terms of technology.…

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Zustand: New. This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations. Series: Business in the Digital Economy. Num Pages: 272 pages, biography. BIC Classification: KCM; KJC; KJMV3; KJQ; KJS; KJSM. Category: (G) General (US: Trade). Dimension: 161 x 239 x 17. Weight in Grams: 412. . 2014. 1st ed. 2014. paperback. . . . . Books ship from the US and Ireland.…

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Hardcover. Zustand: Brand New. 213 pages. 9.25x6.00x0.75 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc. For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses. This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent 'complexity' and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it 'smart' is of paramount importance in agricultural research. Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method. Crafting an efficient optimization method raises three pivotal questions: 1.) What represents the best approach to optimization 2.) How can we achieve a more optimal optimization 3.) Is it possible to demand 'more optimal machine learning,' exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture This book details the foundations of the plant physiology-informed machine learning (PPIML) and the principle of tail matching (POTM) framework. It is the 9th title of the 'Agriculture Automation and Control' book series published by Springer.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc. For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses. This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent 'complexity' and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it 'smart' is of paramount importance in agricultural research. Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method. Crafting an efficient optimization method raises three pivotal questions: 1.) What represents the best approach to optimization 2.) How can we achieve a more optimal optimization 3.) Is it possible to demand 'more optimal machine learning,' exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture This book details the foundations of the plant physiology-informed machine learning (PPIML) and the principle of tail matching (POTM) framework. It is the 9th title of the 'Agriculture Automation and Control' book series published by Springer.…