Krishna Arun Kumar (36 results)

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New. In English.

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Soft cover. Condition: Good. 489 p. chips and tears to top & bottom of spine, unmarked, good binding.

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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

hardcover. Condition: Fine.

Condition: As New. Unread book in perfect condition.

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Condition: As New. Unread book in perfect condition.

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Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: New.

Condition: New.

Hardback. Condition: New. The role of artificial intelligence is crucial in the domain of Earth Observation (EO) data analysis. Deep learning-based approaches have improved accuracy, but they have affected the reliability and transparency of EO data. It is critical to improve the explainability of EO data analysis algorithms and complex deep learning models to ensure the quality of spatial decisions. This book discusses the various advancements in Explainable AI and investigates their suitability for various EO data analyses offering best practices for implementing algorithms that facilitate big and efficient data processing. It lays the foundation of Explainable EO and helps readers build trustworthy, secure, and robust EO systems.Features:Examines explainability of algorithms from the aspect of generalizability and reliabilityReviews state-of-the-art explainability strategies related to the preprocessing algorithmsProvides explanations for specific evaluation metrics of various EO data processing and preprocessing algorithmsDiscusses explainable ante-hoc and post-hoc approaches for EO data analysisServes as a foundational reference for developing future EO data processing strategiesAddresses the key challenges in making EO data processing algorithms interpretable and offers insights for the future of explainable EO data processingThis book is intended for graduate students, researchers and academics in computer and data science, machine learning, and image processing, as well as professionals in geospatial data science using GIS and remote sensing in Earth and environmental sciences.…

Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Condition: Brand New. 320 pages. 9.18x6.12x9.21 inches. In Stock.

Hardback. Condition: New. The role of artificial intelligence is crucial in the domain of Earth Observation (EO) data analysis. Deep learning-based approaches have improved accuracy, but they have affected the reliability and transparency of EO data. It is critical to improve the explainability of EO data analysis algorithms and complex deep learning models to ensure the quality of spatial decisions. This book discusses the various advancements in Explainable AI and investigates their suitability for various EO data analyses offering best practices for implementing algorithms that facilitate big and efficient data processing. It lays the foundation of Explainable EO and helps readers build trustworthy, secure, and robust EO systems.Features:Examines explainability of algorithms from the aspect of generalizability and reliabilityReviews state-of-the-art explainability strategies related to the preprocessing algorithmsProvides explanations for specific evaluation metrics of various EO data processing and preprocessing algorithmsDiscusses explainable ante-hoc and post-hoc approaches for EO data analysisServes as a foundational reference for developing future EO data processing strategiesAddresses the key challenges in making EO data processing algorithms interpretable and offers insights for the future of explainable EO data processingThis book is intended for graduate students, researchers and academics in computer and data science, machine learning, and image processing, as well as professionals in geospatial data science using GIS and remote sensing in Earth and environmental sciences.…

Condition: New. In English.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a compilation of selected papers presented in the International Conference on the theme 'Wood is Good: Current Trends and Future Prospects in Wood'. The contents of the book deal with recent innovations, trends and challenges in wood science and are grouped in five distinct sections. They cover a wide range of topics like wood variability, processing and utilization, wood protection, wood-based composites, wood energy and the role of wood in mitigating climate change. With the ever increasing human population and growing demand for wood, this book offers valuable insights for better understanding and efficient utilization of this wonderful gift of nature. This book will be useful to researchers, professionals, and policy makers involved in forestry and wood related areas.…

Hardcover. Condition: Brand New. 480 pages. 9.50x6.25x1.25 inches. In Stock.

orig. wrappers. (38),489 pp., 24x16cm. Official Correspondence of David Ochterlony relating to the Rajputana Presidency. Spine tears. One page smudged. Good.

Hardcover. Condition: new. Hardcover. The role of artificial intelligence is crucial in the domain of Earth Observation (EO) data analysis. Deep learning-based approaches have improved accuracy, but they have affected the reliability and transparency of EO data. It is critical to improve the explainability of EO data analysis algorithms and complex deep learning models to ensure the quality of spatial decisions. This book discusses the various advancements in Explainable AI and investigates their suitability for various EO data analyses offering best practices for implementing algorithms that facilitate big and efficient data processing. It lays the foundation of Explainable EO and helps readers build trustworthy, secure, and robust EO systems.Features:Examines explainability of algorithms from the aspect of generalizability and reliabilityReviews state-of-the-art explainability strategies related to the preprocessing algorithmsProvides explanations for specific evaluation metrics of various EO data processing and preprocessing algorithmsDiscusses explainable ante-hoc and post-hoc approaches for EO data analysisServes as a foundational reference for developing future EO data processing strategiesAddresses the key challenges in making EO data processing algorithms interpretable and offers insights for the future of explainable EO data processingThis book is intended for graduate students, researchers and academics in computer and data science, machine learning, and image processing, as well as professionals in geospatial data science using GIS and remote sensing in Earth and environmental sciences. This book discusses the various advancements in Explainable AI and investigates their suitability for various EO data analyses offering best practices for implementing algorithms that facilitate big and efficient data processing. It lays the foundation of Explainable EO and helps readers build trustworthy, secure, and robust EO systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…