Hardcover. In today's data-driven world, understanding how artificial intelligence (AI) makes decisions has become essential. As technologies like computer vision and management analytics emerge, interpreting and trusting AI outputs informs decision-making and strategic planning. Computer vision helps machines visualize and analyze data, transforming areas like manufacturing, healthcare, and security. Management analytics leverages data to guide business operations, optimize performance, and reveal new trends. By exploring the intersection of AI decision-making, computer vision, and management analytics, businesses can better understand how intelligent systems operate and how to harness their power responsibly and effectively. Understanding AI Decisions, Computer Vision, and Management Analytics explores the critical aspects of interpretability and decision-making in AI models, focusing on their applications in computer vision and management analytics. It examines the fundamentals of AI and machine learning, advanced techniques for interpreting computer vision models, and the integration of AI insights into management decision-making processes, offering a holistic view of how AI can be leveraged to enhance business intelligence and operational efficiency. This book covers topics such as data analysis, neural networks, and smart logistics, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists. "The primary objective of this book is to provide an in-depth exploration of Artificial Intelligence (AI) with a specific focus on its capabilities in decision-making, computer vision, and management analytics"-- Provided by publisher. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Bibliographic Details

Title: Understanding AI Decisions, Computer Vision, and Management Analytics (Hardcover)
Publisher: Igi Global Scientific Publishing
Publication Date: 2026
Language: English
ISBN 13: 9798369383278
Binding: Hardcover
Condition: new

About this title

Synopsis

In today's data-driven world, understanding how artificial intelligence (AI) makes decisions has become essential. As technologies like computer vision and management analytics emerge, interpreting and trusting AI outputs informs decision-making and strategic planning. Computer vision helps machines visualize and analyze data, transforming areas like manufacturing, healthcare, and security. Management analytics leverages data to guide business operations, optimize performance, and reveal new trends. By exploring the intersection of AI decision-making, computer vision, and management analytics, businesses can better understand how intelligent systems operate and how to harness their power responsibly and effectively. Understanding AI Decisions, Computer Vision, and Management Analytics explores the critical aspects of interpretability and decision-making in AI models, focusing on their applications in computer vision and management analytics. It examines the fundamentals of AI and machine learning, advanced techniques for interpreting computer vision models, and the integration of AI insights into management decision-making processes, offering a holistic view of how AI can be leveraged to enhance business intelligence and operational efficiency. This book covers topics such as data analysis, neural networks, and smart logistics, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists.

About the Author

Swadha Gupta is a Data Scientist at Shiratech Knowtion GmbH in Germany. She completed her doctorate degree at Thapar Institute of Engineering and Technology in India in 2023. Swadha received her Bachelor's degree in Computer Science and Engineering from Shri Mata Vaishno Devi University (SMVDU) in India in 2013 and her Master's degree from Chandigarh University India in 2015. She has authored numerous papers published in prestigious journals and top-tier conferences, focusing on research areas such as face recognition, Online learning, Machine Learning, and Deep Learning

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