Mehul S. Raval – författare
962 kr
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918 kr
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This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering.
This book will benefit readers in the following ways:
Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care
Investigates bridges between computer scientists and physicians being built with XAI
Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent
Initiates discussions on human-AI relationships in health care
Unites learning for privacy preservation in health care
918 kr
Läs direkt efter köp
This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering.
This book will benefit readers in the following ways:
Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care
Investigates bridges between computer scientists and physicians being built with XAI
Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent
Initiates discussions on human-AI relationships in health care
Unites learning for privacy preservation in health care
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Harnessing Data Science for Sustainable Agriculture and Natural Resource Management
2 391 kr
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This book is a comprehensive resource that handles the issues of sustainable agriculture and natural resource management, aligned with the United Nations'' Sustainable Development Goals (SDGs). The book is organized into five sections, Understanding the Problem, Data Collection and Cleaning, Exploratory Data Analysis and Visualization, Model Building, and Model Deployment. Each section covers a critical aspect of data science in this context and addresses specific SDGs 2—zero hunger, 6—clean water and sanitation, 12—responsible consumption and production, 13—climate action, and 15—Life on land. The organized sections are arranged to seamlessly follow the data science pipeline and provide practical guidance from problem understanding to its model deployment and stakeholder management. The book is useful for researchers, students, practitioners, and policymakers.
Digital Ecosystem for Innovation in Agriculture
1 849 kr
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2 373 kr
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Digital Ecosystem for Innovation in Agriculture
1 849 kr
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