Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features:Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosisAssists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equationsInvolves exceptional subject coverage and includes the principles needed to understand deep learning

Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features:Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosisAssists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equationsInvolves exceptional subject coverage and includes the principles needed to understand deep learning

Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features:

Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosis Assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equations Involves exceptional subject coverage and includes the principles needed to understand deep learning

Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features:

Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosis Assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equations Involves exceptional subject coverage and includes the principles needed to understand deep learning

Making a study of East Germanic iron smelting in the Roman period, this open access book gives an in-depth account of the development of metallurgy in the Przeworsk Culture. Its two main ironmaking centres – the Holy Cross Mountains and Masovia – were the largest iron production regions beyond the borders of the Roman Empire. Apart from serving local demand – where it was used for artefacts of many kinds, which are abundantly found in grave furnishings – a considerable part of Przeworsk Culture iron is likely to have been exported to other territories. Despite more than 60 years of research and scholarship on Przeworsk Culture iron metallurgy, this is the first book to carry out smelting slag and ore analyses using the most up-to-date methods available in archaeological science. More specifically, this study also applies a variety of statistical methods to the results of the elemental analyses of nearly 270 smelting slag and ore samples from its territory. Offering an overview of statistical approaches used in iron provenance studies and a detailed step-by-step research methodology, this book paves the way for future studies, including artefact provenance analyses using the latest archaeometric methods.The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Research reported in this book was funded by the National Science Centre Poland. Open access was funded by Grzegorz Zabinski.

Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features:Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosisAssists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equationsInvolves exceptional subject coverage and includes the principles needed to understand deep learning

Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features:Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosisAssists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equationsInvolves exceptional subject coverage and includes the principles needed to understand deep learning

Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features:

Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosis Assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equations Involves exceptional subject coverage and includes the principles needed to understand deep learning

Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features:

Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosis Assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equations Involves exceptional subject coverage and includes the principles needed to understand deep learning

Making a study of East Germanic iron smelting in the Roman period, this open access book gives an in-depth account of the development of metallurgy in the Przeworsk Culture. Its two main ironmaking centres – the Holy Cross Mountains and Masovia – were the largest iron production regions beyond the borders of the Roman Empire. Apart from serving local demand – where it was used for artefacts of many kinds, which are abundantly found in grave furnishings – a considerable part of Przeworsk Culture iron is likely to have been exported to other territories. Despite more than 60 years of research and scholarship on Przeworsk Culture iron metallurgy, this is the first book to carry out smelting slag and ore analyses using the most up-to-date methods available in archaeological science. More specifically, this study also applies a variety of statistical methods to the results of the elemental analyses of nearly 270 smelting slag and ore samples from its territory. Offering an overview of statistical approaches used in iron provenance studies and a detailed step-by-step research methodology, this book paves the way for future studies, including artefact provenance analyses using the latest archaeometric methods.The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Research reported in this book was funded by the National Science Centre Poland. Open access was funded by Grzegorz Zabinski.