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Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications - Couverture rigide
Synopsis
Advances in Machine Learning Approaches in Cancer Prognosis.- Data Analysis on Cancer Disease using Machine Learning Techniques.- Learning from multiple modalities of imaging data for cancer detection/diagnosis .- Neural Network for Lung Cancer diagnosis.- Improved Thyroid Disease Prediction Model Using Data Mining Techniques with Outlier Detection.- Automated Breast Cancer Diagnosis Based on Neural Network Algorithms.
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Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications (Intelligent Systems Reference Library, 204)
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Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications (Intelligent Systems Reference Library, 204)
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Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications (Intelligent Systems Reference Library, 204)
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Advanced Machine Learning Approaches in Cancer Prognosis
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Springer International Publishing Mai 2021, 2021
ISBN 10 : 303071974X
ISBN 13 : 9783030719746
Neuf
Couverture rigide
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic,and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learninghaveproved its vast range of significance and,provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D.students, postdocs, and anyone interested in the subjects discussed. 476 pp. Englisch. N° de réf. du vendeur 9783030719746
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Advanced Machine Learning Approaches in Cancer Prognosis
Edité par
Springer International Publishing, 2021
ISBN 10 : 303071974X
ISBN 13 : 9783030719746
Neuf
Couverture rigide
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Discusses all types of cancer diseases information with their detection, solution, and preventionPresents advanced machine learning approaches spanning the areas of neural networks, fuzzy logic, connectionist systems, genetic algori. N° de réf. du vendeur 458553423
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Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications (Intelligent Systems Reference Library, 204) 1st ed. 2021 Edition
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Advanced Machine Learning Approaches in Cancer Prognosis
ISBN 10 : 303071974X
ISBN 13 : 9783030719746
Neuf
Couverture rigide
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Buch. Etat : Neu. Neuware -This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 476 pp. Englisch. N° de réf. du vendeur 9783030719746
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Advanced Machine Learning Approaches in Cancer Prognosis : Challenges and Applications
Edité par
Springer International Publishing, 2021
ISBN 10 : 303071974X
ISBN 13 : 9783030719746
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic,and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learninghaveproved its vast range of significance and,provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D.students, postdocs, and anyone interested in the subjects discussed. N° de réf. du vendeur 9783030719746
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Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications (Intelligent Systems Reference Library, 204) 1st ed. 2021 Edition
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Advanced Machine Learning Approaches in Cancer Prognosis: Challenges and Applications (Intelligent Systems Reference Library, 204) 1st ed. 2021 Edition
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