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Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032137193 ISBN 13: 9781032137193
Language: English
Seller: Grand Eagle Retail, Mason, OH, U.S.A.
Paperback. Condition: new. Paperback. This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases.The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimers disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering. This reference text covers deep learning methods for detection of various diseases and analysis of medical images for better understanding. It will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032137193 ISBN 13: 9781032137193
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032137193 ISBN 13: 9781032137193
Language: English
Seller: AussieBookSeller, Truganina, VIC, Australia
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Add to basketPaperback. Condition: new. Paperback. This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases.The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimers disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering. This reference text covers deep learning methods for detection of various diseases and analysis of medical images for better understanding. It will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Add to basketPaperback. Condition: Brand New. 168 pages. 9.18x6.12x9.21 inches. In Stock.
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 1032137193 ISBN 13: 9781032137193
Language: English
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Add to basketPaperback. Condition: new. Paperback. This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases.The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimers disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering. This reference text covers deep learning methods for detection of various diseases and analysis of medical images for better understanding. It will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Published by Taylor & Francis Ltd, 2022
ISBN 10: 1032137169 ISBN 13: 9781032137162
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketHardcover. Condition: Brand New. 168 pages. 9.19x6.13x0.59 inches. In Stock.
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Seller: Biblios, Frankfurt am main, HESSE, Germany
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Published by John Wiley & Sons Inc, New York, 2022
ISBN 10: 1119791758 ISBN 13: 9781119791751
Language: English
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Hardcover. Condition: new. Hardcover. Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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