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Verlag: Taylor & Francis Ltd (Sales) Okt 2024, 2024
ISBN 10: 1032358262 ISBN 13: 9781032358260
Sprache: Englisch
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Verlag: Elsevier Science & Technology|Academic Press, 2022
ISBN 10: 0323902405 ISBN 13: 9780323902403
Sprache: Englisch
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In den WarenkorbZustand: New. System Assurances: Modeling and Management updates on system assurance and performance methods using advanced analytics and understanding of software reliability growth modeling from today s debugging team s point-of-view, along with information on p.
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Verlag: Elsevier Science & Technology, 2022
ISBN 10: 0323902405 ISBN 13: 9780323902403
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ISBN 10: 0323902405 ISBN 13: 9780323902403
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Verlag: Taylor & Francis Ltd, London, 2024
ISBN 10: 1032358262 ISBN 13: 9781032358260
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms.Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment.Highlights include:Artificial Intelligence in treating and diagnosing schizophreniaAn analysis of MLs and DLs financial effect on healthcareAn XGBoost-based classification method for breast cancer classificationUsing ML to predict squamous diseasesML and DL applications in genomics and proteomicsApplying ML and DL to biological data This book provides readers a comprehensive understanding of the application of machine Learning and deep Learning in proteomics, genomics, microarrays, text mining and related fields. The key objective is to provide machine learning applications to biological science problems, focusing on problems related to bioinformatics. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Verlag: Elsevier Science & Technology, 2022
ISBN 10: 0323902405 ISBN 13: 9780323902403
Sprache: Englisch
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Verlag: Taylor & Francis Ltd, London, 2024
ISBN 10: 1032358262 ISBN 13: 9781032358260
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms.Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment.Highlights include:Artificial Intelligence in treating and diagnosing schizophreniaAn analysis of MLs and DLs financial effect on healthcareAn XGBoost-based classification method for breast cancer classificationUsing ML to predict squamous diseasesML and DL applications in genomics and proteomicsApplying ML and DL to biological data This book provides readers a comprehensive understanding of the application of machine Learning and deep Learning in proteomics, genomics, microarrays, text mining and related fields. The key objective is to provide machine learning applications to biological science problems, focusing on problems related to bioinformatics. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Verlag: Elsevier Science and Technology, GB, 2022
ISBN 10: 0323902405 ISBN 13: 9780323902403
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: New. System Assurances: Modeling and Management updates on system assurance and performance methods using advanced analytics and understanding of software reliability growth modeling from today's debugging team's point-of-view, along with information on preventive and predictive maintenance and the efficient use of testing resources. The book presents the rapidly growing application areas of systems and software modeling, including intelligent synthetic characters, human-machine interface, menu generators, user acceptance analysis, picture archiving and software systems. Students, research scholars, academicians, scientists and industry practitioners will benefit from the book as it provides better insights into modern related global trends, issues and practices.