Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis
Business Science Reference (Verlag)
978-1-7998-7316-7 (ISBN)
Cancer continues to be a growing problem as it is the foremost cause of death worldwide, killing millions of people each year. The number of people battling cancer continues to increase, owing to different reasons, such as lifestyle choices. Clinically, determining the cause of cancer is very challenging and often inaccurate. Incorporating efficient and accurate algorithms to detect cancer cases is becoming increasingly beneficial for scientists in computer science and healthcare, as well as a long-term benefit for doctors, patients, clinic practitioners, and more. Specifically, an automation of computation in machine learning could be a solution in the next generation of big data science technology.
Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis presents algorithms that have been developed to evaluate big data approaches and cancer research. The chapters include artificial intelligence and machine learning approaches, as well as case studies to solve the predictive issues in colon cancer research. This book includes concepts and techniques used to run tasks in an automated manner with the intent to improve better accuracy in comparison with previous studies and methods. This book also covers the processes of research design, development, and outcome analytics in this field.
Doctors, IT consultants, IT specialists, medical software professionals, data scientists, researchers, computer scientists, healthcare practitioners, academicians, and students can benefit from this critical resource.
Zhongyu (Joan) Lu is Professor in Informatics at the University of Huddersfield (UK). Her extensive research covers information access, retrieval and visualization, XML technology, object oriented technologies, agent technology, data management systems, security issues and Internet computing. She has been an invited speaker for industrial-oriented events and published 5 academic books and more than 160 papers. Professor Lu has acted as the founder and a program chair for the International XML Technology workshop and XMLTech (USA) for 11 years (2003-2011). She also serves as Chair of 5 separate international conferences, is a regular reviewer for several international journals, and a committee member for 16 international conferences. She specializes in XML technology and mobile computing with image retrieval through the latest wireless devices. Professor Lu serves as a member of the British Computer Society (BCS), BCS examiner of Advanced Database Management Systems, and fellow of the Higher Education Academy (UK). She is a founder and Editor-in-Chief for the International Journal of Information Retrieval Research. Dr Xu has been Senior Lecturer in Mechanical/Automotive Engineering at the School of Computing and Engineering, since June 2013 to present. He is a specialist in computational creep damage mechanics. Previously, Dr Xu was Senior Lecturer in Mechanical/Manufacturing Engineering in the School of Science and Engineering at Teesside University from 2006 to 2013. In this role, Dr Xu supervised a number of PhD research projects and completed over 15 consultancy and grant applications. In addition he has held academic appointments at the Swansea Institute of Higher Education and as a Research Fellow and Senior Research Fellow at the University of Huddersfield and Research Associate at UMIST, Manchester, UK. Dr Xu's research work mainly on the computational modelling and analysis for engineering and his work has been cited worldwide by researchers in eight nations including China, the USA, Germany, India, Iran, Russia et al. Further information can be found at University website: https://www.hud.ac.uk/ourstaff/profile/index.php?staffid=1212
| Erscheinungsdatum | 15.01.2021 |
|---|---|
| Sprache | englisch |
| Maße | 216 x 279 mm |
| Gewicht | 633 g |
| Themenwelt | Medizin / Pharmazie ► Medizinische Fachgebiete ► Onkologie |
| ISBN-10 | 1-7998-7316-1 / 1799873161 |
| ISBN-13 | 978-1-7998-7316-7 / 9781799873167 |
| Zustand | Neuware |
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