Computational Learning Approaches to Data Analytics in Biomedical Applications (eBook)

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. - Includes an overview of data analytics in biomedical applications and current challenges - Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices - Provides complete coverage of computational and statistical analysis tools for biomedical data analysis - Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor

Dr. Al-Jabery is a Deputy-Chief engineer in Barah Oil Company. He obtained his Ph.D. in Electrical and Computer engineering from Missouri S&T in 2018, his BS, and M.Sc. in Computer Engineering at the University of Basrah in Iraq in 2005 and 2009 respectively. He has more than 6 years of experience as an IT engineer. He worked for ExxonMobil, South Oil Company-Iraq, and International Organization of Migration (IOM). His research interests are Reinforcement learning, Clustering, Data analysis, Power optimization, and Artificial Neural network.

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Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. - Includes an overview of data analytics in biomedical applications and current challenges- Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices- Provides complete coverage of computational and statistical analysis tools for biomedical data analysis- Presents hands-on training on the use of Python libraries, MATLAB tools, WEKA, SAP-HANA and R/Bioconductor