Handbook of Data Science Approaches for Biomedical Engineering Emilia Balas

Description: Handbook of Data Science Approaches for Biomedical Engineering A comprehensive guide to the application of data science analytics for biomedical engineering, clinical and healthcare research and management Valentina Emilia Balas (Edited by), Vijender Kumar Solanki (Edited by), Manju Khari (Edited by), Raghvendra Kumar (Edited by) 9780128183182, Elsevier Science Paperback / softback, published 13 November 2019 318 pages 23.5 x 19 x 2.1 cm, 0.61 kg Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. 1. Analysis of the role and scope of big data analytics with IoT in Healthcare domain 2. Automated human cortical bone haversian canal Histomorphometric comparison system 3. Biomedical Instrument and automation: Automatic Instrumentation in Biomedical Engineering 4. Contribution of IoT and Big Data in Modern Health Care Applications in Smart City Mamata Rath and Vijender 5. Emerging Trends in IoT and Big Data Analytics for Biomedical and Healthcare Technologies 6. Recent Advances on Big Data Analysis for Malaria Prediction and Various Diagnosis Methodologies 7. Semantic Interoperability in IoT and Big Data for Healthcare: A Collaborative Approach 8. Why Big Data, and what its: Basics-to Advance Big Data Journey for medical industry 9. Semi-Supervised Fuzzy Clustering Methods for X-Ray Image Segmentation Subject Areas: Biomedical engineering [MQW]

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