03019nam 2200637 450 991066985400332120210209120641.01-74224-700-81-74224-181-6(CKB)3710000000217997(EBL)1766778(SSID)ssj0001399530(PQKBManifestationID)11797308(PQKBTitleCode)TC0001399530(PQKBWorkID)11458931(PQKB)11553343(MiAaPQ)EBC1884606(MiAaPQ)EBC1766778(Au-PeEL)EBL1766778(OCoLC)879460841(EXLCZ)99371000000021799720140819h20142014 uy 0engur|n|---|||||txtccrAnzac the unauthorised biography /Carolyn HolbrookSydney, [Australia] :NewSouth,2014.©20141 online resource (280 p.)Description based upon print version of record.1-322-45188-5 1-74223-407-0 Includes bibliographical references and index.Contents; Foreword; Acknowledgments; The Anzac Ascendancy; 1. Before 1914: Nationalism and War; 2. 1916-1936: Early Histories of the Great War; 3. 1920s-1930s: The Great War in Australian Literature; 4. 1940s-1960s: Marxism and Memory of the Great War; 5. 1965-1985: The fall and Rise of Memory of the Great War; 6. 1980s-present: The Great War as Family History; 7. Since 1990: Politicians and Commemoration of the Great War; Epilogue; Notes; Select bibliography; IndexRaise a glass for an Anzac. Run for an Anzac. Camp under the stars for an Anzac. Is there anything Australians won't do to keep the Anzac legend at the centre of our national story?But standing firm on the other side of the Anzac enthusiasts is a chorus of critics claiming that the appetite for Anzac is militarising our history and indoctrinating our children. So how are we to make sense of this struggle over how we remember the Great War? Anzac, the Unauthorised Biography cuts through the clamour to provide a much-needed historical perspective on the battle over Anzac. It traces how, since 19World War, 1914-1918HistoriographyWorld War, 1914-1918AustraliaHistoriographyWorld War, 1914-1918Participation, AustralianNationalism and collective memoryAustraliaAustraliaHistoriographyElectronic books.World War, 1914-1918Historiography.World War, 1914-1918Historiography.World War, 1914-1918Participation, Australian.Nationalism and collective memory940.109235Holbrook Carolyn1168913MiAaPQMiAaPQMiAaPQBOOK9910669854003321Anzac2721820UNINA04712nam 22007215 450 991048337640332120251113181706.03-030-33966-110.1007/978-3-030-33966-1(CKB)4100000009844795(MiAaPQ)EBC5979117(DE-He213)978-3-030-33966-1(PPN)258866721(PPN)243767676(EXLCZ)99410000000984479520191114d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep Learning Techniques for Biomedical and Health Informatics /edited by Sujata Dash, Biswa Ranjan Acharya, Mamta Mittal, Ajith Abraham, Arpad Kelemen1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (395 pages)Studies in Big Data,2197-6511 ;683-030-33965-3 MedNLU: Natural Language Understander for Medical Texts -- Deep Learning Based Biomedical Named Entity Recognition Systems -- Disambiguation Model for Bio-Medical Named Entity Recognition -- Applications of Deep Learning in Healthcare and Biomedicine -- Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcare -- Review of Machine Learning and Deep Learning based Recommender Systems for Health Informatics -- Deep Learning and Explainable AI in Healthcare using EHR -- Deep Learning for Analysis of Electronic Heath Records -- Bioinformatics Using Deep Architecture -- Intelligent, Secure Big Health Data Management using Deep Learning and Blockchain Technology: An Overview -- Malaria Disease Detection using CNN Technique with SGD, RMSprop and ADAM Optimizers -- Deep Reinforcement Learning based Personalized Health Recommendations.This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields. .Studies in Big Data,2197-6511 ;68Computational intelligenceEngineeringData processingBiomedical engineeringBig dataArtificial intelligenceComputational IntelligenceData EngineeringBiomedical Engineering and BioengineeringBig DataArtificial IntelligenceComputational intelligence.EngineeringData processing.Biomedical engineering.Big data.Artificial intelligence.Computational Intelligence.Data Engineering.Biomedical Engineering and Bioengineering.Big Data.Artificial Intelligence.006.31Dash Sujataedthttp://id.loc.gov/vocabulary/relators/edtAcharya Biswa Ranjanedthttp://id.loc.gov/vocabulary/relators/edtMittal Mamtaedthttp://id.loc.gov/vocabulary/relators/edtAbraham Ajithedthttp://id.loc.gov/vocabulary/relators/edtKelemen Arpadedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910483376403321Deep Learning Techniques for Biomedical and Health Informatics2853799UNINA