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    1. Naturvetenskap och teknik
    2. Teknik och industri
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    Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

    Concepts, Methodologies, Tools and Applications

    AvSunil Kumar Dhal,Subhendu Kumar Pani

    Inbunden, Engelska, 2022

    Del i serien Advances in Intelligent and Scientific Computing

    1 935 kr

    Skickas . Fri frakt över 249 kr.

    Beskrivning

    BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

    Produktinformation

    • Utgivningsdatum:2022-08-24
    • Mått:10 x 10 x 10 mm
    • Vikt:454 g
    • Format:Inbunden
    • Språk:Engelska
    • Serie:Advances in Intelligent and Scientific Computing
    • Antal sidor:352
    • Förlag:John Wiley & Sons Inc
    • ISBN:9781119791737

    Utforska kategorier

    • Biokemisk teknik inom Naturvetenskap och teknik
    • Artificiell intelligens inom Data och IT

    Mer om författaren

    Sunil Kumar Dhal, PhD, is a computer scientist and is Head of Department and professor in the Faculty of Management, Sri Sri University, India. He has more than 20 years of teaching experience with more than 60 international publications including eight books and two patents.Subhendu Kumar Pani, PhD, is a professor in the Department of Computer Science & Engineering, Orissa Engineering College (OEC) Bhubaneswar, India. He has more than 15 years of teaching and research experience and has published more than 50 international journal articles as well as five authored books, 12 edited books, and eight patents. Srinivas Prasad, PhD, is a professor in the Department of Computer Science and Engineering at GITAM University, Visakhapatnam, India. He has more than 20 years of teaching experience and published more than 60 publications which include journal articles, conference papers, edited volumes, and book chapters. Sudhir Kumar Mohapatra, PhD, is an associate professor at Addis Ababa Science & Technology University, Addis Ababa, Ethiopia. Besides 10 years of teaching and research, he spent five years in software development in the banking and education domains.

    Innehållsförteckning

    • Preface xiii1 An Introduction to Big Data Analytics Techniques in Healthcare 1Anil Audumbar Pise1.1 Introduction 11.2 Big Data in Healthcare 31.3 Areas of Big Data Analytics in Medicine 51.4 Healthcare as a Big Data Repository 91.5 Applications of Healthcare Big Data 101.6 Challenges in Big Data Analytics 161.7 Big Data Privacy and Security 171.8 Conclusion 181.9 Future Work 182 Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia 21Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam and Mohammed Siddique2.1 Introduction 222.2 Literature Review 232.3 Methodology and Data Source 252.4 Implementation and Results 282.5 Conclusion 443 Pre-Trained CNN Models in Early Alzheimer's Prediction Using Post-Processed MRI 47Kalyani Gunda and Pradeepini Gera3.1 Introduction 483.2 Experimental Study 513.3 Data Exploration 553.4 OASIS Dataset Pre-Processing 613.5 Alzheimer's 4-Class-MRI Features Extraction 693.6 Alzheimer 4-Class MRI Image Dataset 693.7 RMSProp (Root Mean Square Propagation) 803.8 Activation Function 813.9 Batch Normalization 813.10 Dropout 813.11 Result--I 823.12 Conclusion and Future Work 894 Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging 97Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal4.1 Introduction 984.2 Basics of Proposed Methods 1004.3 Experimental Results and Discussion 1074.4 Conclusion 1155 Analysis of Healthcare Systems Using Computational Approaches 119Hemanta Kumar Bhuyan and Subhendu Kumar Pani5.1 Introduction 1205.2 AI & ML Analysis in Health Systems 1245.3 Healthcare Intellectual Approaches 1275.4 Precision Approaches to Medicine 1335.5 Methodology of AI, ML With Healthcare Examples 1345.6 Big Analytic Data Tools 1365.7 Discussion 1415.8 Conclusion 1426 Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy 147Shrikaant Kulkarni6.1 Introduction 1486.2 AI Methods 1496.3 Turing Test 1566.4 Barriers to Technologies 1576.5 Advantages of AI for Behavioral & Mental Healthcare 1576.6 Enhanced Self-Care & Access to Care 1586.7 Other Considerations 1606.8 Expert Systems in Mental & Behavioral Healthcare 1616.9 Dynamical Approaches to Clinical AI and Expert Systems 1656.10 Conclusion 1736.11 Future Prospects 1757 A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19) 187Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad and Mukkamala S.N.V. Jitendra7.1 Introduction 1887.2 Related Work 1897.3 Proposed Frameworks 1907.4 Results and Discussion 1947.5 Conclusion 2018 An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information 205Sowjanya Naidu K. and Srinivasa L. Chakravarthy8.1 Introduction 2068.2 Related Work 2128.3 Need for Blockchain in Healthcare 2168.4 Proposed Frameworks 2198.5 Use Cases 2238.6 Discussions 2298.7 Challenges and Limitations 2318.8 Future Work 2318.9 Conclusion 2329 An Epidemic Graph's Modeling Application to the COVID-19 Outbreak 237Hemanta Kumar Bhuyan and Subhendu Kumar Pani9.1 Introduction 2379.2 Related Work 2399.3 Theoretical Approaches 2409.4 Frameworks 2439.5 Evaluation of COVID-19 Outbreak 2469.6 Conclusions and Future Works 25010 Big Data and Data Mining in e-Health: Legal Issues and Challenges 257Amita Verma and Arpit BansalObject of Study 25710.1 Introduction 25810.2 Big Data and Data Mining in e-Health 26010.3 Big Data and e-Health in India 26210.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health 26310.5 Big Data and Issues of Privacy in e-Health 27110.6 Conclusion and Suggestions 27211 Basic Scientific and Clinical Applications 275Manna Sheela Rani Chetty and Kiran Babu C. V.11.1 Introduction 27511.2 Case Study-1: Continual Learning Using ML for Clinical pplications 28311.3 Case Study-2 28611.4 Case Study-3: ML Will Improve the Radiology Patient Experience 28911.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization 29211.6 Case Study-5: ML will Benefit All Medical Imaging 'ologies' 29511.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data 29811.8 Conclusion 30012 Healthcare Branding Through Service Quality 305Saraju Prasad and Sunil Dhal12.1 Introduction to Healthcare 30512.2 Quality in Healthcare 30712.3 Service Quality 31112.4 Conclusion and Road Ahead 315References 316Index 321
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