Kooperativer Bibliotheksverbund

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Format: 1 online resource (402 pages)

Edition: 1st ed.

ISBN: 0-443-19414-9

Note: Front Cover -- Deep Learning in Personalized Healthcare and Decision Support -- Deep Learning in Personalized Healthcare and Decision Support -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgments -- 1 - The future of health diagnosis and treatment: an exploration of deep learning frameworks and innovative applica ... -- 1. Introduction -- 2. Computational deep learning frameworks for health monitoring -- 3. Advanced architectures and core concepts of deep learning in smart health -- 4. Comparative analysis of deep learning frameworks for different disease detection -- 5. Advantages of deep learning in smart medical healthcare analytics -- 6. Deep leering applications for disease prediction -- 7. Deep learning in research and development -- 8. Future challenges of deep learning in smart health diagnosis and treatment -- 9. Limitations of deep learning frameworks -- 10. Conclusion and future scope -- References -- 2 - Fermatean fuzzy approach of diseases diagnosis based on new correlation coefficient operators -- 1. Introduction -- 2. Fermatean fuzzy sets and their correlation operators -- 2.1 Fermatean fuzzy sets -- 2.2 Existing correlation operators for Fermatean fuzzy sets -- 3. New Fermatean fuzzy correlation operators -- 3.1 Computational example -- 4. Application example of medical diagnosis -- 5. Conclusion -- References -- 3 - Application of Deep-Q learning in personalized health care Internet of Things ecosystem -- 1. Introduction -- 2. Related work -- 3. Proposed mechanism -- 4. Experimental results -- 5. Future directions -- 6. Conclusion -- References -- 4 - Dia-Glass: a calorie-calculating spectacles for diabetic patients using augmented reality and faster R-CNN -- 1. Introduction -- 2. Related works -- 3. Diabetes: categories, concerns and prevalence -- 3.1 Categories -- 3.2 Concerns -- 3.3 Diabetes prevalence. , 4. System methodology -- 4.1 User information insertion module -- 4.2 Data acquisition through spectacles -- 4.3 Food recognition using faster R-CNN -- 4.4 Calorie estimation -- 4.5 Information rendering and user notification -- 5. Result and analysis -- 5.1 Dataset -- 5.2 Performance analysis -- 6. Conclusion -- References -- 5 - Synthetic medical image augmentation: a GAN-based approach for melanoma skin lesion classification with deep le ... -- 1. Introduction -- 1.1 Background and motivation -- 1.2 Contributions -- 1.3 Related works -- 2. Skin lesion classification methodology -- 2.1 Dataset -- 2.2 CNN architecture with modified VGG16 -- 3. Generation of synthetic skin lesions -- 3.1 Traditional data augmentation -- 3.2 Generative adversarial networks for skin lesion synthesis -- 3.3 Conditional skin lesion synthesis -- 4. Experimental results -- 4.1 Dataset evaluation and performance metrics -- 4.2 Implementation specifications -- 4.3 Training and test data -- 5. Performance comparison -- 6. Conclusion and future work -- 7. Conflict of interest -- References -- 6 - Artificial intelligence representation model for drug-target interaction with contemporary knowledge and develo ... -- 1. Introduction -- 1.1 AI will challenge the status Quo in healthcare -- 2. AI privacy and security challenges -- 2.1 Ensuring transparency, explain ability, and intelligibility -- 2.1.1 Algorithmic fairness and biases -- 2.1.2 Data availability -- 2.1.3 Privacy concerns -- 3. Ensuring transparency, explain ability, and intelligibility -- 3.1 Data availability -- 3.2 Concerns regarding privacy -- 4. Drug discovery and precision medicine with deep learning -- 4.1 Discrimination and unequal treatment -- 4.2 The production of data and its availability -- 4.3 The supervision of quality -- 5. Clinical decision support and predictive analytics. , 5.1 Natural language processing could translate EHR jargon for patients -- 5.2 Faster drug screening in the future -- 5.3 Machine learning predictions rely on input data -- 5.4 Connection between quantifiable construction and function -- 5.4.1 Support for clinical decision-making and predictive analytics are included in this section -- 5.4.2 In order to make prescriptive modeling useful, they must be put into practice -- 6. Natural language processing in drug -- 7. Predictive analytics has a wide range of practical applications, including the following -- 7.1 Efforts made to lessen potential dangers to healthcare organizations' security -- 7.2 Future of deep learning in healthcare -- 8. Conclusion -- References -- 7 - Review of fog and edge computing-based smart health care system using deep learning approaches -- 1. Introduction -- 2. Literature review -- 3. Healthcare using artificial intelligence -- 4. Efficient health care system with improved performance -- 4.1 Dataset -- 5. Conclusion -- References -- 8 - Deep learning in healthcare: opportunities, threats, and challenges in a green smart environment solution for s ... -- 1. Introduction -- 1.1 Major findings and motivation -- 1.2 The coal crisis creates the need for alternatives [2] -- 1.3 With minor delays caused by COVID-19, renewable volumes at auction continue to break records [3] -- 2. Green infrastructure measures in the legislature [4] -- 2.1 Approaches within the direction of a green economy -- 2.1.1 An account of the ongoing power situation in Iceland: a global paradigm [5] -- 2.1.2 Saudi Arabia's Vision 2030 [6,7] -- 2.1.3 NEOM [8] -- 2.1.4 Saudi Arabia, Line [9,10] -- 3. Employment creation as part of the sustainable recovery -- 3.1 Organic agriculture has the ability to create jobs [12] -- 3.1.1 Brief explanation -- 4. Carbon power. , 4.1 There are many rehabilitation strategies that have a favorable impact on the environment -- 4.2 Benefits -- 5. Smart buildings [15] -- 6. Climate change disclosure laws [16] -- 7. The action of biodiversity [17] -- 7.1 Smart houses and smart buildings: weather [17] -- 7.1.1 Measures -- 7.1.2 Benefits -- 7.1.3 Concise description -- 8. Case study -- 8.1 Acceptable approved cases of integrating biodiversity in response to COVID-19 and rehabilitation programs [18] -- 8.2 Securing Georgia's forest by space [19,20] -- 8.3 Kazakhstan needs waste management and community well-being technologies [21] -- 9. Future pandemic preparedness [22] -- 9.1 We should be concerned about the legal wildlife trade in order to prevent the next pandemic [23] -- 10. Recent literature -- 10.1 A comparative analysis of data-driven based optimization models for energy-efficient buildings [24] -- 10.2 Machine learning forecasting model for the COVID-19 pandemic in India [25] -- 10.3 AI-based building management and information system with multi-agent topology for an energy-efficient building: toward occu ... -- 11. Conclusions -- 11.1 Advantages -- 11.2 Limitations -- References -- Further reading -- 9 - Hybrid and automated segmentation algorithm for malignant melanoma using chain codes and active contours -- 1. Introduction -- 1.1 Motivation and contribution -- 1.2 Related works -- 1.3 Paper organization -- 2. Materials and methods -- 2.1 Datasets -- 2.2 Image enhancement -- 3. Proposed methodology -- 3.1 Preprocessing phase -- 3.2 h-CEAC segmentation -- 3.2.1 Feature extraction and chain codes -- 3.2.2 Formulation of the chain code -- 3.2.3 Pixel interdependence -- 3.2.4 EDRS and active contours -- 4. Results and discussions -- 4.1 Comparison with existing algorithms -- 5. Conclusion and future scope -- References. , 10 - Development of a predictive model for classifying colorectal cancer using principal component analysis -- 1. Introduction -- 2. Related works -- 3. Methodology -- 3.1 Experimental dataset -- 3.2 Dimensionality reduction tool -- 3.3 Classification -- 3.3.1 Support vector machine -- 3.3.2 K-nearest neighbor -- 3.3.3 Random forest -- 3.4 Research tool -- 3.5 Performance evaluation metrics -- 4. Results and discussions -- 5. Conclusion -- References -- 11 - Using deep learning via long-short-term memory model prediction of COVID-19 situation in India -- 1. Introduction -- 1.1 Research gaps and motivation -- 2. Literature review -- 2.1 Symptoms of COVID-19 -- 3. How to protect yourself from COVID-19 -- 3.1 High-risk groups -- 4. Facts about the vaccine against the COVID-19 -- 4.1 Vaccines in COVID-19 -- 4.2 Here's a peek at some of the initiatives -- 4.3 Immunology and antigen detection for the COVID-19 vaccine -- 4.4 Potential vaccine-related threats -- 4.5 Vaccines have been approved in India -- 4.5.1 Covishield -- 4.5.2 Covaxin -- 4.5.3 Sputnik V -- 5. Materials and methods -- 5.1 Artificial neural network (ANN) -- 5.2 Model of a neuron -- 5.2.1 Recurrent neural network (RNN) -- 6. Results discussion -- 6.1 Top 10 states (confirmed cases and cured cases in Covid-19) -- 7. Conclusion -- References -- 12 - Post-COVID-19 Indian healthcare system: Challenges and solutions -- 1. Creation of robust healthcare system-A nationwide priority and its emergence -- 2. Pandemonium scenes -- 3. Efforts for healthcare system development -- 4. Providing treatment to all amidst difficulties -- 5. Corona warriors and their woes -- 6. Transformation of Indian healthcare sector post COVID-19 -- 7. Healthcare component 1-Hospitals -- 8. Healthcare component 2-Pharmaceutical industry -- 9. Healthcare component 3-Medical devices and equipment. , 10. Healthcare component 4-Diagnostics.

Additional Edition: Print version: Garg, Harish Deep Learning in Personalized Healthcare and Decision Support San Diego : Elsevier Science & Technology,c2023 ISBN 9780443194139

Language: English