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[null,"1.","Record Nr.",null,"UNINA9910564698903321"]
[null,null,"Titolo",null,"Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis / / edited by Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Mallick, Khaled Shaalan"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"981-19-1076-6"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (503 pages)"]
[null,null,"Collana",null,"Studies in Computational Intelligence, , 1860-9503 ; ; 1024"]
[null,null,"Disciplina",null,"610.28563"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Virtual reality"]
[{"$nulls":4},"Augmented reality"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Medical informatics"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Virtual and Augmented Reality"]
[{"$nulls":4},"Big Data"]
[{"$nulls":4},"Health Informatics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Chapter 1. A bibliometric analysis on the role of artificial intelligence in healthcare -- Chapter 2. Supervised Intelligent Clinical Approach for Breast Cancer Tumour Categorisation -- Chapter 3. Health Monitoring and Integrated Wearables -- Chapter 4. A Comprehensive Review Analysis of Alzheimer Disorder using Machine Learning Approach -- Chapter 5. Machine Learning Techniques in Medical Image: A Short Review -- Chapter 6. Analysis of Diabetic Retinopathy Detection Techniques using CNN Models -- Chapter 7. Experimental Evaluation Of Brain Tumor Image Segmentation and Detection Using CNN Model -- Chapter 8. Effective Deep Learning Algorithms for Personalized Healthcare Services -- Chapter 9. Automatic lung carcinoma identification and classification in CT images using CNN deep learning model -- Chapter 10. Augmented Intelligence: Deep Learning Models for Healthcare -- Chapter 11. Sentiment analysis and emotion "]
[{"$nulls":4},"detection with healthcare perspective -- Chapter 12. Augmented Intelligence in Mentalhealthcare: Sentiment analysis & emotion detection with healthcare perspective -- Chapter 13. NLP applications for big data analytics within healthcare -- Chapter 14. Cognitive Computing Driven Healthcare: A Precise Study -- Chapter 15. Cognitive Techniques for Brain Disorder Management: A Future Trend -- Chapter 16. Relevance of Blockchain in Revolutionizing Health Records -- Chapter 17. A Systematic Review on Blockchain Technology: Concepts, Applications, and Prospects in Healthcare -- Chapter 18. Integrated Machine Learning Models for Enhanced Security of Healthcare data -- Chapter 19. Symptoms based Biometric Pattern Detection and Recognition -- Chapter 20. Time Series Analysis of COVID 19 waves in India for Social Good -- Chapter 21. Detection of COVID-19 using A Multi-Scale Deep Learning Network:Covid-MSNet -- Chapter 22. Immersive Technologies in the Healthcare Space -- Chapter 23. Artificial Intelligence in Telemedicine: A Brief Survey -- Chapter 24. Infectious Diseases Reporting System Using Naïve Bayes Classification Algorithm -- Chapter 25. A Comprehensive Study of Explainable Artificial Intelligence In Healthcare."]
[null,null,null,"Sommario/riassunto","The book discusses how augmented intelligence can increase the efficiency and speed of diagnosis in healthcare organizations. The concept of augmented intelligence can reflect the enhanced capabilities of human decision-making in clinical settings when augmented with computation systems and methods. It includes real-life case studies highlighting impact of augmented intelligence in health care. The book offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in healthcare challenges. It presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It also presents specific applications of augmented intelligence in health care, and architectural models and frameworks-based augmented solutions."]