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System design for epidemics using machine learning and deep learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	System design for epidemics using machine learning and deep learningedited by G. R. Kanagachidambaresan ... [et al.].
other author:	Kanagachidambaresan, G. R.
Published:	Cham :Springer International Publishing :2023.
Description:	xxii, 325 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligenceMedical applications.
Online resource:	https://doi.org/10.1007/978-3-031-19752-9
ISBN:	9783031197529$q(electronic bk.)

System design for epidemics using machine learning and deep learning
System design for epidemics using machine learning and deep learning[electronic resource] /edited by G. R. Kanagachidambaresan ... [et al.]. - Cham :Springer International Publishing :2023. - xxii, 325 p. :ill. (chiefly color), digital ;24 cm. - Signals and communication technology,1860-4870. - Signals and communication technology..

1. Pandemic effect of COVID-19: Identification, Present scenario and preventive measures using Machine learning model. -- 2. A Comprehensive Review of the Smart Health Records to prevent Pandemic -- 3. Automation of COVID-19 Disease Diagnosis from Radiograph -- 4. Applications of Artificial Intelligence in the attainment of Sustainable Development Goals -- 5. A Novel Model for IoT Blockchain Assurance Based Compliance to COVID Quarantine -- 6. DEEP LEARNING BASED CONVOLUTIONALNEURAL NETWORK WITH RANDOM FOREST APPROACH FOR MRI BRAIN TUMOUR SEGMENTATION -- 7. Expert systems for improving the effectiveness of remote health monitoring in Covid-19 Pandemic - A Critical Review -- 8. Artificial Intelligence-based predictive tools for Life-threatening diseases -- 9. Deep Convolutional Generative Adversarial Network for Metastatic Tissue Diagnosis in Lymph Node Section -- 10. Transformation in Health Sector during Pandemic by Photonics Devices -- 11. DIAGNOSIS OF COVID-19 FROM CT IMAGES AND RESPIRATORY SOUND SIGNALS USING DEEP LEARNING STRATEGIES -- 12. The Role of Edge Computing in Pandemic and Epidemic Situations with its Solutions -- 13. Advances and application of Artificial Intelligence and Machine learning in the field of cardiovascular diseases and its role during the Pandemic condition -- 14. Effective Health Screening and Prompt Vaccination to Counter the Spread of Covid-19 and Minimize its Adverse Effects -- 15. CROWD DENSITY ESTIMATION USING NEURAL NETWORK FOR COVID'19 AND FUTURE PANDEMICS -- 16. "Role of digital healthcare in rehabilitation during pandemic" -- 17. AN EPIDEMIC OF NEURODEGENERATIVE DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES -- 18. Covid-19 Growth Curve Forecasting for India using Deep Learning Techniques.

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

ISBN: 9783031197529$q(electronic bk.)

Standard No.: 10.1007/978-3-031-19752-9doiSubjects--Topical Terms:

237917
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78

Dewey Class. No.: 610.28563

System design for epidemics using machine learning and deep learning
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245 0 0 $a System design for epidemics using machine learning and deep learning $h [electronic resource] / $c edited by G. R. Kanagachidambaresan ... [et al.].
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300 $a xxii, 325 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Signals and communication technology, $x 1860-4870
505 0 $a 1. Pandemic effect of COVID-19: Identification, Present scenario and preventive measures using Machine learning model. -- 2. A Comprehensive Review of the Smart Health Records to prevent Pandemic -- 3. Automation of COVID-19 Disease Diagnosis from Radiograph -- 4. Applications of Artificial Intelligence in the attainment of Sustainable Development Goals -- 5. A Novel Model for IoT Blockchain Assurance Based Compliance to COVID Quarantine -- 6. DEEP LEARNING BASED CONVOLUTIONALNEURAL NETWORK WITH RANDOM FOREST APPROACH FOR MRI BRAIN TUMOUR SEGMENTATION -- 7. Expert systems for improving the effectiveness of remote health monitoring in Covid-19 Pandemic - A Critical Review -- 8. Artificial Intelligence-based predictive tools for Life-threatening diseases -- 9. Deep Convolutional Generative Adversarial Network for Metastatic Tissue Diagnosis in Lymph Node Section -- 10. Transformation in Health Sector during Pandemic by Photonics Devices -- 11. DIAGNOSIS OF COVID-19 FROM CT IMAGES AND RESPIRATORY SOUND SIGNALS USING DEEP LEARNING STRATEGIES -- 12. The Role of Edge Computing in Pandemic and Epidemic Situations with its Solutions -- 13. Advances and application of Artificial Intelligence and Machine learning in the field of cardiovascular diseases and its role during the Pandemic condition -- 14. Effective Health Screening and Prompt Vaccination to Counter the Spread of Covid-19 and Minimize its Adverse Effects -- 15. CROWD DENSITY ESTIMATION USING NEURAL NETWORK FOR COVID'19 AND FUTURE PANDEMICS -- 16. "Role of digital healthcare in rehabilitation during pandemic" -- 17. AN EPIDEMIC OF NEURODEGENERATIVE DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES -- 18. Covid-19 Growth Curve Forecasting for India using Deep Learning Techniques.
520 $a This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.
650 0 $a Artificial intelligence $x Medical applications. $3 237917
650 0 $a Deep learning (Machine learning) $x Therapeutic use. $3 939586
650 0 $a Epidemics $x Prevention $x Technological innovations. $3 946616
650 1 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Health Informatics. $3 274212
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Kanagachidambaresan, G. R. $3 858756
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Signals and communication technology. $3 557664
856 4 0 $u https://doi.org/10.1007/978-3-031-19752-9
950 $a Engineering (SpringerNature-11647)