Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics (eBook, PDF)
Techniques and Applications
Redaktion: Dash, Sujata; Majhi, Babita; Rodrigues, Joel J. P. C.; Kumar Pani, Subhendu
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Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics (eBook, PDF)
Techniques and Applications
Redaktion: Dash, Sujata; Majhi, Babita; Rodrigues, Joel J. P. C.; Kumar Pani, Subhendu
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Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IOT and Machine Learning based biomedical and health related applications.
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Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IOT and Machine Learning based biomedical and health related applications.
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Produktdetails
- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 382
- Erscheinungstermin: 10. Februar 2022
- Englisch
- ISBN-13: 9781000534009
- Artikelnr.: 63420518
- Verlag: Taylor & Francis
- Seitenzahl: 382
- Erscheinungstermin: 10. Februar 2022
- Englisch
- ISBN-13: 9781000534009
- Artikelnr.: 63420518
Sujata Dash is an Associate Professor at P.G. Department of Computer Science & Application, North Orissa University, at Baripada, India. Subhendu Kumar Pani is a Professor in the Department of Computer Science Engineering and also Research coordinator at Orissa Engineering College (OEC) Bhubaneswar. Joel J. P. C. Rodrigues is a Professor at the Federal University of Piauí, Brazil; and senior researcher at the Instituto de Telecomunicações, Portugal. Babita Majhi is an Assistant Professor in the department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Central University, Bilaspur, India.
Part I: Machine Learning Techniques in Biomedical and Health Informatics.
1. Effect of Socio-economic and environmental factors on the growth rate of
COVID 19 with an overview of speech data for its early diagnosis. 2.
Machine Learning in Healthcare - The Big Picture. 3. Heart Disease
Assessment using Advanced Machine Learning Techniques. 4. Classification of
Pima Indian Diabetes Dataset using Support Vector Machine with Polynomial
Kernel. 5. Prediction and Analysis of Covid-19 Pandemic. 6. Variational
mode decomposition based automated diagnosis method for epilepsy using EEG
signals. 7. Soft-computing approach in Clinical Decision Support Systems.
8. A Comparative Performance Assessment of a Set of Adaptive Median filters
for Eliminating Noise from Medical Images. 9. Early Prediction Of
Parkinson's Disease Using Motor, Non-Motor Features And Machine Learning
Techniques. Part II: Deep Learning Techniques in Biomedical and Health
Informatics. 10. Deep Neural Network for Parkinson Disease Prediction using
SPECT Image. 11. An Insight into Applications of Deep Learning in
Bioinformatics. 12. Classification of Schizophrenia Associated Proteins
using Amino Acid Descriptors and Deep Neural Network. 13. Deep Learning
Architectures, Libraries and Frameworks in Healthcare. 14. Designing
Low-Cost and Easy-To-Access Skin Cancer Detector using Neural Network
Followed by Deep Learning. Part III: Internet of Things ( IoT) in
Biomedical and Health Informatics. 15. Application of Artificial
Intelligence in IoT based Healthcare Systems. 16. Computational
Intelligence in IoT Healthcare. 17. Machine Learning Techniques for
high-performance computing for IoT applications in healthcare. 18. Early
Hypertensive Retinopathy Detection using Improved Clustering algorithm and
Raspberry PI. 19. IoT based Architecture for Elderly Patient Care System.
1. Effect of Socio-economic and environmental factors on the growth rate of
COVID 19 with an overview of speech data for its early diagnosis. 2.
Machine Learning in Healthcare - The Big Picture. 3. Heart Disease
Assessment using Advanced Machine Learning Techniques. 4. Classification of
Pima Indian Diabetes Dataset using Support Vector Machine with Polynomial
Kernel. 5. Prediction and Analysis of Covid-19 Pandemic. 6. Variational
mode decomposition based automated diagnosis method for epilepsy using EEG
signals. 7. Soft-computing approach in Clinical Decision Support Systems.
8. A Comparative Performance Assessment of a Set of Adaptive Median filters
for Eliminating Noise from Medical Images. 9. Early Prediction Of
Parkinson's Disease Using Motor, Non-Motor Features And Machine Learning
Techniques. Part II: Deep Learning Techniques in Biomedical and Health
Informatics. 10. Deep Neural Network for Parkinson Disease Prediction using
SPECT Image. 11. An Insight into Applications of Deep Learning in
Bioinformatics. 12. Classification of Schizophrenia Associated Proteins
using Amino Acid Descriptors and Deep Neural Network. 13. Deep Learning
Architectures, Libraries and Frameworks in Healthcare. 14. Designing
Low-Cost and Easy-To-Access Skin Cancer Detector using Neural Network
Followed by Deep Learning. Part III: Internet of Things ( IoT) in
Biomedical and Health Informatics. 15. Application of Artificial
Intelligence in IoT based Healthcare Systems. 16. Computational
Intelligence in IoT Healthcare. 17. Machine Learning Techniques for
high-performance computing for IoT applications in healthcare. 18. Early
Hypertensive Retinopathy Detection using Improved Clustering algorithm and
Raspberry PI. 19. IoT based Architecture for Elderly Patient Care System.
Part I: Machine Learning Techniques in Biomedical and Health Informatics.
1. Effect of Socio-economic and environmental factors on the growth rate of
COVID 19 with an overview of speech data for its early diagnosis. 2.
Machine Learning in Healthcare - The Big Picture. 3. Heart Disease
Assessment using Advanced Machine Learning Techniques. 4. Classification of
Pima Indian Diabetes Dataset using Support Vector Machine with Polynomial
Kernel. 5. Prediction and Analysis of Covid-19 Pandemic. 6. Variational
mode decomposition based automated diagnosis method for epilepsy using EEG
signals. 7. Soft-computing approach in Clinical Decision Support Systems.
8. A Comparative Performance Assessment of a Set of Adaptive Median filters
for Eliminating Noise from Medical Images. 9. Early Prediction Of
Parkinson's Disease Using Motor, Non-Motor Features And Machine Learning
Techniques. Part II: Deep Learning Techniques in Biomedical and Health
Informatics. 10. Deep Neural Network for Parkinson Disease Prediction using
SPECT Image. 11. An Insight into Applications of Deep Learning in
Bioinformatics. 12. Classification of Schizophrenia Associated Proteins
using Amino Acid Descriptors and Deep Neural Network. 13. Deep Learning
Architectures, Libraries and Frameworks in Healthcare. 14. Designing
Low-Cost and Easy-To-Access Skin Cancer Detector using Neural Network
Followed by Deep Learning. Part III: Internet of Things ( IoT) in
Biomedical and Health Informatics. 15. Application of Artificial
Intelligence in IoT based Healthcare Systems. 16. Computational
Intelligence in IoT Healthcare. 17. Machine Learning Techniques for
high-performance computing for IoT applications in healthcare. 18. Early
Hypertensive Retinopathy Detection using Improved Clustering algorithm and
Raspberry PI. 19. IoT based Architecture for Elderly Patient Care System.
1. Effect of Socio-economic and environmental factors on the growth rate of
COVID 19 with an overview of speech data for its early diagnosis. 2.
Machine Learning in Healthcare - The Big Picture. 3. Heart Disease
Assessment using Advanced Machine Learning Techniques. 4. Classification of
Pima Indian Diabetes Dataset using Support Vector Machine with Polynomial
Kernel. 5. Prediction and Analysis of Covid-19 Pandemic. 6. Variational
mode decomposition based automated diagnosis method for epilepsy using EEG
signals. 7. Soft-computing approach in Clinical Decision Support Systems.
8. A Comparative Performance Assessment of a Set of Adaptive Median filters
for Eliminating Noise from Medical Images. 9. Early Prediction Of
Parkinson's Disease Using Motor, Non-Motor Features And Machine Learning
Techniques. Part II: Deep Learning Techniques in Biomedical and Health
Informatics. 10. Deep Neural Network for Parkinson Disease Prediction using
SPECT Image. 11. An Insight into Applications of Deep Learning in
Bioinformatics. 12. Classification of Schizophrenia Associated Proteins
using Amino Acid Descriptors and Deep Neural Network. 13. Deep Learning
Architectures, Libraries and Frameworks in Healthcare. 14. Designing
Low-Cost and Easy-To-Access Skin Cancer Detector using Neural Network
Followed by Deep Learning. Part III: Internet of Things ( IoT) in
Biomedical and Health Informatics. 15. Application of Artificial
Intelligence in IoT based Healthcare Systems. 16. Computational
Intelligence in IoT Healthcare. 17. Machine Learning Techniques for
high-performance computing for IoT applications in healthcare. 18. Early
Hypertensive Retinopathy Detection using Improved Clustering algorithm and
Raspberry PI. 19. IoT based Architecture for Elderly Patient Care System.