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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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