Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques.
Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. Biographies - p. x 8. Preface - p. xiii 9. Chapter 1. Application of dynamical systems based deep learning algorithms to model emergent characteristics for healthcare diagnostics - p. 1 10. Chapter 2. Computational intelligence in healthcare and biosignal processing - p. 31 11. Chapter 3. A semi-supervised approach for automatic detection and segmentation of optic disc from retinal fundus image - p. 65 12. Chapter 4. Medical decision support system using data mining: an intelligent health care monitoring system for guarded travel - p. 93 13. Chapter 5. Deep learning in gastroenterology: a brief review - p. 121 14. Chapter 6. Application of soft computing techniques to calculation of medicine dose during the treatment of patient: a fuzzy logic approach - p. 151 15. Chapter 7. Multiobjective optimization technique for gene selection and sample categorization - p. 179 16. Chapter 8. Medical decision support system using data mining semicircular-based angle-oriented facial recognition using neutrosophic logic - p. 195 17. Chapter 9. Preservation module prediction by weighted differentially coexpressed gene network analysis (WDCGNA) of HIV-1 disease: a case study for cancer - p. 213 18. Chapter 10. Computational intelligence for genomic data: a network biology approach - p. 247 19. Chapter 11. A Kinect-based motor rehabilitation system for stroke recovery - p. 261 20. Chapter 12. Empirical study on Uddanam chronic kidney diseases (UCKD) with statistical and machine learning analysis including probabilistic neural networks - p. 283 21. Chapter 13. Enhanced brain tumor detection using fractional wavelet transform and artificial neural network - p. 315 22. Chapter 14. A study on smartphone sensor-based Human Activity Recognition using deep learning approaches - p. 343 23. Index - p. 371
Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques.
Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. Biographies - p. x 8. Preface - p. xiii 9. Chapter 1. Application of dynamical systems based deep learning algorithms to model emergent characteristics for healthcare diagnostics - p. 1 10. Chapter 2. Computational intelligence in healthcare and biosignal processing - p. 31 11. Chapter 3. A semi-supervised approach for automatic detection and segmentation of optic disc from retinal fundus image - p. 65 12. Chapter 4. Medical decision support system using data mining: an intelligent health care monitoring system for guarded travel - p. 93 13. Chapter 5. Deep learning in gastroenterology: a brief review - p. 121 14. Chapter 6. Application of soft computing techniques to calculation of medicine dose during the treatment of patient: a fuzzy logic approach - p. 151 15. Chapter 7. Multiobjective optimization technique for gene selection and sample categorization - p. 179 16. Chapter 8. Medical decision support system using data mining semicircular-based angle-oriented facial recognition using neutrosophic logic - p. 195 17. Chapter 9. Preservation module prediction by weighted differentially coexpressed gene network analysis (WDCGNA) of HIV-1 disease: a case study for cancer - p. 213 18. Chapter 10. Computational intelligence for genomic data: a network biology approach - p. 247 19. Chapter 11. A Kinect-based motor rehabilitation system for stroke recovery - p. 261 20. Chapter 12. Empirical study on Uddanam chronic kidney diseases (UCKD) with statistical and machine learning analysis including probabilistic neural networks - p. 283 21. Chapter 13. Enhanced brain tumor detection using fractional wavelet transform and artificial neural network - p. 315 22. Chapter 14. A study on smartphone sensor-based Human Activity Recognition using deep learning approaches - p. 343 23. Index - p. 371
