Handbook of Computational Intelligence in Biomedical Engineering and Healthcare
Coordonnateurs : Nayak Janmenjoy, Naik Bighnaraj, Pelusi Danilo, Das Asit Kumar
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.
Part 1: Computational Intelligence in Bioengineering and Health Care: An Introduction 1. Data Analysis in Bioengineering and Health Care: Advances and Challenges 2. Impact of Data Type and Analysis on Nature of Data 3. Computational Intelligence in Healthcare: Real Life Applications
Part 2: Computational Intelligence Techniques 4. Computational Intelligence: Past to Present 5. Computational Intelligence: Methods and Tools 6. Computational Intelligence: Trends and Applications 7. Computational Intelligence: Issues and Future Challenges
Part 3: Computational Intelligence in Bioengineering: A step towards the Next 8. Advance Computational Intelligence Techniques in bioengineering 9. A Case Study 10. New Technologies for biosensors 11. Performance Analysis: Statistical Approach
Bighnaraj Naik is an Assistant Professor in the Department of Computer Application, Veer Surendra Sai University of Technology (formerly UCE Burla), Odisha, India. He has published more than 100 research articles in various peer reviewed international journals, conferences, and book chapters. He has edited 10 books for publishers including Elsevier, Springer, and IGI Global. At present, he has more than 10 years of teaching experience in the field of computer science and information technology. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and his areas of interest include data science, data mining, machine learning, deep learning, computational intelligence (CI), and CI’s applications in science and engineering. He has served as Guest Editor of various special issues of journals such as Information Fusion (Elsevier), Neural Computing and Applications (Springer), Evolutionary Intelligence (Springer), International Journal of Computational Intelligence Studies (Inderscience), and International Journal of Swarm Intelligence (Inderscience). He is an active reviewer of various journals from publishers including IEEE Transactions, Elsevier, Springer, and Inderscience. Currently, he is undertaking a major research project as Principal Investigator, which is funded by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India.
Danilo Pelusi is an Associate Professor in the Department of Communication Sciences, University of Teramo, where he received his PhD in Computational Astrophysics. He is an Editor of books for Springer and Elsevier, and an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intell
- Presents a comprehensive handbook that covers an Introduction to Computational Intelligence in Biomedical Engineering and Healthcare, Computational Intelligence Techniques, and Advanced and Emerging Techniques in Computational Intelligence
- Helps readers analyze and do advanced research in specialty healthcare applications
- Includes links to websites, videos, articles and other online content to expand and support primary learning objectives
Date de parution : 04-2021
Ouvrage de 396 p.
19x23.3 cm
Thèmes de Handbook of Computational Intelligence in Biomedical... :
Mots-clés :
1D-CNN; ANN; Artificial intelligence; Artificial neural network; Back propagation; Biological network analysis; Body sensors; Brainwaves; Chronic intestine; Circular Hough transform; Cluster evaluation; Clustering; CNN-LSTM; ConvLSTM; Data classification; Data mining; Decision support system; Deep clustering networks; Deep learning; Defuzzification; Differentially coexpressed genes (DCEGs)Differentially expressed genes (DEGs)Eigengene network of differential coexpression modules; Discrete wavelet transform; Drugs; Dynamical systems; Electronic health record (EHR)Health status; Evolutionary algorithm; Facerecognition; Fractional Fourier transform; Fractional wavelet transform; Fuzzification; Fuzzy logic; Gastroenterology; Gastrointestinal; Gene selection; Genomic information; Healthcare; Hierarchical clustering; Human activity recognition; Intelligent system; KEGG Pathway; Kidney Diseases; Kinect; Long short term memory; Machine learning; Medical diagnosis; Membership function; Monitoring; Motor rehabilitation; Multi objective optimization; Multilayer perceptron; Neural networks; Next generation sequencing; Nondominated pareto front; Optic disc; PNN; Principal component analysis; Recurrent neural network (RNN)Retina; RNN-LSTM; Sample clustering; Segmentation; Sensor data; Signal processing; Stacked-CNN; Stroke recovery; Uddanam; Weighted coexpression network of differentially expressed gene network (WDCGN)
