Intelligent Data Analysis for Biomedical Applications

Erscheinungsjahr 2019
ISBN: 978-0-12-815553-0
Verlag: ACADEMIC PRESS

Buch, Englisch, 294 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 935 g

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases.

Zielgruppe

Graduate students, researchers and professionals working on computing, biomedical and healthcare areas. Hospital industry, health care diagnostic centres, professionals and practitioners in intelligent data analysis in biomedical science.

Autoren/Hrsg.

Hemanth, D. Jude

Gupta, Deepak

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein Mathematische Logik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien

Inhaltsverzeichnis

1. A Review on Intelligent Data Analysis System for an IoT-Based Capsule Endoscopy System
2. Ant Colony optimization-based image texture segmentation in medical images
3. A Feature Fusion Based Discriminant Learning Model for Diagnosis of Neuromuscular Disorders Using Single Channel Needle EMG Signal
4. Evolution of Consciousness Systems with bacteria
5. Analysis of transform-based compression techniques for MRI and CT images
6. Liveness Assured Multimodal Biometrics using Electrocardiogram for Smart Health Applications
7. Medical Image Retrieval System in PACS Environment for Clinical Decision Making
8. A Neuro Fuzzy Inference Model for Diabetic Retinopathy Classification
9. Predictive Analysis of breast cancer using machine learning techniques
10. Right Ventricle Image Segmentation from MRI using optimization techniques
11. Computational Automated System for Red Blood Cell Detection and Segmentation
12. Fuzzy logic based approaches for gene regulatory network inference
13. Evolutionary Algorithm with Memetic Search for Optic Disc Localization in Retinal Fundus Images
14. A novel approach of targeting prognostics of Bruxism disorder using C4-P4 channel of EEG signal
15. Physiotherapy and Rehabilitation Assessment System Using Bio-inspired Visual Cortex Computational Model
16. Classification of Myocardial Ischemia in Contrast Enhancement Using Machine Learning
17. Simple link sensor network based remote monitoring of multiple patients
18. Prognosticating the risk of fibrosis progression in non-alcoholic fatty liver diseases
19. Alzheimer Disease Detection from Structural MR Images using Fuzzy C-Means based Deep Belief Neural Network
20. Optimal Control of Fractional Order HIV-Immune System Using Neural Network Approach
21. Diagnosis of Ocular Disease using Optical Coherence Tomography Image
22. Hybrid Approach for Classification of EEG Signals using T-F Images with Wavelets and Texture Features

Über Autor(innen)

Gupta, Deepak
Dr. Deepak Gupta is an assistant professor at Maharaja Agrasen Institute of Technology, Delhi, India. He is an eminent academician, including roles as lecturer, researcher, consultant, community service, PhD, and post-doctorate supervision. Dr. Gupta focuses on rational and practical learning and has contributed important literature in the fields of Human-Computer Interaction, Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning, and Soft Computing. Dr. Gupta has authored/edited a number of books, including Emerging Trends and Roles of Fog, Edge, and Pervasive Computing in Intelligent IoT-Driven Applications, Wiley; Advanced Machine Intelligence and Signal Processing, Springer; Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press; Explainable Edge AI: A Futuristic Computing Perspective, Springer; Applications of Big Data in Healthcare, Elsevier/Academic Press; and Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; among others.