Apple Academic Press
622 pages | 6 Color Illus. | 165 B/W Illus.
Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications.
This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert’s knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications.
The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts.
1. Design of a Medical Expert System Using Machine Learning Techniques
S. Anto et al.
2. From Design Issues to Validation: Machine Learning in Biomedical Engineering
I. L. Sharon Christa and V. Suma
3. Biomedical Engineering and Informatics Using Artificial Intelligence
K. Padmavathi and A. S. Saranya
4. Hybrid Genetic Algorithms for Biomedical Applications
P. Srividya and Sindhu Rajendran
5. Healthcare Applications of Biomedical AI System
S. Shyni Carmel Mary and S. Sasikala
6. Medical Applications of Artificial Intelligence
Puja Sahay Prasad et al.
7. Biomedical Imaging Techniques Using AI Systems
Aafreen Nawresh and S. Sasikala
8. Analysis of Heart Disease Prediction Using Machine Learning Techniques
N. Hema Priya, N. Gopikarani, and S. Shymalagowri
9. A Review on Patient Monitoring and Diagnosis Assisted by Artificial Intelligence Tools
Sindhu Rajendran et al.
10. Semantic Annotation of Healthcare Data
M. Manonmani and Dr. Sarojini Balakrishanan
11. Mining the Frequency of Drug Side Effects Over a Large Twitter Dataset Using Apache Spark
Dennis Hsu et al.
12. Deep Learning in Brain Segmentation
13. Security and Privacy Issues in Biomedical AI Systems and Potential Solutions
G. Niranjana and Deya Chatterjee
14. Limos-Live Patient Monitoring System
T. Ananth Kumar et al.
15. Real-Time Detection of Facial Expressions Using Classifiers and Convolution Neural Networks
A. Sharmila et al.
16. An Analysis and Interpretation of Uterine Contraction Signals Using Artificial Intelligence
P. Mahalakshmi and S. Suja Priyadharsini
17. Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques
Subha Velappan, Manivanna Boopathi Arumugam, and Zafer Comert
18. Deployment of Supervised Machine Learning Algorithms in Biomedical Text Classification
G. Kumaravelan and Bichitrananda Behera
19. Energy Efficient Optimum Cluster Head Estimation for Body Area Networks
P. Sundareswaran and R. S. Rajesh
20. Segmentation and Classification of Tumor Regions from Brain Magnetic Resonance Images by Neural Network-Based Technique
J. V. Bibal Benifa and G. Venifa Mini
21. Hypothetical Study in Biomedical Based Artificial Intelligence Systems Using Machine Learning Rudiments
D. Renuka Devi and S. Sasikala
22. Neural Source Connectivity Estimation Using Particle Filter and Granger Causality Methods
Santhosh Kumar Veeramalla and T. V. K. Hanumantha Rao
23. Exploration of Lymph Node-Negative Breast Cancers by Support Vector Machines, Naïve Bayes, and Decision Trees: A Comparative Study
J. Satya Eswari and Pradeep Singh