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Full Description
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.
Contents
Preface
1. Design of Medical Expert Systems Using Machine Learning Techniques
S. Anto, S. Siamala Devi, K. R. Jothi, and R. Lokeshkumar
2. FFrom Design Issues to Validation: Machine Learning in Biomedical Engineering
Christa I L Sharon and V. Suma
3. Biomedical Engineering and Informatics Using Artificial Intelligence
K. Padmavathi and A. S. Saranya
4. Hybrid Genetic Algorithms for Biomedical Applications
Srividya P. and Rajendran Sindhu
5. Healthcare Applications of the Biomedical AI System
S. Shyni Carmel Mary and S. Sasikala
6. Applications of Artificial Intelligence in Biomedical Engineering
Puja Sahay Prasad, Vinit Kumar Gunjan, Rashmi Pathak, and Saurabh Mukherjee
7. Biomedical Imaging Techniques Using AI Systems
A. Aafreen Nawresh and S. Sasikala
8. Analysis of Heart Disease Prediction Using Machine Learning Techniques
N. Hema Priya, N. Gopikarani, and S. Shymala Gowri
9. A Review on Patient Monitoring and Diagnosis Assistance by Artificial Intelligence Tools
Sindhu Rajendran, Meghamadhuri Vakil, Rhutu Kallur, Vidhya Shree, Praveen Kumar Gupta, and Lingaiya Hiremat
10. Semantic Annotation of Healthcare Data
M. Manonmani and Sarojini Balakrishanan
11. Drug Side Effect Frequency Mining over a Large Twitter Dataset using Apache Spark
Dennis Hsu, Melody Moh, Teng-Sheng Moh, and Diane Moh
12. Deep Learning in Brain Segmentation
Hao-Yu Yang
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, S. Arunmozhi Selvi, R.S. Rajesh, P. Sivananaintha Perumal, and J. Stalin
15. Real-Time Detection of Facial Expressions Using k-NN, SVM, Ensemble classifier and Convolution Neural Networks
A. Sharmila, B. Bhavya, and K. V. N. Kavitha, and P. Mahalakshmi
16. 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 and Deep 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 Tumour Regions from Brain Magnetic Resonance Images by Neural Network-Based Technique
J. V. Bibal Benifa and G. Venifa Mini
21. A Hypothetical Study in Biomedical Based Artificial Intelligence Systems using Machine Language (ML) 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
Index
