Handbook of Artificial Intelligence in Biomedical Engineering

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.

Preface

1. <b>Design of Medical Expert Systems Using Machine Learning Techniques</b>
<i>S. Anto, S. Siamala Devi, K. R. Jothi, and R. Lokeshkumar</i>

2. <b>FFrom Design Issues to Validation: Machine Learning in Biomedical Engineering</b>
<i>Christa I L Sharon and V. Suma</i>

3. <b>Biomedical Engineering and Informatics Using Artificial Intelligence</b>
<i>K. Padmavathi and A. S. Saranya</i>

4. <b>Hybrid Genetic Algorithms for Biomedical Applications</b>
<i>Srividya P. and Rajendran Sindhu</i>

5. <b>Healthcare Applications of the Biomedical AI System</b>
<i>S. Shyni Carmel Mary and S. Sasikala</i>

6. <b>Applications of Artificial Intelligence in Biomedical Engineering</b>
<i>Puja Sahay Prasad, Vinit Kumar Gunjan, Rashmi Pathak, and Saurabh Mukherjee</i>

7. <b>Biomedical Imaging Techniques Using AI Systems</b>
<i>A. Aafreen Nawresh and S. Sasikala</i>

8. <b>Analysis of Heart Disease Prediction Using Machine Learning Techniques</b>
<i>N. Hema Priya, N. Gopikarani, and S. Shymala Gowri</i>

9. <b>A Review on Patient Monitoring and Diagnosis Assistance by Artificial Intelligence Tools</b>
<i>Sindhu Rajendran, Meghamadhuri Vakil, Rhutu Kallur, Vidhya Shree, Praveen Kumar Gupta, and Lingaiya Hiremat</i>

10. <b>Semantic Annotation of Healthcare Data</b>
<i>M. Manonmani and Sarojini Balakrishanan</i>

11. <b>Drug Side Effect Frequency Mining over a Large Twitter Dataset using Apache Spark</b>
<i>Dennis Hsu, Melody Moh, Teng-Sheng Moh, and Diane Moh</i>

12. <b>Deep Learning in Brain Segmentation</b>
<i>Hao-Yu Yang</i>

13. <b>Security and Privacy Issues in Biomedical AI Systems and Potential Solutions</b>
<i>G. Niranjana and Deya Chatterjee</i>

14. <b>LiMoS—Live Patient Monitoring System</b>
<i>T. Ananth Kumar, S. Arunmozhi Selvi, R.S. Rajesh, P. Sivananaintha Perumal, and J. Stalin</i>

15. <b>Real-Time Detection of Facial Expressions Using k-NN, SVM, Ensemble classifier and Convolution Neural Networks</b>
<i>A. Sharmila, B. Bhavya, and K. V. N. Kavitha, and P. Mahalakshmi</i>

16. <b>Analysis and Interpretation of Uterine Contraction Signals Using Artificial Intelligence</b>
<i>P. Mahalakshmi and S. Suja Priyadharsini</i>

17. <b>Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques</b>
<i>Subha Velappan, Manivanna Boopathi Arumugam, and Zafer Comert</i>

18. <b>Deployment of Supervised Machine Learning and Deep Learning Algorithms in Biomedical Text Classification</b>
<i>G. Kumaravelan and Bichitrananda Behera</i>

19. <b>Energy Efficient Optimum Cluster Head Estimation for Body Area Networks</b>
<i>P. Sundareswaran and R.S. Rajesh</i>

20. <b>Segmentation and Classification of Tumour Regions from Brain Magnetic Resonance Images by Neural Network-Based Technique</b>
<i>J. V. Bibal Benifa and G. Venifa Mini</i>

21. <b>A Hypothetical Study in Biomedical Based Artificial Intelligence Systems using Machine Language (ML) Rudiments</b>
<i>D. Renuka Devi and S. Sasikala</i>

22. <b>Neural Source Connectivity Estimation using particle filter and Granger causality methods</b>
<i>Santhosh Kumar Veeramalla and T. V. K. Hanumantha Rao</i>

23. <b>Exploration of Lymph Node-Negative Breast Cancers by Support Vector Machines, Naïve Bayes, and Decision Trees: A Comparative Study</b>
<i>J. Satya Eswari and Pradeep Singh</i>

Index

Saravanan Krishnan, PhD, is a Senior Assistant Professor in the Department of Computer Science & Engineering at Anna University, Regional Campus, Tirunelveli, Tamilnadu, India. He has 14 years of experience in academics and the IT industry and has published papers in 14 international conferences and 24 international journals. He has also written six book chapters and has edited three books with international publishers. He has done four research projects and two consultancy projects with the total worth of Rs.70 Lakhs. He is an active researcher and academician. Also, he is reviewer for many reputed journals published by Elsevier, Springer, IEEE, etc. He also received an outstanding reviewer certificate from Elsevier, Inc. He is a Mentor of Change for Atal Tinkering Lab of NITI Aayog. He has professional membership with several professional organizations. He has previously worked at Cognizant Technology Solutions, Pvt Ltd. as software associate. He earned his PhD in 2015 and completed his ME (Software Engineering) in 2007.

Ramesh Kesavan, PhD, is currently an Assistant Professor in the Department of Computer Applications, Anna University Regional Campus, Tirunelveli, India. His area of research includes cloud computing, big data analytics, data mining, and machine learning. He earned his PhD degree in Computer Science from Anna University, Chennai, India.

B. Surendiran, PhD, is an Associate Dean (Academic) and Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology, Puducherry, Karaikal, India. His research interests include medical imaging, machine learning, dimensionality reduction, and intrusion detection. He has published over 20 papers in international journals and has several conference publications to his credit. He is an active reviewer for various SCI and Scopus journals. He earned his PhD at the National Institute of Technology, Tiruchirappalli, India.

G. S. Mahalakshmi, P