Handbook of Artificial Intelligence in Biomedical Engineering

 
 
Taylor & Francis Ltd (Verlag)
  • erscheint ca. am 30. März 2021
  • |
  • 564 Seiten
 
E-Book | ePUB ohne DRM | Systemvoraussetzungen
978-1-000-06767-5 (ISBN)
 
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.
  • Englisch
  • Milton
  • |
  • Großbritannien
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163 schwarz-weiße und 6 farbige Abbildungen, 41 schwarz-weiße Tabellen
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978-1-000-06767-5 (9781000067675)
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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, PhD, is currently working as Associate Professor in the Computer Science and Engineering Department at College of Engineering, Anna University, Guindy, Chennai, India. She has vast research experience and has published 180 papers in reputed journals and international conferences. She is also deputy director for the Centre for Entrepreneurship Development, Anna University. She is an active reviewer for various SCI, Scopus journals. Her research interest includes machine learning, artificial intelligence, text mining, and natural language processing.

1. Design of Medical Expert Systems Using Machine Learning Techniques S. Anto, S. Siamala Devi, K. R. Jothi, and R. Lokeshkumar 2. From 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

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