Handbook of artificial intelligence in biomedical engineering / edited by Saravanan Krishnan, PhD, Ramesh Kesavan, B. Surendiran, PhD, G. S. Mahalakshmi, PhD.
| Other author | Saravanan, Krishnan, 1982- |
| Other author | Kesavan, Ramesh. |
| Other author | Surendiran, B. |
| Format | Electronic |
| Publication Info | Palm Bay, FL : Apple Academic Press, [2021] |
| Description | xxvi, 538 pages ; color illustrations ; 25 cm |
| Supplemental Content | Full text available from Taylor & Francis eBooks |
| Subjects |
| Series | Biomedical engineering: techniques and applications Biomedical engineering (Apple Academic Press) UNAUTHORIZED |
| Contents | Design of Medical Expert Systems Using Machine Learning Techniques / S. Anto, S. Siamala Devi, K.R. Jothi, and R. Lokeshkumar -- From Design Issues to Validation : Machine Learning in Biomedical Engineering / Christail Sharon and V. Suma -- Biomedical Engineering and Informatics Using Artificial Intelligence / K. Padmavathi and A.S. Saranya -- Hybrid Genetic Algorithms for Biomedical Applications / P. Srividya and Rajendran Sindhu -- Healthcare Applications Using Biomedical AI System / S. Shyni Carmel Mary and S. Sasikala -- Applications of Artificial Intelligence in Biomedical Engineering / Puja Sahay Prasad, Vinit Kumar Gunjan, Rashmi Pathak, and Saurabh Mukherjee -- Biomedical Imaging Techniques Using AI Systems / A. Aafreen Nawresh and S. Sasikala -- Analysis of Heart Disease Prediction Using Machine Learning Techniques / N. Hema Priya, N. Gopikarani, and S. Shymala Gowri -- Review of Patient Monitoring and Diagnosis Assistance by Artificial Intelligence Tools / Sindhu Rajendran, Meghamadhuri Vakil, Rhutu Kallur, Vidhya Shree, Praveen Kumar Gupta, and Lingaiya Hiremat -- Semantic Annotation of Healthcare Data / M. Manonmani and Sarojini Balakrishanan -- Drug Side Effect Frequency Mining over a Large Twitter Dataset using Apache Spark / Dennis Hsu, Melody Moh, Teng-Sheng Moh, and Diane Moh -- Deep Learning in Brain Segmentation / Hao-Yu Yang -- Security and Privacy Issues in Biomedical AI Systems and Potential Solutions / G. Niranjana and Deya Chatterjee -- LIMOS-Live Patient Monitoring System / T. Ananth Kumar, S. Arunmozhi Selvi, R.S. Rajesh, P. Sivananaintha Perumal, and J. Stalin -- 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 -- Analysis and Interpretation of Uterine Contraction Signals Using Artificial Intelligence / P. Mahalakshmi and S. Suja Priyadharsini -- Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques / -- Subha Velappan, Manivanna Boopathi Arumugam, and Zafer Comert -- Deployment of Supervised Machine Learning and Deep Learning Algorithms in Biomedical Text Classification / G. Kumaravelan and Bichitrananda Behera -- Energy Efficient Optimum Cluster Head Estimation for Body Area Networks / P. Sundareswaran and R.S. Rajesh -- Segmentation and Classification of Tumour Regions from Brain Magnetic Resonance Images by Neural Network-Based Technique / J.V. Bibal Benifa and G. Venifa Mini -- A Hypothetical Study in Biomedical Based Artificial Intelligence Systems Using Machine Language (ML) Rudiments / D. Renuka Devi and S. Sasikala -- Neural Source Connectivity Estimation Using Particle Filter and Granger Causality Methods / Santhosh Kumar Veeramalla and T.V.K. Hanumantha Rao -- Exploration of Lymph Node-Negative Breast Cancers by Support Vector Machines, Na©�ve Bayes, and Decision Trees : A Comparative Study / J. Satya Eswari, Pradeep Singh, and Srilakshmi Mutyala. |
| Abstract | "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. Topics include: Security and privacy issues in biomedical AI systems and potential solutions Healthcare applications using biomedical AI systems Machine learning in biomedical engineering Live patient monitoring systems Semantic annotation of healthcare data This book presents a broad exploration of biomedical systems using artificial intelligence techniques with detailed coverage of the applications, techniques, algorithms, platforms, and tools in biomedical AI systems. This book will benefit researchers, medical and industry practitioners, academicians, and students"-- Provided by publisher. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2020038313 |
| ISBN | 9781771889209 (hardcover) |
| ISBN | (ebook) |