Autorentext

S. Sumathi is a Professor in the Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore with teaching and research experience of 34 years. Her research interests include Neural Networks, Fuzzy Systems and Genetic Algorithms, Pattern Recognition and Classification, Data Warehousing and Data Mining, Operating systems and Parallel Computing. She is the author of more than 40 papers in refereed journals and international conferences. She has authored 10 books with reputed publishers such as Springer, CRC Press and Nova Publishers.

Suresh V. Rajappa PhD PMP MBA is a seasoned senior IT management consulting professional with 25 years' experience leading large global IT programs and projects in IT Strategy, Finance IT (FINTECH) Transformation Strategy, BI and data warehousing / Data Analytics and Management for multiple fortune 100 clients across diverse industries, generating millions of dollars to top and bottom lines.

B. Vinoth Kumar is a Professor in the Department of Information Technology at PSG College of Technology, with 20 years of experience. His current research interests include Computational Intelligence, Memetic Algorithms, Affective Computing, and Image Processing. He has authored over 60 papers in refereed journals and international conferences. Additionally, he has edited eight books published by reputed publishers such as Springer and CRC Press. He also serves as a guest editor and reviewer for numerous journals from leading publishers including IEEE, Elsevier, Springer, Inderscience, and De Gruyter.

Surekha P. is an Assistant Professor (Sr. Gr) in the Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India with 20 years of experience in teaching, industry and research. She has published 35 papers in International and National journals and conferences. She has authored 7 books with leading publishers such as CRC Press and Springer. Her research interests include Control Systems, Computational Intelligence, Machine Learning, Signal and Image Processing, Embedded Systems, Real time operating systems, and Virtual Instrumentation.

P. Sivakumar is an Assistant Professor (Sl.Gr) in the Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore with teaching and research experience of 18 years.. He completed his Ph.D. in Electrical Engineering with a specialization in Automotive Embedded Software in the year 2018 from Anna University, PSG College of Technology. His research interests include Embedded System, Model Based Design, and Model- Based testing of Automotive Software, Automotive Software Development, Fog and Edge Computing. He serves as a Guest Editor/Reviewer of journals with leading publishers such as Inderscien

Klappentext

Bioinformatics deals with computational and mathematical approaches for understanding and processing biological data. This book highlights the computational procedures and the applications evolving around the biological data, including usage of decision sciences for a variety of applications, namely text mining, "OMIC" sciences, systems biology, analyzing biological/medical images, computer-aided diagnosis/treatment of diseases, decision sciences for public health with COVID-19 data, biodiversity, smart wearables, personalized medicine, data deluge issues, and knowledge management.

Key Features:

• Presents exclusive material on decision sciences in bioinformatics

• Highlights the computational procedures and applications evolving around biological data

• Provides solution to problems in bioinformatics with decision sciences

• Addresses the research gaps in bioinformatics

• Includes case studies emphasizing societal needs This book is aimed at researchers and graduate students in bioinformatics and data analytics.

  Inhalt

  1. Semantic Data Fabric for Automated Health Care Data Integration Using AI for Risk Prognosis and Preventive Care; 2. Design of Implantable and Wearable Fractal Antenna for COVID-19 Health Monitoring Devices; 3. Thyroid Prediction Using Hybrid CNN and LSTM Model; 4. Hybrid GAN Model with LSTM-Combined ResNet Discriminator for COVID-19 Classification in CT Images; 5. Dynamic Weighted Ensemble Framework; 6. Cataloging of Alzheimer's Disease and Its Stages Using Machine Learning; 7. COVID-19 Fake News Detection to Combat and Mitigate Its Spread; 8. Overcoming and Solving the Challenges of Data Deluge in Healthcare; 9. Utilizing Artificial Intelligence for the Identification of Plant Species and Detection of Diseases through Deep Learning