Artificial Intelligence and Machine Learning for Womens Health Issues: Challenges, Impact, and Solutions discusses the applications, challenges, and solutions that machine learning can bring to womens health challenges. This book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning, which enhance the future healthcare system. This book's primary focus is on womens health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of womens health issues.
1. Role of Artificial Intelligence in Gynecology and Obstetrics
2. Prediction of Female Pregnancy Complication using Artificial Intelligence
3. Early Stage Prediction of Endometriosis Cancer Using Fuzzy Machine
Learning Technique
4. Artificial Intelligence approaches for ultrasound examination in
pregnancy
5. Early assessment of pregnancy using machine learning
6. Ensemble learning-based analysis of perinatal health disorders in women
7. Machine learning applications to predict gestational diabetes in early
pregnancy
8. Contribution of artificial intelligence to improve women health in
pregnancy
9. Artificial Intelligence based Prediction of Health Risks Among Women
during Menopause
10. Mammography Screening of Women in Forties: Benefits and Risks
11. Machine learning approach to predict the early assessment of Post partum
depression
12. Artificial intelligence approaches for polycystic ovarian syndrome
13. Improving women's mental health through AI-powered interventions and
diagnoses
14. Early stage breast cancer diagnostics using Vision Transformers
15. Recent and Future Applications of Artificial Intelligence in Obstetric
Ultrasound Examination
16. Deadly Canker of Cervix Tackled With Early Diagnosis using Machine
Learning
17. AI, Womens health care and Trust: Problems and Prospects
18. Role of Artificial Intelligence and Machine learning in women's health:
Challenges and Solutions
Dr. Meenu Gupta is an Associate Professor at the UIE-CSE Department, Chandigarh University, India. She completed her Ph.D. in Computer Science and Engineering with an emphasis on Traffic Accident Severity Problems from Ansal University, Gurgaon, India, in 2020. She has more than 15 years of teaching experience. Her research areas cover Machine Learning, Intelligent Systems, and Data mining, with a specific interest in Artificial Intelligence, Image Processing and Analysis, Smart Cities, Data Analysis, and Human/Brain-machine Interaction (BMI). She has five edited and four authored books. She has also authored or co-authored more than 20 book chapters and over 80 papers in refereed international journals and conferences. She has five filled patents and was awarded the best faculty and department researcher in 2021 and 2022.
Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of Visiting Professor in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the Research Scientist of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain. Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.
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