Cardiotocography Data Analysis for Fetal Health Classification Using Machine Learning Models-Reference-Cited by-同舟云学术

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Cardiotocography Data Analysis for Fetal Health Classification Using Machine Learning Models

Published:2024 Issue: Volume:12 Page:26005-26022
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Salini Yalamanchili¹^ORCID,Mohanty Sachi Nandan²^ORCID,Ramesh Janjhyam Venkata Naga³^ORCID,Yang Ming⁴,Chalapathi Mukkoti Maruthi Venkata²^ORCID

Affiliation:

1. Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, India

2. School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India

3. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

4. College of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10431783.pdf?arnumber=10431783

Reference37 articles.

1. Intended and Unintended Pregnancies Worldwide in 2012 and Recent Trends

2. Global, regional, and national age–sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AI-driven paradigm shift in computerized cardiotocography analysis: A systematic review and promising directions;Neurocomputing;2024-11

2. Advancing Fetal Heart Rate Monitoring: AI-Based Approach for Baseline Detection and Classification;2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM);2024-07-23

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