A Machine-Learning Approach for Children’s Pain Assessments Using Prosodic and Spectral Acoustic Features-Reference-Cited by-同舟云学术

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A Machine-Learning Approach for Children’s Pain Assessments Using Prosodic and Spectral Acoustic Features

Published:2023-07-19 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
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Author:

Jamal Abdulrahman¹,Al-Azani Sadam²

Affiliation:

1. SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum & Minerals,Information & Computer Science Department,Dhahran,Saudi Arabia

2. King Fahd University of Petroleum & Minerals,SDAIA-KFUPM Joint Research Center for AI,Dhahran,Saudi Arabia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10252163/10252090/10252478.pdf?arnumber=10252478

Reference25 articles.

1. Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model

2. Infant Weeping Calls Decoder using Statistical Feature Extraction and Gaussian Mixture Models

3. A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

4. Calm the crying: Using the dunstan baby language;dunstan;Avery,2012

5. A CNN-Based Method for Infant Cry Detection and Recognition

Cited by 1 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. From Cries to Answers: A Comprehensive CNN+DNN Hybrid Model for Infant Cry Classification with Enhanced Data Augmentation and Feature Extraction;2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS);2024-03-08

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