Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection-Reference-Cited by-同舟云学术

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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection

Published:2023-11-06 Issue: Volume:9 Page:27-32
ISSN:
Container-title:2023 18th International Conference on Emerging Technologies (ICET)
language:
Short-container-title:

Author:

Ajmal Sahar¹,Usman Muhammad²,Javed Abqa³

Affiliation:

1. National University of Computer and Emerging Sciences,Dept. of Software Engineering,Chiniot-Faisalabad,Pakistan

2. National University of Computer and Emerging Sciences,Dept. of Computer Science,Chiniot-Faisalabad,Pakistan

3. Univ. of Engineering and Technology,Dept. of Computer Science,Lahore,Pakistan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10374480/10374484/10375035.pdf?arnumber=10375035

Reference20 articles.

1. A textual-based featuring approach for depression detection using machine learning classifiers and social media texts

2. Classification Based on Decision Tree Algorithm for Machine Learning

3. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Networks

4. Meta-Learning in Neural Networks: A Survey

5. Introduction to machine learning, neural networks, and deep learning;Choi;Translational vision science & technology,2020

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