Dragonfly Optimised Regressive Gradient Convolutional Deep Belief Network for Depression Prediction using Social Media Texts
Dragonfly Optimised Regressive Gradient Convolutional Deep Belief Network for Depression Prediction using Social Media Texts |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-6 |
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| Year of Publication : 2025 | ||
| Author : R. Geetha, D. Vimal Kumar | ||
| DOI : 10.14445/22315381/IJETT-V73I6P108 | ||
How to Cite?
R. Geetha, D. Vimal Kumar, "Dragonfly Optimised Regressive Gradient Convolutional Deep Belief Network for Depression Prediction using Social Media Texts," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.76-90, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P108
Abstract
Depression is a widespread and deeply impactful mental health condition that affects millions of individuals globally. Conventional methods face challenges in improving the accuracy and robustness of depression detection. Dragonfly Optimized Piecewise Regression Gradient Convolutional Deep Belief Network (DOPR-GCDBN) model is proposed to enhance the accuracy of depression prediction through sentiment analysis of Twitter social media text data.
Keywords
Depression prediction, Twitter data, Convolutional deep belief network, Pre-processing, Dragonfly optimisation algorithm, Piecewise regression, Stochastic gradient.
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