Depression prediction based on LassoNet-RNN model: A longitudinal study

Han, Jiatong; Li, Hao; Lin, Han; Wu, Pingping; Wang, Shidan; Tu, Juan; Lu, Jing

doi:10.1016/j.heliyon.2023.e20684

Cited by 5 publications

(1 citation statement)

References 74 publications

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“…Recent years have witnessed significant advancements in image recognition within the realm of computational vision, propelled by the application of machine learning methods across diverse fields [13][14][15]. The prediction of image popularity poses a formidable task, which has prompted numerous researchers to tackle this issue [16,17].…”

Section: Introductionmentioning

confidence: 99%

Unlocking Visual Attraction: The Subtle Relationship between Image Features and Attractiveness

Sun,

Zhang,

Zhu

et al. 2024

Mathematics

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The interest of advertising designers and operators in crafting appealing images is steadily increasing. With a primary focus on image attractiveness, this study endeavors to uncover the correlation between image features and attractiveness. The ultimate objective is to enhance the accuracy of predicting image attractiveness to achieve visually captivating effects. The experimental subjects encompass images sourced from the Shutterstock website, and the correlation between image features and attractiveness is analyzed through image attractiveness scores. In our experiments, we extracted traditional features such as color, shape, and texture from the images. Through a detailed analysis and comparison of the accuracy in predicting image attractiveness before and after feature selection using Lasso and LassoNet,, we confirmed that feature selection is an effective method for improving prediction accuracy. Subsequently, the Lasso and LassoNet feature selection methods were applied to a dataset containing image content features. The results verified an enhancement in the model’s accuracy for predicting image attractiveness with the inclusion of image content features. Finally, through an analysis of the four-dimensional features of color, texture, shape, and content, we identified specific features influencing image attractiveness, providing a robust reference for image design.

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