Visual Communication Design Style Recognition and Automatic Creation Technology Based on Deep Learning and Generative Adversarial Network (GAN) (2028)

Status: pending Scraped: 2026-08-12 Source: crossref Relevance: General

Authors: Lianghua Ma, Li Yan, Yuxing Ye et al.

Citation: Lianghua Ma, Li Yan, Yuxing Ye et al. (2028), Visual Communication Design Style Recognition and Automatic Creation Technology Based on Deep Learning and Generative Adversarial Network (GAN), crossref. Type: Determine after review

Abstract

Key Contribution

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Design

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Key Results

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Impact

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Linked To

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Data Quality Assessment

Field	Assessment
Subject selection	How were subjects selected? Inclusion/exclusion criteria, sampling strategy, n=? Power analysis?
Data acquisition	How was data physically acquired? Device model (E4, Empatica, WHOOP, Polar, dedicated sensor), sampling rate, placement, firmware version.
Acquisition context	From whom, what were they doing during recording? Clinic, home, school, sleep-only, free-living, task-based? Naturalistic or controlled?
Gold standard labels	How were outcome events defined? Clinician-validated scale (ABCD, ABC, CAAS, VAS), event logs, direct observation, chart review? Inter-rater reliability reported?
Data validation	How were data fields validated? Artifact rejection, signal quality indices, cross-modal checks, manual inspection, automated QC pipeline?
Missingness	n=X subjects — do all have complete data? Missing at random, missing completely at random, or missing not at random? Is missingness correlated with outcome?