Research on Humanized Design and Implementation of Computer Software User Interface Based on Visual Data Mining
Bibliographic record
Abstract
With the rapid development of the Internet and the continuous increase of users, computer software increasingly needs to consider humanized design when designing. Previous User Interface (UI) had issues such as insufficient consistency and slow response time in humanized design. This article applied visual Data Mining (DM) to the user-friendly design of computer software UI. The article compared the UI humanization design effects of neural network method and decision tree method. The experimental results showed that the average consistency of UI humanization design based on neural network method and decision tree method was 95.2% and 88.4%, respectively, for data after one month of UI upgrade. Based on the data from two months after the UI upgrade, the average consistency of UI humanization design based on neural network method and decision tree method was 96.3% and 92.0%, respectively. From this, it could be concluded that applying neural network method to humanized design of UI could effectively improve the consistency of humanized design of UI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".