The Advanced Data Cleansing Method for Knowledge Discovery in Databases Using KDD With Generative AI and Cyber Security
Bibliographic record
Abstract
The objective of this research is to investigate sophisticated approaches for data mining and purification via knowledge discovery in databases (KDD) with generative AI concept, with an emphasis on using generative artificial intelligence. Unstructured data from customer service machine registry reports and structured data from sales, staff, and customer management duties are the two main types of information found in customer databases. To improve the extraction and interpretation of patterns, this study uses generative AI with cyber security hacks to secure the cleaning data in conjunction with the K-means algorithm to cluster and sort data. Our work focuses on the need to repositories of information and knowledge discovery, work for data storage and data mining. Dependent variable is KDD with generative AI and independent is Data Mining and KDD with cyber security as well. The main problem is to specific list, use useful pattern, Purposed model, inter-discipline action with cohort structure. We tried to secure and centralize and use branch level distribution in this.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.034 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".