The heterogeneity of employee performance data collected in organizations—stemming from variations informat, structure, and recording methods—creates significant inaccuracies within KPI systems. This article proposesan AI-based normalization methodology aimed at standardizing KPI data, automatically filtering noisy and inconsistententries, and converting heterogeneous inputs into a unified mathematical representation. The study employs NLPtechniques, min–max scaling, z-score standardization, Isolation Forest, and sentence-embedding models. Experimentalresults demonstrate that the proposed normalization pipeline increases data accuracy from 78% to 94% and reduces theKPI calculation time from 40 hours to 0.8 hours
| Mualliflar | Shuhratov , Mamurjon |
|---|---|
| Jurnal | Innovation science and technologiy |
| Nashr sanasi | 2025-12-01 |
| Jild | 1 |
| Son | 12 |
| Til | Ingliz |
| DOI | 10.5281/zenodo.17847027 |
DOI: 10.5281/zenodo.17847027 · Maqolaning asl sahifasi
normalization, artificial intelligence, data cleaning, automation, NLP.
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