Abstract
The present disclosure describes a method and system for comprehensive employee performance assessment using adaptive multi-channel data integration. The system collects data from at least three independent sources (psychometric tests, physical activity monitoring, operational workload metrics, communication activity, and behavioral logs). It employs Shannon entropy-based filtering to exclude noisy sources, Tukey's interquartile range method for outlier removal, and sliding-window normalization. Dynamic weights are calculated daily based on statistical dependence between each data source and actual employee productivity. An integrated performance index is computed with seasonal adjustment. The system is scalable, computationally efficient, and reduces processing load by up to 40%. This publication creates prior art to prevent patenting of similar approaches.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Gorobtsov, Vladimir, "Method and System for Adaptive Multi-Channel Employee Performance Assessment with Dynamic Data Source Weighting and Entropy-Based Filtering", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11347