Application research and effectiveness evaluation mechanism of hybrid intelligent algorithm integrating cognitive computing and deep learning for dynamically adjusting employee performance evaluation in multi-scale organizational networks
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| Title: | Application research and effectiveness evaluation mechanism of hybrid intelligent algorithm integrating cognitive computing and deep learning for dynamically adjusting employee performance evaluation in multi-scale organizational networks |
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| Authors: | Zhenlin Luo, Kebin Lu |
| Source: | Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-29 (2025) |
| Publisher Information: | Springer, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Computational linguistics. Natural language processing LCC:Electronic computers. Computer science |
| Subject Terms: | Hybrid intelligent algorithms, Employee performance evaluation, Deep learning, KPI methods, Fairness analysis, Computational linguistics. Natural language processing, P98-98.5, Electronic computers. Computer science, QA75.5-76.95 |
| Description: | Abstract This study investigates the impact of a hybrid intelligent algorithm, integrating cognitive computing and deep learning, on the dynamic adjustment of employee performance evaluation in multi-scale organizational networks, through simulation experiments. Additionally, it proposes a performance evaluation mechanism based on algorithm optimization. The experiment initially compared the hybrid intelligent algorithm with the traditional KPI method. The results revealed that the mean square error (MSE) of the hybrid algorithm was significantly lower than that of the KPI (Key Performance Indicators) method across all datasets, with a 43.5% improvement in accuracy. It demonstrated superior accuracy in processing multi-dimensional employee data. Additional experiments involving noise interference indicate that the hybrid algorithm exhibits strong adaptability across varying data volumes. As the data size increases, the performance of the hybrid algorithm remains stable and continues to improve, outperforming traditional KPI and classical algorithms. Simultaneously, hybrid intelligent algorithms outperform support vector machines (SVM) in terms of response speed, with a 61.9% reduction in response time compared to SVM, highlighting their advantages in processing large-scale datasets. In terms of fairness, the hybrid intelligent algorithm outperforms the random forest algorithm (Gini coefficient of 0.22), with a lower Gini coefficient of 0.18, effectively reducing assessment bias and ensuring a fairer performance evaluation. Additionally, hybrid intelligent algorithms exhibit outstanding performance in improving employee satisfaction, with an 18.4% increase compared to traditional decision tree algorithms, suggesting that they provide more personalized feedback and enhance employees' identification with the performance appraisal system. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2731-0809 |
| Relation: | https://doaj.org/toc/2731-0809 |
| DOI: | 10.1007/s44163-025-00285-x |
| Access URL: | https://doaj.org/article/0db0918b57f4473987b7c763c5066adc |
| Accession Number: | edsdoj.0db0918b57f4473987b7c763c5066adc |
| Database: | Directory of Open Access Journals |
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Nájsť tento článok vo Web of Science | Abstract: | Abstract This study investigates the impact of a hybrid intelligent algorithm, integrating cognitive computing and deep learning, on the dynamic adjustment of employee performance evaluation in multi-scale organizational networks, through simulation experiments. Additionally, it proposes a performance evaluation mechanism based on algorithm optimization. The experiment initially compared the hybrid intelligent algorithm with the traditional KPI method. The results revealed that the mean square error (MSE) of the hybrid algorithm was significantly lower than that of the KPI (Key Performance Indicators) method across all datasets, with a 43.5% improvement in accuracy. It demonstrated superior accuracy in processing multi-dimensional employee data. Additional experiments involving noise interference indicate that the hybrid algorithm exhibits strong adaptability across varying data volumes. As the data size increases, the performance of the hybrid algorithm remains stable and continues to improve, outperforming traditional KPI and classical algorithms. Simultaneously, hybrid intelligent algorithms outperform support vector machines (SVM) in terms of response speed, with a 61.9% reduction in response time compared to SVM, highlighting their advantages in processing large-scale datasets. In terms of fairness, the hybrid intelligent algorithm outperforms the random forest algorithm (Gini coefficient of 0.22), with a lower Gini coefficient of 0.18, effectively reducing assessment bias and ensuring a fairer performance evaluation. Additionally, hybrid intelligent algorithms exhibit outstanding performance in improving employee satisfaction, with an 18.4% increase compared to traditional decision tree algorithms, suggesting that they provide more personalized feedback and enhance employees' identification with the performance appraisal system. |
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| ISSN: | 27310809 |
| DOI: | 10.1007/s44163-025-00285-x |