ArticlePloS one2022
Design and interactive performance of human resource management system based on artificial intelligence.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Retraction: Design and interactive performance of human resource management system based on artificial intelligence.PloS one · 2025Article
- Article
- A method for managing scientific research project resource conflicts and predicting risks using BP neural networks.Scientific reports · 2024Article
- Article
- Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework.BioData mining · 2023Article
- Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology.Computational intelligence and neuroscience · 2022Article
- Construction of Hospital Human Resource Information Management System under the Background of Artificial Intelligence.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
- Retraction · 2025-04-23Compromised Peer Review · Concerns/Issues about Article · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Objections by Author(s) · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/05E4876C53E68A013297E858581683
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose is to strengthen Human Resources Management (HRM) through information management using Artificial Intelligence (AI) technology. First, the selection criteria of the applicant's resume during recruitment and the formulation standards of the contract salary are analyzed. Then, the resume information is extracted and converted into the data-type format. Besides, the salary forecast model in the HRM system (HRMS) is designed based on the Back Propagation Neural Network (BPNN), and network structure, parameter initialization, and activation function of the BPNN are selected and optimized. The experimental results demonstrate that the algorithm optimized by the Nadm has shown improved convergence speed and forecast effect, with 187 iterations. Moreover, compared with other regression algorithms, the designed algorithm achieves the best test scores. The above results can provide references for designing the AI-based HRMS.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.