E-HRM: Learning approaches, applications and the role of artificial intelligence
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.4.45Keywords:
E-HRM, E-Learning, Artificial Intelligence, Information TechnologyDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
E-HRM (Electronic Human Resources Management), which is derived from the concept of HRM (Human Resources Management) plays a significant role in automating certain key processes in the department of human resources. One of the modules of E-HRM is the training or the learning module, which when combined with a digital source turns out to be an E-Learning (Electronic Learning) or E-Training (Electronic Training) module. This is a transformation of converting the learning platform from an offline to an online mode. The organizations to increase their level of training from their employees should look for a fast-paced solution at a shorter turn-around time and the prime way to perform such a strategy is to automate the whole process of training and predict the training need and outcome. This research paper is focused on two aspects of e-learning i.e., how an e-learning system is collaborated with an intelligent system in the form of Artificial Intelligence and the other aspect is how an employee turn over data fetched from organizations in the IT (Information Technology) sector can help understand the real requirements of learning among employees in the IT organizations.Abstract
How to Cite
Downloads
Similar Articles
- C. Agilan, Lakshna Arun, Optimization-based clustering feature extraction approach for human emotion recognition , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- B. S. E. Zoraida, J. Jasmine Christina Magdalene, Smart grid precision: Evaluating machine learning models for forecasting of energy consumption from a smart grid , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Manpreet Kaur, Shweta Mishra, A smart grid data privacy-preserving aggregation approach with authentication , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Sindhu S, L. Arockiam, DRMF: Optimizing machine learning accuracy in IoT crop recommendation with domain rules and MissForest imputation , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Dadhaniya Deepa Karshanbhai, Nilofar Bhatti, Bioremediation of Textile Dyes Using Native Microorganisms: Sustainable Microbiological Approaches , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- P. Rajkumar, B. Vijay Bhaskar, Assessing the impact of indoor air pollution on respiratory health: A survey of home residents in rural area , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- M. Iniyan, A. Banumathi, Brower blowfish nash secured stochastic neural network based disease diagnosis for medical WBAN in cloud environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Hariharan V.S, Phaneendra S, Evaluating the combustion characteristics of methanol-gasoline blends in IC engines , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Deena Merit C K , Haridass M, Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Kowsalya Ramasamy, Thiyagarajan Krishnan, Performance analysis of RF substrate materials in ISM band antenna applications , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
<< < 17 18 19 20 21 22 23 24 25 26 > >>
You may also start an advanced similarity search for this article.