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
- S Rehan Ahmad, KDV Prasad, Seema Bhakuni, Amit Hedau, P B Shankar Narayan, P Parameswari, The role and relation of emotional intelligence with work-life balance for working women in job stress , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- M. Deepika, I. Antonitte Vinoline, The Impact of ERP Integration and Preservation Technology on Profit Optimization in Inventory Systems with Shortages and Deterioration , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- P. Ananthi, A. Chandrabose, Exploring learning-assisted optimization for mobile crowd sensing , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Shamba Gowda, AR Chethan Kumar, S. Srinivasaragavan, Scholarly communication behavior in forestry research: A bibliometric analysis of global publications , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- B Tharini, R. Rajasudha , A Kannammal, Performance analysis of microstrip patch antenna using binomial series expansion , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Temesgen A. Asfaw, Deep learning hyperparameter’s impact on potato disease detection , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Vikas Jangra, Dr. Vikas Jangra, Vandana, Comparative study of color difference on coated and uncoated paper in digital printing , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Abhishek Pandey, V Ramesh, Puneet Mittal, Suruthi, Muniyandy Elangovan, G.Deepa, Exploring advancements in deep learning for natural language processing tasks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Fauzi Aldina, Yusrizal ., Deny Setiawan, Alamsyah Taher, Teuku M. Jamil, Social science education based on local wisdom in forming the character of students , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- V. Karthikeyan, C. Jayanthi, Advancements in image quality assessment: a comparative study of image processing and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 4 5 6 7 8 9 10 11 12 13 > >>
You may also start an advanced similarity search for this article.

