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
- Simeon P, Vijayalakshmi D, Design and development of wall hanging and plant hangers using tie and dye , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Amalraj . P, Vinodkumar P. B., Existence of a homeomorphism from the space of continuous functions to the space of compact Subsets of a topological space, X , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- S. TAMIL FATHIMA, K. FATHIMA BIBI, Early diagnosis of cardiac disease using Xgboost ensemble voting-based feature selection, based lightweight recurrent neural network approach , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Amit Maru, Dhaval Vyas, Hybrid deep learning approach for pre-flood and post-flood classification of remote sensed data , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Reshmi J S, Sandhya S, Ahir embroidery of Kutch , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Y. Mohammed Iqbal, M. Mohamed Surputheen, S. Peerbasha, A COVID Net-predictor: A multi-head CNN and LSTM-based deep learning framework for COVID-19 diagnosis , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- V. Parimala, D. Ganeshkumar, Solar energy-driven water distillation with nanoparticle integration for enhanced efficiency, sustainability, and potable water production in arid regions , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Nithya R, Kokilavani T, Joseph Charles P, Multi-objective nature inspired hybrid optimization algorithm to improve prediction accuracy on imbalance medical datasets , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Pravin P. P, J. Arunshankar, Development of digital twin for PMDC motor control loop , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Ashwani Pandey, Sanjay Madan, Kumari Sandhiya, Ruchi Sharma, Akansha Raturi, Ashmita Bhatt, Naveen Gaurav, Comparison of Antioxidant, Phytochemical Profiling of Bacopa monnieri (Brahmi) , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
<< < 24 25 26 27 28 29 30 31 32 33 > >>
You may also start an advanced similarity search for this article.

