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
- Archana G, Vijayalakshmi V, Improving classification precision for medical decision systems through big data analytics application , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- D. Padma Prabha, C. Victoria Priscilla, A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Rajesh Kumar Singh, Abhishek Kumar Mishra, Ramapati Mishra, Hand Gesture Identification for Improving Accuracy Using Convolutional Neural Network(CNN) , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Kanthalakshmi S, Nikitha M. S, Pradeepa G, Classification of weld defects using machine vision using convolutional neural network , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Josephine Theresa S, A Framework for Environment Thermal Comfort Prediction Model , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Susithra N, Rajalakshmi K, Ashwath P, Performance analysis of compressive sensing and reconstruction by LASSO and OMP for audio signal processing applications , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- R. Porselvi, D. Kanchana, Beulah Jackson, L. Vigneash, Dynamic resource management for 6G vehicular networks: CORA-6G offloading and allocation strategies , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Surbhi Choudhary, Vinay Chauhan, Exploring the metaverse: A new era for hospitality , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- Akshay J., G. Mahesh Kumar, B. H. Manjunath, Optimizing durability of the thin white topping applying Taguchi method using desirability function , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- M. Yamunadevi, P. Ponmuthuramalingam, A review and analysis of deep learning methods for stock market prediction with variety of indicators , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
<< < 18 19 20 21 22 23 24 25 26 27 > >>
You may also start an advanced similarity search for this article.

