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
- Santhanalakshmi M, Ms Lakshana K, Ms Shahitya G M, Enhanced AES-256 cipher round algorithm for IoT applications , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Gautam Nayak, Parthivkumar Patel, Developing speaking skills through task-based learning in English as a foreign language classroom , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Azar Bagheri Masoudzade, Maryam Ebrahim Nezhad, Appraising social class dimensions on learning motivation of Iranian students: Family studies and their status in focus , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Shamba Gowda, AR Chethan Kumar, S. Srinivasaragavan, Mapping of research productivity on forestry research in India: A scientometric study , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- M. Jayakandan, A. Chandrabose, An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Rattan Singh, Sushil Gupta, Anil Kumar, EFFECTS OF SOURCES, INFORMATION, COMMUNICATION AND KNOWLEDGE IN HIV/AIDS AWARENESS PROGRAMME IN PUNJAB. , The Scientific Temper: Vol. 1 No. 01 (2010): The Scientific Temper
- Jonah, Danush Kumar SM, Yugeshkrishnan M, Santhoshkumar K, Shahid Gaffa, Satellite hardfacing of mild steel using robotic mig welding , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- B Bindu, Srikanth N, Haris Raja V, Barath Kumar JK, Dharmendra R, Comparative analysis of inverted pendulum control , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Raja S, Nagarajan L., Hybridization of bio-inspired algorithms with machine learning models for predicting the risk of type 2 diabetes mellitus , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- A. Basheer Ahamed, M. Mohamed Surputheen, M. Rajakumar, Quantitative transfer learning- based students sports interest prediction using deep spectral multi-perceptron neural network , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 7 8 9 10 11 12 13 14 15 16 > >>
You may also start an advanced similarity search for this article.

