An Enhanced Affective Computing Technology for Fostering an Emotionally Healthy Workplace

Aurobind, G and Ramachandiran, R (2024) An Enhanced Affective Computing Technology for Fostering an Emotionally Healthy Workplace. Journal of Data Science, 2024 (37). pp. 1-15. ISSN 2805-5160

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Abstract

The mental health of workers is of paramount importance in today’s fast-paced and demanding workplaces. This research establishes a strong connection between affective decision-making and mental health, presenting a novel method to improve affective computing technologies for developing emotionally healthy workplaces. We use Multinomial Naive Bayes Integrated Gated Recurrent Units (MNB-GRU) for classification and prediction to reach our goals. This dataset includes many measures of mental health, working climate, and individual variables. Data Exploration is performed to learn more about the properties of the dataset, and Preprocessing Grouping is used to get the data ready for analysis. Relationships between emotional decisionmaking and mental health markers are shown using data visualization approaches to give intuitive insights. To evaluate the reliability of the connection, a correlation analysis is used in Model Assessment. Understanding how people are feeling emotionally at work can be gained via this assessment, which examines the degree of association between affective decision-making and mental health. As a result of using categorization methods to divide the workforce into several categories based on their emotional well-being, we can better assist businesses in meeting the varying demands of their staff members. This method guarantees that efforts to improve mental health are focused and productive. By creating a solid connection between effective decisionmaking and mental health, businesses can take preventative measures to aid their workers' emotional well-being and create a more upbeat and productive workplace.

Item Type:	Article
Uncontrolled Keywords:	Affective Computing, Multinomial Naïve Bayes integrated gated recurrent units, Grouping, Correlation Coefficient, Model Evaluation, Mental health
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	08 Nov 2024 00:25
Last Modified:	08 Nov 2024 00:25
URI:	http://eprints.intimal.edu.my/id/eprint/2017

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