Deep Q-Network-Based Agent Learning for Employee Performance Prediction in Industrial Environments

Arun Prasad Jaganathan; Prakash Subramani; Nirmal Manmathan

doi:10.1109/AIIoT65859.2025.11105331

Deep Q-Network-Based Agent Learning for Employee Performance Prediction in Industrial Environments

Arun Prasad Jaganathan
, Prakash Subramani
, Nirmal Manmathan

Jugl Incorporation Limited
Jugl Incorporation Limited Anna University
Madras University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025
Editors	Rajashree Paul
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	149-155
Number of pages	7
ISBN (Electronic)	9798331525088
DOIs	https://doi.org/10.1109/AIIoT65859.2025.11105331
State	Published - 2025
Externally published	Yes
Event	6th IEEE Annual World AI IoT Congress, AIIoT 2025 - Seattle, United States Duration: May 28 2025 → May 30 2025

Publication series

Series	2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025

Conference

Conference	6th IEEE Annual World AI IoT Congress, AIIoT 2025
Country/Territory	United States
City	Seattle
Period	5/28/25 → 5/30/25

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Safety, Risk, Reliability and Quality
Education

Keywords

Employee performance
Industries
Prediction
Reinforcement Learning
Workforce Optimization

Access to Document

10.1109/AIIoT65859.2025.11105331

Cite this

Deep Q-Network-Based Agent Learning for Employee Performance Prediction in Industrial Environments. / Jaganathan, Arun Prasad; Subramani, Prakash; Manmathan, Nirmal.
2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025. ed. / Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2025. p. 149-155 (2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jaganathan, AP, Subramani, P & Manmathan, N 2025, Deep Q-Network-Based Agent Learning for Employee Performance Prediction in Industrial Environments. in R Paul (ed.), 2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025. Institute of Electrical and Electronics Engineers Inc., 2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025, pp. 149-155, 6th IEEE Annual World AI IoT Congress, AIIoT 2025, Seattle, United States, 5/28/25. https://doi.org/10.1109/AIIoT65859.2025.11105331

Jaganathan AP, Subramani P, Manmathan N. Deep Q-Network-Based Agent Learning for Employee Performance Prediction in Industrial Environments. In Paul R, editor, 2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 149-155. (2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025). doi: 10.1109/AIIoT65859.2025.11105331

Jaganathan, Arun Prasad ; Subramani, Prakash ; Manmathan, Nirmal. / Deep Q-Network-Based Agent Learning for Employee Performance Prediction in Industrial Environments. 2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025. editor / Rajashree Paul. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 149-155 (2025 IEEE 6th Annual World AI IoT Congress, AIIoT 2025).

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Deep Q-Network-Based Agent Learning for Employee Performance Prediction in Industrial Environments

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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