Modelling Effective Parameters For Enhanced Interaction Practices In Industry: An AI Perspective

  • Ashwini Kumar
  • Rekha Agarwal
  • Archana Singh
Keywords: The COFI framework, the Random Forest algorithm, K Means clustering, interactions, predictive models, Human AI Interactions, industrial mathematics optimization, segmentation analysis, AI typical system models.

Abstract

The focal point in digitalization and automation opportunities has been improving the industry's interaction practices for better productivity, customer engagement, and operational efficiency. This study, titled "Modelling Effective Parameters for Enhanced Interaction Practices in Industry: The paper titled "An AI Perspective", discusses how Interaction parameters by using machine learning methods RF and K-Means on Interaction coups and applying the COFI framework, namely Context, Content, Competency, and Culture. Applying the novel approach of data preprocessing, modelling and evaluation techniques for the utilization of the combination of supervised and unsupervised learning, this study established that the employed supervised models of learning offered an accuracy of 54% as found in the RF model, offering the highest efficient predictive accuracy and generalizable for industrial applications of general understanding. Nevertheless, K-Means clustering shows better results in the case of the COFI metric within the human-AI interaction analysis, regarding the Completeness (CC), the Correctness (CU), and the Accuracy (CA) measures, scoring more than 70%. These results show that even if RF is appropriate for decision-making for fine-tuned predictions, K-Means is better for segmenting targets based on the COFI framework to offer systematic data on interaction. So, this research adds to the limited body of literature on using AI for industrial interaction practices while arguing for integrating both RF and K-Means models to address the diverse needs of today's industries.

Author Biographies

Ashwini Kumar

Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, 201301, India, 

Rekha Agarwal

AIIT, Amity University, Noida, Uttar Pradesh, 201301, India

Archana Singh

Caliper, Foresight Health Solutions LLC, Noida, Uttar Pradesh, 201301, India, 

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Published
2024-11-20
How to Cite
Ashwini Kumar, Rekha Agarwal, & Archana Singh. (2024). Modelling Effective Parameters For Enhanced Interaction Practices In Industry: An AI Perspective. Revista Electronica De Veterinaria, 25(1), 2772-2783. https://doi.org/10.69980/redvet.v25i1.1383
Section
Articles