Personality Detection of Applicants And Employees Using K-mode Algorithm And Ocean Model

TL;DR

This paper proposes a system combining K-Modes clustering, the OCEAN personality model, and CNNs to predict personality types and employee well-being, aiming to improve candidate screening and behavioral analysis.

Contribution

It introduces a novel integration of clustering, personality modeling, and deep learning for personality detection and employee behavior analysis.

Findings

AVIs can effectively assist in candidate screening.

The model predicts employee well-being factors.

Behavioral changes can be detected using the proposed system.

Abstract

The combination of conduct, emotion, motivation, and thinking is referred to as personality. To shortlist candidates more effectively, many organizations rely on personality predictions. The firm can hire or pick the best candidate for the desired job description by grouping applicants based on the necessary personality preferences. A model is created to identify applicants' personality types so that employers may find qualified candidates by examining a person's facial expression, speech intonation, and resume. Additionally, the paper emphasises detecting the changes in employee behaviour. Employee attitudes and behaviour towards each set of questions are being examined and analysed. Here, the K-Modes clustering method is used to predict employee well-being, including job pressure, the working environment, and relationships with peers, utilizing the OCEAN Model and the CNN algorithm in the AVI-AI administrative system. Findings imply that AVIs can be used for efficient candidate screening with an AI decision agent. The study of the specific field is beyond the current explorations and needed to be expanded with deeper models and new configurations that can patch extremely complex operations.