Development of an AI-based solutions to predict absenteeism

Employee absenteeism presents significant challenges for organizations, affecting productivity, team morale, and operational efficiency. Accurately predicting absenteeism enables businesses to proactively address its underlying causes, optimize workforce planning, and minimize disruptions. Advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) provide robust methods for analyzing complex datasets to uncover patterns associated with absentee behavior.
This project aims to develop an AI-based solution to efficiently predict absenteeism in organizational contexts. Leveraging a large dataset collected by an industrial partner with expertise in workforce and performance analytics, the project will focus on designing and evaluating ML/DL models. The primary goal is to create models capable of delivering high accuracy while generalizing effectively across datasets with similar attributes, ensuring broad applicability.

Faculty Supervisor:

Moulay Akhloufi;Fadoua Khennou

Student:

Partner:

FITSTATS Technologies Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université de Moncton

Program: