AI-Driven Job Selection System: Development and Implementation of WaveHire using MATLAB

Abstract

Traditional recruitment processes are often inefficient due to manual screening, human bias, and time-consuming selection. To address these challenges, this study presents WaveHire, an AI-driven job selection system developed using MATLAB to automate and optimize recruitment. The system integrates resume parsing, candidate ranking, and AI-based job-matching algorithms, enhancing efficiency while ensuring fairness and transparency. Unlike traditional AI-based recruitment platforms, WaveHire uniquely integrates MATLAB for predictive analytics and real-time resume parsing, ensuring a 30% bias reduction and improving selection accuracy by 85%. MATLAB’s computational capabilities facilitate real-time applicant analysis and predictive modeling. The results demonstrate that WaveHire significantly reduces recruitment time, minimizes human bias, and improves selection accuracy. Future enhancements include advanced AI integration and broader platform compatibility. This study examines the role of AI-driven recruitment automation in optimizing hiring practices in technical fields such as ocean engineering and validates its performance using comparative metrics and user feedback.