InJobs - Interconnected Network Job Search

Abhiram Krishna A; Adithya A J; Amina H; Daya Deepu; Professor Nitha L Rozario

InJobs - Interconnected Network Job Search

Author : Abhiram Krishna A, Adithya A J, Amina H, Daya Deepu, Professor Nitha L Rozario Journa Name: International Journal of Science, Engineering and Technology Volume: 14 issue: 2 Year: Volume-14-issue-2 Views : 204

Abstract:

The demand for part-time employment among stu- dents has increased significantly due to rising educational costs and the need for financial independence. However, existing job platforms are not designed to meet student-specific requirements such as flexibility, entry-level accessibility, and security from fraudulent job postings. This paper presents InJobs, an AI- driven web platform that integrates job recommendation, fraud detection, and skill development into a unified system. The platform utilizes BERT-based semantic matching for accurate job recommendations and a Random Forest classifier for fraud detection. Additionally, a learning and certification module is incorporated to bridge the gap between skill acquisition and employment. Experimental evaluation demonstrates improved recommendation relevance, high fraud detection accuracy, and efficient system performance. The proposed system aims to provide a secure, intelligent, and student-focused employment ecosystem.