International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Next-Hire: An AI-Powered Job Recruitment Platform |
|---|---|
| ABSTRACT | This paper presents NEXT-HIRE, an AI-powered end-to-end recruitment platform that automates the complete hiring pipeline. The system integrates Sentence-BERT (SBERT) for semantic resume screening, a Gradient Boosting Classifier (GBC) for aptitude evaluation, Code2Vec for coding skill assessment, a fine-tuned T5 Transformer for personalized interview question generation, MFCC + BiLSTM for voice proficiency analysis, and a Spatiotemporal Attention Network for facial behavior recognition. A VRM-rendered HR Avatar conducts the virtual interview. All module results are aggregated into a weighted performance index for objective, data-driven hiring decisions. Testing confirms that NEXT-HIRE significantly improves recruitment accuracy, reduces bias, and shortens hiring cycle time. |
| AUTHOR | DIVAN SHA REBAK, SRIKANTH S, AKILAN B, RAJAPANDI P, S.SARANYA Department of Artificial Intelligence and Data Science, Christ the King Engineering College, Anna University, Coimbatore, India Assistant Professor, Department of Artificial Intelligence and Data Science, Christ the King Engineering College, Anna University, Coimbatore, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405045 |
| pdf/45_Next-Hire An AI-Powered Job Recruitment Platform.pdf | |
| KEYWORDS | |
| References | [1] C. Qin et al., “Automatic skill-oriented question generation for intelligent job interviews,” ACM Trans. Inf. Syst., vol. 42, no. 1, pp. 1–32, 2024. [2] L.-K. Lee et al., “Utilizing virtual reality and generative AI chatbot for job interview simulations,” Proc. ISET, pp. 209–212, 2024. [3] P. K. Mishra et al., “AI-driven virtual mock interview development,” Proc. Joint 13th Int. Conf. Soft Comput. Intell. Syst., pp. 1–4, 2024. [4] A. Vinciarelli et al., “Social signal processing: Survey of an emerging domain,” Image Vis. Comput., vol. 27, no. 12, pp. 1743–1759, 2009. [5] Y. I. Nakano and R. Ishii, “Estimating user’s engagement from eye-gaze in human-agent conversations,” Proc. IUI, pp. 139–148, 2010. [6] U. Alon et al., “Code2Vec: Learning distributed representations of code,” Proc. ACM POPL, vol. 3, pp. 1–29, 2019. [7] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-Networks,” Proc. EMNLP, pp. 3982–3992, 2019. |
