Published on: April 2026
RESUME INTELLIGENCE SYSTEM
B.Sanjay Deepika Gudla E.Hansika Y.Dinesh
K.Kiran Babu
Department of CSE (Data Science), ACE Engineering College, Hyderabad, Telangana, India
Article Status
Available Documents
Abstract
The Resume Intelligence System is an AI-driven application designed to automate the lifecycle of resume management, including creation, analysis, and evaluation. By leveraging Natural Language Processing (NLP) and Machine Learning (ML) techniques, the system bridges the gap between job seekers and recruitment standards.An AI-Powered Approach to Recruitment and Career Assistance.
The core functionality allows users to generate or upload resumes, from which relevant keywords and professional skills are extracted using text preprocessing and feature extraction methods such as TF-IDF. A key component of the platform is its inbuilt Applicant Tracking System (ATS) module, which calculates a similarity score to evaluate resume compatibility with specific company job descriptions.
How to Cite this Paper
B.Sanjay, , Gudla, D., E.Hansika, & Y.Dinesh, (2026). Resume Intelligence System. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.118
B.Sanjay, , et al.. "Resume Intelligence System." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.118.
B.Sanjay, ,Deepika Gudla, E.Hansika, and Y.Dinesh. "Resume Intelligence System." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.118.
Search & Index
References
1) Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.Conference: NAACL-HLT. DOI: 10.18653/v1/N19-142.link:-A Review of Natural-Language-Instructed Robot Execution Systems2) Ramos, J. (2003). Using TF-IDF to Determine Word Relevance in Document Classification.Conference: Proceedings of the First Instructional Conference on Machine Learning.Link :- https://www.researchgate.net/publication/228737527_Using_TF-IDF_to_Determine_Word_Relevance_in_Document_Classification
3) Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python.Journal: Journal of Machine Learning Research (JMLR).Link :- [1201.0490] Scikit-learn: Machine Learning in Python
4) Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. Publisher: O’Reilly Media.Link :-
5) Loper, E., & Bird, S. (2002). NLTK: The Natural Language Toolkit.Conference: ACL Workshop
6) Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.Conference: EMNLP
7) Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.Conference: EMNLP
8) McKinney, W. (2010). Data Structures for Statistical Computing in Python.Conference: Python in Science Conference
9) Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python.Publisher: O’Reilly Media
10) Google Developers. (2023). Flutter Documentation.Available: https://docs.flutter.dev
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 07 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.
← Previous Article
Resqher: An IOT-Enabled Smart Women Safety System with Real-Time MonitoringNext Article →
Retail & E-Commerce