Neural Network-Driven Resume Skill Inflation Detection Using NLP and Source Code Repositories
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| Format: | Recurso digital |
| Langue: | anglais |
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2026
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| _version_ | 1866901307165507584 |
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| author | Krishna Swamy, Tejesh Kumar Krishna Swamy, Vanitha |
| author_facet | Krishna Swamy, Tejesh Kumar Krishna Swamy, Vanitha |
| contents | <p><span>Inflation in resumes has been a major issue in recruitment, and in most cases, a candidate can exaggerate his technical expertise without any proof. The conventional screening </span><span>process relies heavily on self-reported skills, which results in inef</span><span>fective recruitment and skill constraints. In this paper, I suggest </span><span>an AI-based model to identify skill inflation by matching resume </span><span>assertions with publicly available developer actions, such as </span><span>GitHub repositories and professional profile texts. The suggested </span><span>system is based on the principles of Natural Language Processing </span><span>(NLP) and its ability to identify purported skills in resumes </span><span>and compare them to such objective measures as repository </span><span>originality, commit frequency, and metrics of code quality. A </span><span>machine learning algorithm is utilized to categorize the resumes </span><span>as either genuine or inflated. The outcomes of experiments prove </span><span>the suggested method to be effective in detecting the differences </span><span>between alleged and proven skills, providing a scalable and </span><span>automated method for recruitment screening.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18447635 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Neural Network-Driven Resume Skill Inflation Detection Using NLP and Source Code Repositories Krishna Swamy, Tejesh Kumar Krishna Swamy, Vanitha Skill Inflation, Resume Analysis, Natural Language Processing, GitHub Mining, Machine Learning, AI Recruitment <p><span>Inflation in resumes has been a major issue in recruitment, and in most cases, a candidate can exaggerate his technical expertise without any proof. The conventional screening </span><span>process relies heavily on self-reported skills, which results in inef</span><span>fective recruitment and skill constraints. In this paper, I suggest </span><span>an AI-based model to identify skill inflation by matching resume </span><span>assertions with publicly available developer actions, such as </span><span>GitHub repositories and professional profile texts. The suggested </span><span>system is based on the principles of Natural Language Processing </span><span>(NLP) and its ability to identify purported skills in resumes </span><span>and compare them to such objective measures as repository </span><span>originality, commit frequency, and metrics of code quality. A </span><span>machine learning algorithm is utilized to categorize the resumes </span><span>as either genuine or inflated. The outcomes of experiments prove </span><span>the suggested method to be effective in detecting the differences </span><span>between alleged and proven skills, providing a scalable and </span><span>automated method for recruitment screening.</span></p> |
| title | Neural Network-Driven Resume Skill Inflation Detection Using NLP and Source Code Repositories |
| topic | Skill Inflation, Resume Analysis, Natural Language Processing, GitHub Mining, Machine Learning, AI Recruitment |
| url | https://doi.org/10.5281/zenodo.18447635 |