Summary of Unlocking Futures: a Natural Language Driven Career Prediction System For Computer Science and Software Engineering Students, by Sakir Hossain Faruque et al.
Unlocking Futures: A Natural Language Driven Career Prediction System for Computer Science and Software Engineering Students
by Sakir Hossain Faruque, Sharun Akter Khushbu, Sharmin Akter
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel AI-assisted model is proposed for early prediction of career paths, providing better guidance to Computer Science (CS) and Software Engineering (SWE) students. The model leverages machine learning (ML) algorithms, natural language processing (NLP) techniques, and dataset pre-processing to predict suitable careers based on students’ skills, interests, and activities. Multiple classification ML algorithms and deep learning (DL) algorithms are applied for comparative analysis. This research contributes valuable insights to educational advising by offering specific career suggestions tailored to CS and SWE students. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps students find the best job that matches their skills, interests, and activities. The goal is to provide meaningful guidance during studies, making it easier to fulfill desires through hard work. |
Keywords
» Artificial intelligence » Classification » Deep learning » Machine learning » Natural language processing » Nlp
